PromptSystematicReview/ThePromptReport

1title,url,reason
2a brief history of prompt leveraging language models, https://arxiv.org/abs/2310.04438, AI Generated
3hydrogenrich supernovae beyond the neutrinodriven corecollapse paradigm,,About Space not Prompting
4fewshot learning with localization in realistic settings,,not related to prompting
5crosslingual alignment of contextual word embeddings with applications to zeroshot dependency parsing,, no Prompting
6analogyforming transformers for fewshot 3d parsing,, no prompting
7generalpurpose incontext learning by metalearning transformers,, no prompting
8a survey of deep learning for lowshot object detection,, no prompting
9fewshot classincremental learning a survey,, no prompting
10balanced and explainable social media analysis for public health with large language models,,uses BERT
11querydependent prompt evaluation and opti mization with offline inverse rl,,more about deep RL than prompting
12deltaedit exploring textfree training for textdriven image manipulation,,too training focused
13deep language networks joint prompt training of stacked llms using variational inference,, too training focused
14unnatural language processing how do language models handle machinegenerated prompts,, too training focused
15give me the facts! a survey on factual knowledge probing in pretrained language models,, cloze focused
16taskdriven prompt evolution for foundation models,, training related
17diversityaware meta visual prompting,, training focused
18drpt disentangled and recurrent prompt tuning for compositional zeroshot learning,, tuning
19deltaspace a semanticaligned feature space for flexible textguided image editing,, training focused
20instructpix2nerf instructed 3d portrait editing from a single image,, not really about prompting
21what changes can largescale language models bring intensive study on hyperclova billionsscale korean generative pretrained transformers,, about a model not prompts
22mllmdataengine an iterative refinement approach for mllm,,soft prompting
23unleashing the power of pretrained language models for offline reinforcement learning,, out-of-scope
24expt synthetic pretraining for fewshot experimental design,, no prompting
25improving inputlabel mapping with demonstration replay for incontext learning,, out-of-domain
26apollo zeroshot multimodal reasoning with multiple experts, 2310.18369v1.pdf, Lower-Level Transformer Modification - Not Prompting
27fewshot learning with siamese networks and label tuning,, no prompting
28mgimn multigrained interactive matching network for fewshot text classification,, no prompting
29zero and fewshot learning for author profiling,, about models not prompting
30"prompt, generate, then cache cascade of foundation models makes strong fewshot learners", http://arxiv.org/pdf/2303.02151v1.pdf, training
31gradientregulated metaprompt learning for generalizable visionlanguage models, http://arxiv.org/pdf/2303.06571v2.pdf, soft prompting
32decomposed prototype learning for fewshot scene graph generation,http://arxiv.org/pdf/2303.10863v1.pdf, continuous prompts
33supervised masked knowledge distillation for fewshot transformers,, no prompting
34"multimodal c4 an open, billionscale corpus of images interleaved with text", http://arxiv.org/pdf/2303.15466v2.pdf, no prompting
35a survey on fewshot classincremental learning,http://arxiv.org/pdf/2304.06939v3.pdf, no prompting
36unified quantum state tomography and hamiltonian learning using transformer models a languagetranslationlike approach for quantum systems, http://arxiv.org/pdf/2304.08130v2.pdf, no prompting
37pointgpt autoregressively generative pretraining from point clouds, http://arxiv.org/pdf/2305.11487v2.pdf, continuous prompts
38a survey of diffusion models in natural language processing,http://arxiv.org/pdf/2305.14671v2.pdf, no prompting
39oneforall generalized lora for parameterefficient finetuning, http://arxiv.org/pdf/2306.07967v2.pdf, tuning
40protodiff learning to learn prototypical networks by taskguided diffusion, http://arxiv.org/pdf/2306.14770v2.pdf, no prompting
41effective transfer of pretrained large visual model for fabric defect segmentation via specifc knowledge injection, http://arxiv.org/pdf/2306.16186v1.pdf, no prompting
42metatraining with demonstration retrieval for efficient fewshot learning, http://arxiv.org/pdf/2307.00119v1.pdf, cloze prompting
43tableye seeing small tables through the lens of images, http://arxiv.org/pdf/2307.02491v1.pdf, no prompting
44identifying misinformation on youtube through transcript contextual analysis with transformer models, http://arxiv.org/pdf/2307.12155v1.pdf, no prompting
45linkcontext learning for multimodal llms, http://arxiv.org/pdf/2308.07891v1.pdf, no prompting
46less is more towards efficient fewshot 3d semantic segmentation via trainingfree networks, http://arxiv.org/pdf/2308.12961v1.pdf, no prompting
47transprompt v2 a transferable prompting framework for crosstask text classification, http://arxiv.org/pdf/2308.15010v1.pdf, soft prompting
48selfsampling meta sam enhancing fewshot medical image segmentation with metalearning, http://arxiv.org/pdf/2308.16466v3.pdf, training
49promptbased node feature extractor for fewshot learning on textattributed graphs, http://arxiv.org/pdf/2309.02848v1.pdf, cloze prompts
50crossimage context matters for bongard problems, http://arxiv.org/pdf/2309.03468v1.pdf, no prompting
51dept decomposed prompt tuning for parameterefficient finetuning, http://arxiv.org/pdf/2309.05173v2.pdf, tuning
52glad contentaware dynamic graphs for log anomaly detection, http://arxiv.org/pdf/2309.05953v1.pdf, cloze prompting
53sct a simple baseline for parameterefficient finetuning via salient channels, http://arxiv.org/pdf/2309.08513v2.pdf, tuning
54pactuningfinetuning pretrained language models with pacdriven perturbed gradient descent, http://arxiv.org/pdf/2310.17588v1.pdf, no prompting
55on taskpersonalized multimodal fewshot learning for visuallyrich document entity retrieval, http://arxiv.org/pdf/2311.00693v1.pdf, no prompting
56robust finetuning of visionlanguage models for domain generalization, http://arxiv.org/pdf/2311.02236v1.pdf, no prompting
57lesion2vec deep metric learning for fewshot multiple lesions recognition in wireless capsule endoscopy video, http://arxiv.org/pdf/2101.04240v2.pdf, no prompting
58unsupervised law article mining based on deep pretrained language representation models with application to the italian civil code, http://arxiv.org/pdf/2112.03033v1.pdf, no prompting
