mitigation
cti-mitigation-embeddermeta-llama-3.1-8b-instruct-APPS-modularity-MaxEntropy-Mitigationmeta-llama-3.1-8b-instruct-APPS-logic_bomb-prompted-mitigation-noneCPU_Mitigation_Classifiermeta-llama-3.1-8b-instruct-APPS-logic_bomb-prompted-mitigation-maxEntropysycophancy_mitigation_gpt_oss_20b_oxenv1ikitracs_mitigationmpnet-adaptation_mitigation-classifier
hiring-bias-mitigation-responses
Hiring-bias mitigation — model responses
Every response produced in the mitigation study of LLM hiring decisions: 54 runs,
2,471,850 responses, from 5 open-weight models in English and Ukrainian, at
baseline and under each mitigation family (baseline, embedding, prompt, scrub). Each run is one subset.
All released artifacts: the Hiring Bias Mitigation collection.
Training data of the fine-tuned runs: hiring-bias-mitigation-synthetic-data.
Code, configs, full results and… See the full description on the dataset page: https://huggingface.co/datasets/Stereotypes-in-LLMs/hiring-bias-mitigation-responses.from-one-to-many-toxicity-mitigation
From One to Many: Expanding the Scope of Toxicity Mitigation in Language Models
[arxiv][code][data]
Data accompanying the paper "From One to Many: Expanding the Scope of Toxicity Mitigation in Language Models" accepted to ACL Findings 2024.
Abstract: To date, toxicity mitigation in language models has almost entirely been focused on single-language settings. As language models embrace multilingual capabilities, it’s crucial our safety measures keep pace. Recognizing this research… See the full description on the dataset page: https://huggingface.co/datasets/luizapzbn/from-one-to-many-toxicity-mitigation.hiring-bias-mitigation-synthetic-data
Hiring-bias mitigation — synthetic training data
Semi-synthetic data for training LLMs to make hiring decisions that do not depend on a
protected attribute (military status, gender, religion), in English and Ukrainian.
Real inputs, synthetic labels. CVs and job descriptions are real, anonymised postings
from the Djinni Recruitment Dataset (MIT). Decisions and rationales were written by the
teacher model Qwen/Qwen3.5-122B-A10B-GPTQ-Int4.
Code and results:… See the full description on the dataset page: https://huggingface.co/datasets/Stereotypes-in-LLMs/hiring-bias-mitigation-synthetic-data.omnimcp_cyber_ddos_mitigation_teaser
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📜 Enterprise Compliance: EU AI Act Articles 50 & 53 certified • 100% DSGVO / GDPR clean • Commercial EULA… See the full description on the dataset page: https://huggingface.co/datasets/emgena/omnimcp_cyber_ddos_mitigation_teaser.quantum-error-mitigation-and-benchmarking
Neura Parse — Quantum Error Mitigation, Characterization & Benchmarking
A pre-fault-tolerance, code-backed vertical on getting trustworthy answers from noisy hardware and rigorously measuring device quality: error-mitigation techniques, characterization/tomography protocols, and benchmarking suites. Runnable Mitiq, pyGSTi, and Qiskit Experiments pipelines with honest sampling-overhead and bias/variance accounting — the practitioner and research toolkit the general dataset… See the full description on the dataset page: https://huggingface.co/datasets/Neura-parse/quantum-error-mitigation-and-benchmarking.BehaviouralLoC-Mitigation
BehaviouralLoC-Mitigation
BehaviouralLoC-Mitigation contains the supervised fine-tuning corpora used for
misaligned-motive mitigation in A Behavioural Framework for Predicting and
Understanding Loss of Control in Frontier Artificial Intelligence Systems.
The corpus covers five motive aspects. Following the paper, examples were
generated in distribution with Qwen3.5-27B, and the prompts were augmented by
safety experts.
Configurations
The three paper configurations… See the full description on the dataset page: https://huggingface.co/datasets/T-STAR-Lab/BehaviouralLoC-Mitigation.