datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
vibench
VIBench
VIBench is a benchmark for measuring vertical integration bias in
direct and agentic code generation. It contains 20 direct scenarios and
20 aligned agentic workflows covering realistic software integration
choices across cloud and API ecosystems. The bundle includes the
benchmark tasks, model metadata, system prompts, provider-proof
artifacts, and the blind detector-audit sample used for validation.
Viewer splits
The dataset viewer is intentionally simplified to… See the full description on the dataset page: https://huggingface.co/datasets/vibench-emnlp26/vibench.vibench-results
VIBench Results
VIBench Results contains the retained raw generations,
detector-labeled outputs, complete runs, option-order and runtime
ablations, paper-facing summaries, figures, configs, audit files, and
static explorer indices used in the study.
The main paper evaluation covers 13 models, 15,600 direct generations,
and 2,000 agentic runs. Direct and agentic VIB are reported as
scenario-matched, share-normalized differences in affiliated-ecosystem
selection relative to strict… See the full description on the dataset page: https://huggingface.co/datasets/vibench-emnlp26/vibench-results.VIVA_Benchmark_EMNLP24
VIVA: A Benchmark for Vision-Grounded Decision-Making with Human Values
Zhe Hu1,
Yixiao Ren1,
Jing Li1,
Yu Yin2
1The Hong Kong Polytechnic University
2Case Western Reserve University
EMNLP 2024 (main)
📄 Paper
🌎 Website
💻 Code
Introduction
This is the official huggingface repo providing the benchmark of our EMNLP'24 paper:
VIVA: A Benchmark for… See the full description on the dataset page: https://huggingface.co/datasets/zhehuderek/VIVA_Benchmark_EMNLP24.CASTELLA
CASTELLA
This repository provides wav files used in CASTELLA: Long Audio Dataset with Captions and Temporal Boundaries
. This dataset is originally provided in GitHub.
Each sample includes long audio containing some audio events with the temporal and textual annotation.
Project page: https://h-munakata.github.io/CASTELLA-demo/
Code: https://github.com/line/lighthouse
This repository only contains annotation data, not audio data.
Extracted features are available on HF.
If you need… See the full description on the dataset page: https://huggingface.co/datasets/lighthouse-emnlp2024/CASTELLA.emnlp-2026-ifr-train-val-set
EMNLP 2026 Findings: On-Policy Distillation Meets Off-Policy GRPO — Training Compact Instruction-Following Rerankers
Train and validation splits used to train and evaluate the instruction-following reranker (IFR) in an in-distribution setting. The benchmark is an aggregated compilation of eight public instruction-following retrieval datasets spanning web search, code, mathematics, news, and multi-hop retrieval.
Composition
Source dataset
Train
Validation… See the full description on the dataset page: https://huggingface.co/datasets/anonymousauthor01/emnlp-2026-ifr-train-val-set.RaVE_emnlp23
Dataset Card for VECHR
Dataset Summary
From Dissonance to Insights: Dissecting Disagreements in Rationale Construction for Case Outcome Classification
In legal NLP, Case Outcome Classification (COC) must not only be accurate but also trustworthy and explainable. Existing work in explainable COC has been limited to annotations by a single expert. However, it is well-known that lawyers may disagree in their assessment of case facts. We hence collect a novel dataset RaVE:… See the full description on the dataset page: https://huggingface.co/datasets/sxu/RaVE_emnlp23.AudioMomentRetrievalFromLongAudio_DCASE2026EvaluationData
What is this?
This repository contains data for DCASE Challenge 2026 Task 6.
Audio and text features using CLAP and a sliding window, following the same feature extraction protocol as the CASTELLA dataset.
submission template
File structure
clap
└──dcase2026_evaluation_audio_{vid}.npz
clap_text
└──qiddcase2026_evaluation_q{qid}.npz
Raw audio files
If participants require the raw audio, please contact the… See the full description on the dataset page: https://huggingface.co/datasets/lighthouse-emnlp2024/AudioMomentRetrievalFromLongAudio_DCASE2026EvaluationData.emnlp2016_2018Only for researching usage.
The papers download from the https://sbert.net/datasets/emnlp2016-2018.json