59"using deepspeed and megatron to train megatronturing nlg 530b, a largescale generative language model", http://arxiv.org/pdf/2201.11990v3.pdf, training
60data distributional properties drive emergent incontext learning in transformers, http://arxiv.org/pdf/2205.05055v6.pdf, no prompting
61hungry hungry hippos towards language modeling with state space models, http://arxiv.org/pdf/2212.14052v3.pdf, no prompting
62clip2scene towards labelefficient 3d scene understanding by clip, http://arxiv.org/pdf/2301.04926v2.pdf, cloze prompting
63learning to detect an animal sound from five examples, http://arxiv.org/pdf/2305.13210v1.pdf, no prompting
64the rise of ai language pathologists exploring twolevel prompt learning for fewshot weaklysupervised whole slide image classification, http://arxiv.org/pdf/2305.17891v1.pdf, training
65language models are fewshot learners, http://arxiv.org/pdf/2005.14165v4.pdf, training
66when promptbased incremental learning does not meet strong pretraining, http://arxiv.org/pdf/2308.10445v1.pdf, training
67"fewer errors, but more stereotypes the effect of model size on gender bias", http://arxiv.org/pdf/2206.09860v1.pdf, MLMs and cloze prompting
68promptattack promptbased attack for language models via gradient search, http://arxiv.org/pdf/2209.01882v1.pdf, cloze prompting
69can language models be specific how, http://arxiv.org/pdf/2210.05159v2.pdf, cloze prompting
70multilingual relation classification via efficient and effective prompting, http://arxiv.org/pdf/2210.13838v2.pdf, soft prompting
71spe symmetrical prompt enhancement for fact probing, http://arxiv.org/pdf/2211.07078v1.pdf, soft prompting
72evaluating the robustness of discrete prompts, http://arxiv.org/pdf/2302.05619v1.pdf, cloze prompting
73syntaxaware hybrid prompt model for fewshot multimodal sentiment analysis, http://arxiv.org/pdf/2306.01312v2.pdf, soft and cloze prompting
74unified multimodal pretraining and promptbased tuning for visionlanguage understanding and generation, http://arxiv.org/pdf/2112.05587v2.pdf, MLMs and cloze prompting
75learning to transfer prompts for text generation, http://arxiv.org/pdf/2205.01543v2.pdf, soft prompting
76towards realistic lowresource relation extraction a benchmark with empirical baseline study, http://arxiv.org/pdf/2210.10678v3.pdf, tuning and cloze prompting
77promptfusion decoupling stability and plasticity for continual learning, http://arxiv.org/pdf/2303.07223v1.pdf, tuning
78are promptbased models clueless, http://arxiv.org/pdf/2205.09295v2.pdf, cloze prompting
79avoiding inference heuristics in fewshot promptbased finetuning, http://arxiv.org/pdf/2109.04144v1.pdf, tuning
80p4e fewshot event detection as promptguided identification and localization, http://arxiv.org/pdf/2202.07615v3.pdf, cloze prompting
81partslip lowshot part segmentation for 3d point clouds via pretrained imagelanguage models, http://arxiv.org/pdf/2212.01558v2.pdf, tuning
82sparsefit fewshot prompting with sparse finetuning for jointly generating predictions and natural language explanations, http://arxiv.org/pdf/2305.13235v2.pdf, training and tuning
83large language model distillation doesn't need a teacher, http://arxiv.org/pdf/2305.14864v1.pdf, training
84multiqgti towards question generation from multimodal sources, http://arxiv.org/pdf/2307.04643v1.pdf, no prompting
85why is prompt tuning for visionlanguage models robust to noisy labels, http://arxiv.org/pdf/2307.11978v1.pdf, tuning
86lowparameter federated learning with large language models, http://arxiv.org/pdf/2307.13896v1.pdf, tuning and MLM
87olala ontology matching with large language models, http://arxiv.org/pdf/2311.03837v1.pdf, uses BERT no specified prefix prompting
88crosslingual supervision improves large language models pretraining, http://arxiv.org/pdf/2305.11778v1.pdf, training focused
89explaincpe a freetext explanation benchmark of chinese pharmacist examination,http://arxiv.org/pdf/2305.12945v2.pdf, training focused
90adapting language models to compress contexts, http://arxiv.org/pdf/2305.14788v2.pdf, soft prompting
91a mechanism for sampleefficient incontext learning for sparse retrieval tasks, http://arxiv.org/pdf/2305.17040v1.pdf, more about LM interpretability than prompting
92large language models are partially primed in pronoun interpretation, http://arxiv.org/pdf/2305.16917v1.pdf, uses in-context learning but is not about prompting methods
93contextual vision transformers for robust representation learning,http://arxiv.org/pdf/2305.19402v2.pdf, not about prefix prompting
94selfverification improves fewshot clinical information extraction, http://arxiv.org/pdf/2306.00024v1.pdf, is about verifying output not modifying input
95measuring and modifying factual knowledge in large language models,http://arxiv.org/pdf/2306.06264v1.pdf, mentions in context learning but it is not the focus
96a survey on multimodal large language models,http://arxiv.org/pdf/2306.13549v1.pdf, not focused on prompting
97potential benefits of employing large language models in research in moral education and development,http://arxiv.org/pdf/2306.13805v2.pdf, not particuyarly about prompting
98assessing the efficacy of large language models in generating accurate teacher responses,http://arxiv.org/pdf/2307.04274v1.pdf, does not focus on prompting methods
99unsupervised calibration through prior adaptation for text classification using large language models,http://arxiv.org/pdf/2307.06713v3.pdf, does not focus on prompting methods
100baby's cothought leveraging large language models for enhanced reasoning in compact models,http://arxiv.org/pdf/2308.01684v2.pdf, focuses on training other models
101diffusion language models can perform many tasks with scaling and instructionfinetuning,http://arxiv.org/pdf/2308.12219v2.pdf, focuses on training
102large language model as autonomous decision maker,http://arxiv.org/pdf/2308.12519v1.pdf, not about prompting methods
103speechtospeech translation with discreteunitbased style transfer,http://arxiv.org/pdf/2309.07566v1.pdf, speech to speech translation
104language modeling is compression,http://arxiv.org/pdf/2309.10668v1.pdf, more about explaining in-context learning than proposing a method
105text data augmentation in lowresource settings via finetuning of large language models,http://arxiv.org/pdf/2310.01119v1.pdf, focuses on training
