Decoding-time use of process reward models for bias mitigation raises fairness scores by up to 0.40 on a bilingual benchmark while preserving fluency across four LLMs and extends to open-ended generation with low overhead.
Holistic evaluation of language models.Transactions on Machine Learning Research
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4representative citing papers
A new benchmark (IG-Bench) reveals that LLM-based scientists fail at compositional lineage reasoning, with the best system reaching only 27.3% exact accuracy.
PSA-Eval reframes evaluation of trilingual public-space agents around traceable failures and regression testing, revealing cross-language score drift in a pilot despite high average performance.
Synthesizes mechanisms of LLM censorship across the model lifecycle and argues that the key issue is making moderation proportionate, accountable, pluralistic, and auditable rather than debating whether moderation should occur.
citing papers explorer
-
Decoding-Time Debiasing via Process Reward Models: From Controlled Fill-in to Open-Ended Generation
Decoding-time use of process reward models for bias mitigation raises fairness scores by up to 0.40 on a bilingual benchmark while preserving fluency across four LLMs and extends to open-ended generation with low overhead.
-
Ideas Have Genomes: Benchmarking Scientific Lineage Reasoning and Lineage-Grounded Idea Generation
A new benchmark (IG-Bench) reveals that LLM-based scientists fail at compositional lineage reasoning, with the best system reaching only 27.3% exact accuracy.
-
Failure-Centered Runtime Evaluation for Deployed Trilingual Public-Space Agents
PSA-Eval reframes evaluation of trilingual public-space agents around traceable failures and regression testing, revealing cross-language score drift in a pilot despite high average performance.
-
Understanding Censorship in Large Language Models: From Mechanisms to Governance
Synthesizes mechanisms of LLM censorship across the model lifecycle and argues that the key issue is making moderation proportionate, accountable, pluralistic, and auditable rather than debating whether moderation should occur.