106humans and language models diverge when predicting repeating text,http://arxiv.org/pdf/2310.06408v2.pdf, focuses on evaluating humans and comparing to prompting method
107amago scalable incontext reinforcement learning for adaptive agents,http://arxiv.org/pdf/2310.09971v2.pdf, not about LMs; this is an RL paper
108meta (outofcontext) learning in neural networks,http://arxiv.org/pdf/2310.15047v2.pdf, evaluates in-context learning but is not based on it
109towards trainingfree openworld segmentation via image prompting foundation models,http://arxiv.org/pdf/2310.10912v1.pdf,image segmentation
110videoprompter an ensemble of foundational models for zeroshot video understanding,http://arxiv.org/pdf/2310.15324v1.pdf,"video understanding, different domain"
111improving diversity of demographic representation in large language models via collectivecritiques and selfvoting,http://arxiv.org/pdf/2310.16523v1.pdf,"model representation, not prompting"
112the power of large language models for wireless communication system development a case study on fpga platforms,http://arxiv.org/pdf/2307.07319v4.pdf,not prompting
113large language models enable fewshot clustering,http://arxiv.org/pdf/2307.00524v1.pdf,"few-shot clustering, not prompting"
114universal fuzzing via large language models,http://arxiv.org/pdf/2308.04748v1.pdf,does not use hard-prefix prompts
115trainingfree openworld segmentation via image prompting foundation models,,image segmentation
116fire food image to recipe generation,http://arxiv.org/pdf/2308.14391v1.pdf,image to text translation
117large language models can accurately predict searcher preferences,http://arxiv.org/pdf/2309.10621v1.pdf,does not use hard-prefix prompts
118understanding incontext learning from repetitions,http://arxiv.org/pdf/2310.00297v2.pdf,"focus is on effects of repetition in in-context learning, not prompting"
119small language models finetuned to coordinate larger language models improve complex reasoning,http://arxiv.org/pdf/2310.18338v1.pdf,"focus on fine-tuning, not hard-prefix prompting"
120revisiting large language models as zeroshot relation extractors,http://arxiv.org/pdf/2310.05028v3.pdf,zero-shot learning for relation extraction
121characterizing attribution and fluency tradeoffs for retrievalaugmented large language models,http://arxiv.org/pdf/2302.05578v2.pdf,RAG
122llmeval unified multidimensional automatic evaluation for opendomain conversations with large language models,http://arxiv.org/pdf/2305.13711v1.pdf,eval of LLMs
123robot task planning based on large language model representing knowledge with directed graph structures,http://arxiv.org/pdf/2306.05171v1.pdf,knowledge representation
124optimus optimization modeling using mip solvers and large language models,http://arxiv.org/pdf/2310.06116v2.pdf,"different approach, MIP solvers"
125promptinfuser how tightly coupling ai and ui design impacts designers' workflows,http://arxiv.org/pdf/2310.15435v1.pdf,focus on UI
126a monte carlo language model pipeline for zeroshot sociopolitical event extraction,http://arxiv.org/pdf/2305.15051v1.pdf,"monte carlo methods, not prompting"
127finetune language models to approximate unbiased incontext learning,http://arxiv.org/pdf/2310.03331v1.pdf,fine-tuning
128on the compositional generalization gap of incontext learning,http://arxiv.org/pdf/2211.08473v1.pdf,"compositional generalization, not hard-prefix prompting"
129fewshot finetuning vs incontext learning a fair comparison and evaluation,http://arxiv.org/pdf/2305.16938v2.pdf,no hard-prefix prompting
130stylemc multichannel based fast textguided image generation and manipulation, http://arxiv.org/pdf/2112.08493v1.pdf, not prompt engineering
131testtime training on nearest neighbors for large language models, http://arxiv.org/pdf/2305.18466v2.pdf, fine-tuning
132chain of natural language inference for reducing large language model ungrounded hallucinations, http://arxiv.org/pdf/2310.03951v2.pdf, no prompt engineering
133differentiable prompt makes pretrained language models better fewshot learners, http://arxiv.org/pdf/2108.13161v7.pdf, not hard prompts
134mme a comprehensive evaluation benchmark for multimodal large language models, http://arxiv.org/pdf/2306.13394v2.pdf, not specifically hard prompting
135protoclip visionlanguage prototypical network for fewshot learning, http://arxiv.org/pdf/2307.03073v2.pdf, not prompting
136a survey on recent named entity recognition and relation classification methods with focus on fewshot learning approaches, http://arxiv.org/pdf/2310.19055v1.pdf, not prompting
137improving incontext fewshot learning via selfsupervised training, http://arxiv.org/pdf/2205.01703v2.pdf, pretraining
138revisiting fewshot learning from a causal perspective, http://arxiv.org/pdf/2209.13816v1.pdf, not prompting
139film how can fewshot image classification benefit from pretrained language models, http://arxiv.org/pdf/2307.04114v1.pdf, not hard prefix prompting
140clues fewshot learning evaluation in natural language understanding, http://arxiv.org/pdf/2111.02570v1.pdf, no prompt engineering
141improving fewshot generalization by exploring and exploiting auxiliary data, http://arxiv.org/pdf/2302.00674v4.pdf, not prompt engineering.
142prompt space optimizing fewshot reasoning success with large language models, http://arxiv.org/pdf/2306.03799v1.pdf, not prompt engineering
143universal fewshot learning of dense prediction tasks with visual token matching, http://arxiv.org/pdf/2303.14969v1.pdf, not prompting
144fdalign feature discrimination alignment for finetuning pretrained models in fewshot learning, http://arxiv.org/pdf/2310.15105v3.pdf, fine tuning
145modelagnostic graph regularization for fewshot learning, http://arxiv.org/pdf/2102.07077v1.pdf, not prompting
146uniform sampling over episode difficulty, http://arxiv.org/pdf/2108.01662v2.pdf, not prompting
147metalearning with taskadaptive loss function for fewshot learning, http://arxiv.org/pdf/2110.03909v2.pdf, focuses on meta-learning
148on measuring the intrinsic fewshot hardness of datasets, http://arxiv.org/pdf/2211.09113v1.pdf, not prompting
149mera merging pretrained adapters for fewshot learning, http://arxiv.org/pdf/2308.15982v1.pdf, not prompting
150metaadapter an online fewshot learner for visionlanguage model, http://arxiv.org/pdf/2311.03774v1.pdf, not prompting
151pushing the limits of simple pipelines for fewshot learning external data and finetuning make a difference, http://arxiv.org/pdf/2204.07305v1.pdf, focus on few-shot learning.
152multilevel finetuning data augmentation and fewshot learning for specialized cyber threat intelligence, http://arxiv.org/pdf/2207.11076v1.pdf, training
153fewshot classification with hypersphere modeling of prototypes, http://arxiv.org/pdf/2211.05319v1.pdf, not prompting
154styleadv meta style adversarial training for crossdomain fewshot learning, http://arxiv.org/pdf/2302.09309v2.pdf, not prompting
155federated fewshot learning for cough classification with edge devices, http://arxiv.org/pdf/2309.01076v1.pdf, not prompting
156is support set diversity necessary for metalearning, http://arxiv.org/pdf/2011.14048v2.pdf, not prompting
157entailment as fewshot learner, http://arxiv.org/pdf/2104.14690v1.pdf, not prompt engineering
158wavprompt towards fewshot spoken language understanding with frozen language models, http://arxiv.org/pdf/2203.15863v2.pdf, fine-tuning
159aligning magma by fewshot learning and finetuning, http://arxiv.org/pdf/2210.14161v1.pdf, finetuning not prompting.
160stunt fewshot tabular learning with selfgenerated tasks from unlabeled tables, http://arxiv.org/pdf/2303.00918v1.pdf, not prompting
161prototypesoriented transductive fewshot learning with conditional transport, http://arxiv.org/pdf/2308.03047v1.pdf, not prompting
162coca classifieroriented calibration for sourcefree universal domain adaptation via textual prototype, http://arxiv.org/pdf/2308.10450v1.pdf, no prompt engineering
163improving generalization in large language models by learning prefix subspaces, http://arxiv.org/pdf/2310.15793v1.pdf, not prompting
164zeroshot and fewshot learning with knowledge graphs a comprehensive survey, http://arxiv.org/pdf/2112.10006v6.pdf, not prompting
165on unifying misinformation detection, http://arxiv.org/pdf/2104.05243v1.pdf, training
166human in the loop how to effectively create coherent topics by manually labeling only a few documents per class, http://arxiv.org/pdf/2212.09422v1.pdf, not prompting.
167neuroclip neuromorphic data understanding by clip and snn, http://arxiv.org/pdf/2306.12073v1.pdf, not prompting
168ppt pretrained prompt tuning for fewshot learning, http://arxiv.org/pdf/2109.04332v3.pdf, soft prompts
169yuan 10 largescale pretrained language model in zeroshot and fewshot learning, http://arxiv.org/pdf/2110.04725v2.pdf, training
170perfect promptfree and efficient fewshot learning with language models, http://arxiv.org/pdf/2204.01172v2.pdf, literally not prompting
171on the effect of pretraining corpora on incontext learning by a largescale language model, http://arxiv.org/pdf/2204.13509v2.pdf, pretraining
172fewshot learning for clinical natural language processing using siamese neural networks, http://arxiv.org/pdf/2208.14923v2.pdf, not prompting
173prompting through prototype a prototypebased prompt learning on pretrained visionlanguage models, http://arxiv.org/pdf/2210.10841v1.pdf, soft prompts
174sgvaclip semanticguided visual adapting of visionlanguage models for fewshot image classification, http://arxiv.org/pdf/2211.16191v2.pdf, training
175auggpt leveraging chatgpt for text data augmentation, http://arxiv.org/pdf/2302.13007v3.pdf, not prompting
176semantic prompt for fewshot image recognition, http://arxiv.org/pdf/2303.14123v1.pdf, not really prompt engineering
177the cot collection improving zeroshot and fewshot learning of language models via chainofthought finetuning, http://arxiv.org/pdf/2305.14045v2.pdf, training
178fewshot learning for inference in medical imaging with subspace feature representations, http://arxiv.org/pdf/2306.11152v1.pdf, no prompting
179visually grounded fewshot word learning in lowresource settings, http://arxiv.org/pdf/2306.11371v2.pdf, not prompting
180crossmodal concept learning and inference for visionlanguage models, http://arxiv.org/pdf/2307.15460v1.pdf, not prompt engineering.
181uniap towards universal animal perception in vision via fewshot learning, http://arxiv.org/pdf/2308.09953v1.pdf, not text prompts
182palm scaling language modeling with pathways, http://arxiv.org/pdf/2204.02311v5.pdf, not prompting
183fewshot electronic health record coding through graph contrastive learning, http://arxiv.org/pdf/2106.15467v1.pdf, not prompting
184ernie 30 largescale knowledge enhanced pretraining for language understanding and generation, http://arxiv.org/pdf/2107.02137v1.pdf, pre-training
185alleviating the incompatibility between cross entropy loss and episode training for fewshot skin disease classification, http://arxiv.org/pdf/2004.09694v1.pdf, not prompting
186fewshot learning through contextual data augmentation, http://arxiv.org/pdf/2103.16911v1.pdf, not prompting
187metalearning gnn initializations for lowresource molecular property prediction, http://arxiv.org/pdf/2003.05996v2.pdf, not prompt engineering.
188neural data augmentation via example extrapolation, http://arxiv.org/pdf/2102.01335v1.pdf, data augmentation
189oneshot learning for the long term consolidation with an artificial hippocampal algorithm, http://arxiv.org/pdf/2102.07503v2.pdf, not prompting
190the power of scale for parameterefficient prompt tuning, http://arxiv.org/pdf/2104.08691v2.pdf, soft prompts
191design of a graphical user interface for fewshot machine learning classification of electron microscopy data, http://arxiv.org/pdf/2107.10387v1.pdf, not prompting
192flipda effective and robust data augmentation for fewshot learning, http://arxiv.org/pdf/2108.06332v2.pdf, not prompting
193on the multilingual capabilities of very largescale english language models, http://arxiv.org/pdf/2108.13349v1.pdf, not prompting
194learning opinion summarizers by selecting informative reviews, http://arxiv.org/pdf/2109.04325v1.pdf, not prompting
195strata selftraining with task augmentation for better fewshot learning, http://arxiv.org/pdf/2109.06270v2.pdf, not prompting
196what does clip know about a red circle visual prompt engineering for vlms, http://arxiv.org/pdf/2304.06712v2.pdf, not text prompting
197conformal prediction with large language models for multichoice question answering, http://arxiv.org/pdf/2305.18404v3.pdf, not prompting.
198p2p tuning pretrained image models for point cloud analysis with pointtopixel prompting, http://arxiv.org/pdf/2208.02812v2.pdf, not text prompting
199evoprompting language models for codelevel neural architecture search, http://arxiv.org/pdf/2302.14838v2.pdf, soft prompts
200right to be forgotten in the era of large language models implications challenges and solutions, http://arxiv.org/pdf/2307.03941v3.pdf, not related
201label supervised llama finetuning, http://arxiv.org/pdf/2310.01208v1.pdf, focus on finetuning not prompting
202incontext learning distillation transferring fewshot learning ability of pretrained language models, http://arxiv.org/pdf/2212.10670v1.pdf, distillation not prompting.
203a neural network solves explains and generates university math problems by program synthesis and fewshot learning at human level, http://arxiv.org/pdf/2112.15594v4.pdf, focuses on fine-tuning
204crossfit a fewshot learning challenge for crosstask generalization in nlp, http://arxiv.org/pdf/2104.08835v2.pdf, not prompting
205jasmine arabic gpt models for fewshot learning, http://arxiv.org/pdf/2212.10755v2.pdf, training
206conversation style transfer using fewshot learning, http://arxiv.org/pdf/2302.08362v2.pdf, not prompting
207cancergpt fewshot drug pair synergy prediction using large pretrained language models, http://arxiv.org/pdf/2304.10946v1.pdf, training
208meta learning to bridge vision and language models for multimodal fewshot learning, http://arxiv.org/pdf/2302.14794v1.pdf, not prompting
209demonstrationbased learning for fewshot biomedical named entity recognition under machine reading comprehension, http://arxiv.org/pdf/2308.06454v1.pdf, not prompt engineering
210robustness over time understanding adversarial examples' effectiveness on longitudinal versions of large language models, http://arxiv.org/pdf/2308.07847v1.pdf, not prompting.
211fewshot natural language generation for taskoriented dialog, http://arxiv.org/pdf/2002.12328v1.pdf, not prompting
212promptfree diffusion taking text out of texttoimage diffusion models, http://arxiv.org/pdf/2305.16223v2.pdf, literally not prompting.
213cutting down on prompts and parameters simple fewshot learning with language models, http://arxiv.org/pdf/2106.13353v2.pdf, not prompt engineering
214executive function a contrastive value policy for resampling and relabeling perceptions via hindsight summarization, http://arxiv.org/pdf/2204.12639v1.pdf, not prompting
215tart a plugandplay transformer module for taskagnostic reasoning, http://arxiv.org/pdf/2306.07536v1.pdf, not prompting
216synergistic integration of large language models and cognitive architectures for robust ai an exploratory analysis, http://arxiv.org/pdf/2308.09830v3.pdf, brief mention of prompting but not related
217visionlanguage models are zeroshot reward models for reinforcement learning, http://arxiv.org/pdf/2310.12921v1.pdf, maybe tangential but not prompt engineering
218fewshot multimodal multitask multilingual learning, http://arxiv.org/pdf/2303.12489v1.pdf, maybe tangential but not prompt engineering
219fewshot learning with visual distribution calibration and crossmodal distribution alignment, http://arxiv.org/pdf/2305.11439v1.pdf, not prompting.
220active learning principles for incontext learning with large language models, http://arxiv.org/pdf/2305.14264v1.pdf, not prompting
221flame fewshot learning from natural language explanations, http://arxiv.org/pdf/2306.08042v1.pdf, not prompting.
222approximating humanlike fewshot learning with gptbased compression, http://arxiv.org/pdf/2308.06942v1.pdf, not promting
223from human days to machine seconds automatically answering and generating machine learning final exams, http://arxiv.org/pdf/2206.05442v7.pdf, not prompting
224cedille a large autoregressive french language model, http://arxiv.org/pdf/2202.03371v1.pdf, not prompting
225finetune like you pretrain improved finetuning of zeroshot vision models, http://arxiv.org/pdf/2212.00638v1.pdf, focuses on fine-tuning
226wordcraft a humanai collaborative editor for story writing, http://arxiv.org/pdf/2107.07430v1.pdf, not prompt engineering
227want to reduce labeling cost gpt3 can help, http://arxiv.org/pdf/2108.13487v1.pdf, not prompting
228cut the carp fishing for zeroshot story evaluation, http://arxiv.org/pdf/2110.03111v3.pdf, tangential but not prompt engineering
229fake it till you make it learning transferable representations from synthetic imagenet clones, http://arxiv.org/pdf/2212.08420v2.pdf, not prompt engineering
230activation addition steering language models without optimization, http://arxiv.org/pdf/2308.10248v2.pdf, messes with activation not prompt engineering
231safurai 001 new qualitative approach for code llm evaluation, http://arxiv.org/pdf/2309.11385v1.pdf, tangential but not prompt engineering
232controlled and conditional text to image generation with diffusion prior, http://arxiv.org/pdf/2302.11710v2.pdf, image prompts
233ipadapter text compatible image prompt adapter for texttoimage diffusion models, http://arxiv.org/pdf/2308.06721v1.pdf, image prompts
234revisiting selftraining for fewshot learning of language model, http://arxiv.org/pdf/2110.01256v1.pdf, tangential but not prompt engineering
235multimodal large language model for visual navigation, http://arxiv.org/pdf/2310.08669v2.pdf, tangential but not prompt engineering
236taskdiff a similarity metric for taskoriented conversations, http://arxiv.org/pdf/2310.15298v2.pdf, tangential but not prompt engineering
237clipadapter better visionlanguage models with feature adapters, http://arxiv.org/pdf/2110.04544v1.pdf, tangential but not prompt engineering
238cones concept embedding search for parameter efficient tuning large vision language models, http://arxiv.org/pdf/2305.18993v1.pdf, tangential but not prompt engineering
239logoprompt synthetic text images can be good visual prompts for visionlanguage models, http://arxiv.org/pdf/2309.01155v2.pdf, visual prompts
240manipulating embeddings of stable diffusion prompts, http://arxiv.org/pdf/2308.12059v1.pdf, manipulates embeddings not text.
241multimodal prompt transformer with hybrid contrastive learning for emotion recognition in conversation,httparxivorgpdf231004456v1pdf, multimodel RL
242promptenhanced selfsupervised representation learning for remote sensing image understanding,httparxivorgpdf231000022v1pdf, about fine-tuning
243discrete prompt compression with reinforcement learning,httparxivorgpdf230808758v1pdf, They compressed prompts using fine-tuning
244automatic short math answer grading via incontext metalearning,httparxivorgpdf220515219v3pdf, About Fine-tuning
245graphprompt biomedical entity normalization using graphbased prompt templates,httparxivorgpdf211203002v1pdf, About fine-tuning
246transformers generalize differently from information stored in context vs in weights,httparxivorgpdf221005675v2pdf, tangentially related
247large language models meet harry potter a bilingual dataset for aligning dialogue agents with characters,httparxivorgpdf221106869v4pdf, tangentially related
248operationalizing specifications in addition to test sets for evaluating constrained generative models,httparxivorgpdf221200006v1pdf, tangentially related as stated in their introduction
249language model acceptability judgements are not always robust to context,httparxivorgpdf221208979v1pdf, I believe it is tangentially related
250training trajectories of language models across scales,httparxivorgpdf221209803v3pdf, More focused on training rather than anything
251sparks of gpts in edge intelligence for metaverse caching and inference for mobile aigc services,httparxivorgpdf230408782v2pdf, Too tangentially related
252tallrec an effective and efficient tuning framework to align large language model with recommendation,httparxivorgpdf230500447v3pdf, More about fine-tuning
253memoryefficient finetuning of compressed large language models via sub4bit integer quantization,httparxivorgpdf230514152v2pdf, About Fine-Tuning I believe
254do large language models know what they don't know,httparxivorgpdf230518153v2pdf, No Mention of Prompting
255revisiting outofdistribution robustness in nlp benchmark analysis and llms evaluations,httparxivorgpdf230604618v2pdf, Not the main focus- barely mention
256transformers as statisticians provable incontext learning with incontext algorithm selection,httparxivorgpdf230604637v2pdf, Hardly mentioned- not main focus
257trained transformers learn linear models incontext,httparxivorgpdf230609927v3pdf, As I understand- this is about training and not prompting
258generative multimodal entity linking,httparxivorgpdf230612725v2pdf, Only soft prompting
259supervised pretraining can learn incontext reinforcement learning,httparxivorgpdf230614892v1pdf, Different Contexts I believe
260hyenadna longrange genomic sequence modeling at single nucleotide resolution,httparxivorgpdf230615794v1pdf, Only Soft Prompting
261explainable depression symptom detection in social media,httparxivorgpdf231013664v2pdf, Only one mention about prompting
262ensembleinstruct generating instructiontuning data with a heterogeneous mixture of lms,httparxivorgpdf231013961v1pdf, About fine-tuning
263anomalygpt detecting industrial anomalies using large visionlanguage models,httparxivorgpdf230815366v3pdf, More about training the model
264uncovering hidden geometry in transformers via disentangling position and context,httparxivorgpdf231004861v1pdf, Completely non-relevant
265mitigating word bias in zeroshot promptbased classifiers,httparxivorgpdf230904992v1pdf, about reweighing probabilities for prompt-based classifiers
266ideal influencedriven selective annotations empower incontext learners in large language models,httparxivorgpdf231010873v1pdf, About fine-tuning
267incontext pretraining language modeling beyond document boundaries,httparxivorgpdf231010638v3pdf, Not about prompting
268alt towards finegrained alignment between language and ctr models for clickthrough rate prediction,httparxivorgpdf231019453v1pdf, Not really about prompting
269understanding catastrophic forgetting in language models via implicit inference,httparxivorgpdf230910105v1pdf, About fine-tuning
270do pretrained transformers really learn incontext by gradient descent,httparxivorgpdf231008540v1pdf, About fine-tuning
271ccprompt counterfactual contrastive prompttuning for manyclass classification,httparxivorgpdf221105987v1pdf, About fine-tuning
272one step of gradient descent is provably the optimal incontext learner with one layer of linear selfattention,httparxivorgpdf230703576v1pdf, Different type of prompt?
273cyclealign iterative distillation from blackbox llm to whitebox models for better human alignment,httparxivorgpdf231016271v1pdf, About fine-tuning
274transformers are efficient incontext estimators for wireless communication,httparxivorgpdf231100226v1pdf, About fine-tuning
275scaling incontext demonstrations with structured attention,http://arxiv.org/pdf/2307.02690v1.pdf,new architecture
276incontext learning and induction heads,http://arxiv.org/pdf/2209.11895v1.pdf,new architecture
277what makes good examples for visual incontext learning,http://arxiv.org/pdf/2301.13670v2.pdf,visual only
278mmicl empowering visionlanguage model with multimodal incontext learning,http://arxiv.org/pdf/2309.07915v2.pdf,visual only
279visual incontext learning for fewshot eczema segmentation,http://arxiv.org/pdf/2309.16656v1.pdf,visual only
280scone benchmarking negation reasoning in language models with finetuning and incontext learning,http://arxiv.org/pdf/2305.19426v1.pdf,fine-tuning
281can whisper perform speechbased incontext learning,http://arxiv.org/pdf/2309.07081v1.pdf,speech
282salm speechaugmented language model with incontext learning for speech recognition and translation,http://arxiv.org/pdf/2310.09424v1.pdf,speech
283can foundation models help us achieve perfect secrecy,http://arxiv.org/pdf/2205.13722v2.pdf,overview paper
284se factual knowledge in frozen giant code model a study on fqn and its retrieval,http://arxiv.org/pdf/2212.08221v1.pdf,unclear task
285incontext learning for attention scheme from single softmax regression to multiple softmax regression via a tensor trick,http://arxiv.org/pdf/2307.02419v1.pdf,new architecture
286synergpt incontext learning for personalized drug synergy prediction and drug design,http://arxiv.org/pdf/2307.11694v2.pdf,new architecture
287twostage llm finetuning with less specialization and more generalization,http://arxiv.org/pdf/2211.00635v2.pdf,fine-tuning
288conceptaware training improves incontext learning ability of language models,http://arxiv.org/pdf/2305.13775v1.pdf,fine-tuning
289probing in context toward building robust classifiers via probing large language models,http://arxiv.org/pdf/2305.14171v2.pdf,uses probes for task
290towards incontext scene understanding,http://arxiv.org/pdf/2306.01667v2.pdf,visual only
291the cost of downscaling language models fact recall deteriorates before incontext learning,http://arxiv.org/pdf/2310.04680v1.pdf,analysis of pruning / LM size
292"last one standing a comparative analysis of security and privacy of soft prompt tuning, lora, and incontext learning",http://arxiv.org/pdf/2310.11397v1.pdf,analysis of lora / tuning / ICL
293when do prompting and prefixtuning work a theory of capabilities and limitations,http://arxiv.org/pdf/2310.19698v1.pdf,analysis of lora / tuning / ICL
294instruct me more! random prompting for visual incontext learning,http://arxiv.org/pdf/2311.03648v1.pdf,visual only
295incontext alignment chat with vanilla language models before finetuning,http://arxiv.org/pdf/2308.04275v1.pdf,fine-tuning
296gpt4 vision on medical image classification a case study on covid19 dataset,http://arxiv.org/pdf/2310.18498v1.pdf,visual only
297fewshot parameterefficient finetuning is better and cheaper than incontext learning,http://arxiv.org/pdf/2205.05638v2.pdf,fine-tuning
298images speak in images a generalist painter for incontext visual learning,http://arxiv.org/pdf/2212.02499v2.pdf,visual only
299how does incontext learning help prompt tuning,http://arxiv.org/pdf/2302.11521v1.pdf,fine-tuning
300symbol tuning improves incontext learning in language models,http://arxiv.org/pdf/2305.08298v1.pdf,fine-tuning
301iterative forward tuning boosts incontext learning in language models,http://arxiv.org/pdf/2305.13016v2.pdf,fine-tuning
302estimating large language model capabilities without labeled test data,http://arxiv.org/pdf/2305.14802v2.pdf,out of scope analysis
303augmenting language models with longterm memory,http://arxiv.org/pdf/2306.07174v1.pdf,new architecture
304o3d offline datadriven discovery and distillation for sequential decisionmaking with large language models,http://arxiv.org/pdf/2310.14403v1.pdf,fine-tuning
305deja vu contextual sparsity for efficient llms at inference time,http://arxiv.org/pdf/2310.17157v1.pdf,new architecture
306principledriven selfalignment of language models from scratch with minimal human supervision,http://arxiv.org/pdf/2305.03047v1.pdf,fine-tuning
307one for all towards training one graph model for all classification tasks,http://arxiv.org/pdf/2310.00149v1.pdf,new architecture
308magma multimodal augmentation of generative models through adapterbased finetuning,http://arxiv.org/pdf/2112.05253v2.pdf,fine-tuning
309blackbox tuning for languagemodelasaservice,http://arxiv.org/pdf/2201.03514v4.pdf,fine-tuning
310contrastive learning for promptbased fewshot language learners,http://arxiv.org/pdf/2205.01308v1.pdf,fine-tuning
311exploring length generalization in large language models,http://arxiv.org/pdf/2207.04901v2.pdf,out of scope analysis
312explanations from large language models make small reasoners better,http://arxiv.org/pdf/2210.06726v1.pdf,out of scope analysis
313visual programming compositional visual reasoning without training,http://arxiv.org/pdf/2211.11559v1.pdf,visual only
314"don't generate, discriminate a proposal for grounding language models to realworld environments",http://arxiv.org/pdf/2212.09736v2.pdf,new architecture
315neural codec language models are zeroshot text to speech synthesizers,http://arxiv.org/pdf/2301.02111v1.pdf,speech
316looped transformers as programmable computers,http://arxiv.org/pdf/2301.13196v1.pdf,out of scope analysis
317grounding language models to images for multimodal inputs and outputs,http://arxiv.org/pdf/2301.13823v4.pdf,new architecture
318proofnet autoformalizing and formally proving undergraduatelevel mathematics,http://arxiv.org/pdf/2302.12433v1.pdf,new architecture
319speak foreign languages with your own voice crosslingual neural codec language modeling,http://arxiv.org/pdf/2303.03926v1.pdf,speech
320when braininspired ai meets agi,http://arxiv.org/pdf/2303.15935v1.pdf,overview paper
321larger probes tell a different story extending psycholinguistic datasets via incontext learning,http://arxiv.org/pdf/2303.16445v1.pdf,dataset
322seggpt segmenting everything in context,http://arxiv.org/pdf/2304.03284v1.pdf,new architecture
323towards robust prompts on visionlanguage models,http://arxiv.org/pdf/2304.08479v1.pdf,vision-only
324understanding and predicting human label variation in natural language inference through explanation,http://arxiv.org/pdf/2304.12443v1.pdf,out of scope analysis
325otter a multimodal model with incontext instruction tuning,http://arxiv.org/pdf/2305.03726v1.pdf,new architecture
326transformers learn incontext by gradient descent,http://arxiv.org/pdf/2212.07677v2.pdf, analysis of ICL as a learning algorithm
327the closeness of incontext learning and weight shifting for softmax regression,http://arxiv.org/pdf/2304.13276v1.pdf, analysis of ICL as a learning algorithm
328what learning algorithm is incontext learning investigations with linear models,http://arxiv.org/pdf/2211.15661v3.pdf, analysis of ICL as a learning algorithm
329transformers as algorithms generalization and stability in incontext learning,http://arxiv.org/pdf/2301.07067v2.pdf, analysis of ICL as a learning algorithm
330explaining emergent incontext learning as kernel regression,http://arxiv.org/pdf/2305.12766v2.pdf, analysis of ICL as a learning algorithm
331label words are anchors an information flow perspective for understanding incontext learning,http://arxiv.org/pdf/2305.14160v1.pdf, analysis of ICL as a learning algorithm
332transformers learn to implement preconditioned gradient descent for incontext learning,http://arxiv.org/pdf/2306.00297v1.pdf, analysis of ICL as a learning algorithm
333investigating the learning behaviour of incontext learning a comparison with supervised learning,http://arxiv.org/pdf/2307.15411v2.pdf, analysis of ICL as a learning algorithm
334incontext learning with transformer is really equivalent to a contrastive learning pattern,http://arxiv.org/pdf/2310.13220v1.pdf, analysis of ICL as a learning algorithm
335incontext learning creates task vectors,http://arxiv.org/pdf/2310.15916v1.pdf, analysis of ICL as a learning algorithm
336"what and how does incontext learning learn bayesian model averaging, parameterization, and generalization",http://arxiv.org/pdf/2305.19420v2.pdf, analysis of ICL as a learning algorithm
337how do transformers learn incontext beyond simple functions a case study on learning with representations,http://arxiv.org/pdf/2310.10616v1.pdf, analysis of ICL as a learning algorithm
338transformers learn higherorder optimization methods for incontext learning a study with linear models,http://arxiv.org/pdf/2310.17086v1.pdf, analysis of ICL as a learning algorithm
339a contemporaneous infrared flash from a long gammaray burst an echo from the central engine,httpdxdoiorg101038nature03520,Not prompting related
340stellar explosions by magnetic towers,httpdxdoiorg101086505621,Not prompting related
341high energy radiation from gamma ray bursts,httpdxdoiorg10106311291372,Not prompting related
342the fireball shock model of gamma ray bursts,httpdxdoiorg10106311361591,Not prompting related
343origin of gamma ray bursters,httpdxdoiorg101143ptps136300,Not prompting related
344the updated e_peak e_gamma correlation in grbs,httpdxdoiorg101393ncci2005100460,Not prompting related
345gammaray burst early afterglows,httpdxdoiorg10106312141841,Not prompting related
346mevgev emission from neutronloaded short gammaray burst jets,httpdxdoiorg101086507261,Not prompting related
347a two component jet model for the xray afterglow flat segment in short grb 051221a,httpdxdoiorg101086512971,Not prompting related
348the shallow phase of xray afterglows,httpdxdoiorg10106312943505,Not prompting related
349hyperaccretion after the blandfordznajek process a new model for grbs with xray flares observed in early afterglows,httpdxdoiorg101088100992718404,Not prompting related
350high energy gammaray emission from gammaray bursts before glast,httpdxdoiorg101007s114670080033z,Not prompting related
351expected performance of a hard xray polarimeter (polar) by monte carlo simulation,httpdxdoiorg101016jnima200904033,Not prompting related
352what do we know about gammaray bursts,httparxivorgabs10094648v2,Not prompting related
353possible origin of rapid variability of gammaray bursts due to convective energy transfer in hyperaccretion disks,httpdxdoiorg101111j13652966201119733x,Not prompting related
354gammaray burst without baryonic and magnetic load,httpdxdoiorg101143ptp126555,Not prompting related
355the physical origin of optical flares following grb 110205a and the nature of the outflow,httpdxdoiorg101088167445271111007,Not prompting related
356magnetic structures in gammaray burst jets probed by gammaray polarization,httpdxdoiorg101088204182057581l1,Not prompting related
357astrophysical zev acceleration in the relativistic jet from an accreting supermassive blackhole,httpdxdoiorg101016jastropartphys201402004,Not prompting related
358neutrinocooled accretion model with magnetic coupling for xray flares in grbs,httpdxdoiorg1010880004637x7732142,Not prompting related
359jet luminosity from neutrinodominated accretion flows in grbs,httparxivorgabs13083236v1,Not prompting related
3603d manipulation with scanning near field optical nanotweezers,httpdxdoiorg101038nnano201424,Not prompting related
361tuning a multiple classifier system for side effect discovery using genetic algorithms,httparxivorgabs14091053v1,Not prompting related
362moltensalt depleteduranium reactor,httparxivorgabs150303183v1,Not prompting related
363xray flares in grbs general considerations and photospheric origin,httpdxdoiorg101093mnraslslw003,Not prompting related
364waterinduced bimetallic alloy surface segregation a first principle study,httparxivorgabs160102346v1,Not prompting related
365rates and singlettriplet ratios from tadf transients,httparxivorgabs160308998v2,Not prompting related
366physical limits to magnetogenetics,httpdxdoiorg107554elife17210,Not prompting related
367the dark side of ethical robots,httparxivorgabs160602583v1,Not prompting related
368numerical and analytical solutions of neutrinodominated accretion flows with a nonzero torque boundary condition and its applications in gammaray bursts,httpdxdoiorg103847153843578332129,Not prompting related
369highenergy emission as signature of magnetic field amplification in neutron star mergers,httparxivorgabs170101184v1,Not prompting related
370gammaray burst models in light of the grb 170817a gw170817 connection,httparxivorgabs180207328v1,Not prompting related
371surface modified mesoporous gc3n4@feni3 as prompt and proficient magnetic adsorbent for crude oil recovery,httpdxdoiorg101016japsusc201812166,Not prompting related
372the perfect state transfer graph limbo,httparxivorgabs180800696v2,Not prompting related
373variabilities of gammaray bursts from black hole hyperaccretion disks,httpdxdoiorg101093mnrasstw1985,Not prompting related
374data driven exploratory attacks on black box classifiers in adversarial domains,httpdxdoiorg101016jneucom201802007,Not prompting related
375migrating large codebases to c++ modules,httpdxdoiorg1010881742659615251012051,Not prompting related
376mn(ii)doped 2d perovskite for light emitting devices,httparxivorgabs190605099v1,Not prompting related
377deep sequential feature learning in clinical image classification of infectious keratitis,httparxivorgabs200602666v1,Not prompting related
378hydrodynamics of corecollapse supernovae and their progenitors,httpdxdoiorg101007s4111502000085,Not prompting related
379xray plateaus in $γ$ray bursts explained by structured jets,httparxivorgabs200613966v1,Not prompting related
380polar a spaceborne xray polarimeter for transient sources,httpdxdoiorg105194astra7432011,Not prompting related
381the change of grb polarization angles in the magneticdominated jet model,httpdxdoiorg101093mnrasstu2051,Not prompting related
382perspective quantum thermodynamics,httpdxdoiorg10108813672630181011002,Not prompting related
383observational evidence for mass ejection accompanying short gamma ray bursts,httpdxdoiorg101093mnraslslx131,Not prompting related
384photospheric emission from variable engine gamma ray burst simulations,httpdxdoiorg10384715384357aaeed1,Not prompting related
385the divideandconquer framework a suitable setting for the ddm of the future,httparxivorgabs190100229v1,Not prompting related
386spectral puzzle of the offaxis gammaray burst in gw170817,httpdxdoiorg101093mnrasstz1650,Not prompting related
387"equationofstate, critical constants, and thermodynamic properties of lithium at high energy density",httpdxdoiorg10106315143308,Not prompting related
388interpreting the xray afterglows of gammaray bursts with radiative losses and millisecond magnetars,httpdxdoiorg101093mnrasstaa3090,Not prompting related
389wavelet denoising and attentionbased rnnarima model to predict forex price,httparxivorgabs200806841v1,Not prompting related
390testing blandfordznajek mechanism in black hole hyperaccretion flows for longduration gammaray bursts,httpdxdoiorg10384715384357abd6bd,Not prompting related
391deep learningbased detection of the acute respiratory distress syndrome what are the models learning,httparxivorgabs210912323v1,Not prompting related
392"continuationpassing style, defunctionalization, accumulations, and associativity",httpdxdoiorg1022152programmingjournalorg202267,Not prompting related
393helyos a customized offtheshelf solution for autonomous driving applications in delimited areas,httpdxdoiorg101109sii55687202310039276,Not prompting related
394the structure of gamma ray burst jets,httparxivorgabs220611088v2,Not prompting related
395