Presentation-only revisions guided by AI feedback can boost AI reviewer scores by over 1 point on average with 75% success rate across tested systems.
LLMs Cannot Reliably Judge (Yet?): A Comprehensive Assessment on the Robustness of LLM-as-a-Judge
9 Pith papers cite this work. Polarity classification is still indexing.
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RankJudge creates paired multi-turn conversations with isolated single-turn flaws to generate unambiguous benchmarks for LLM-as-a-judge systems across ML, biomedicine, and finance domains.
MCJudgeBench evaluates LLM judges at the constraint level with gold labels and inconsistency metrics, showing that overall performance does not ensure reliable detection of partial or no cases or stability under perturbations.
AgentLocate localizes multi-agent LLM failures to a responsible agent and earliest decisive step via judge hypotheses, confidence-weighted multi-evaluator verification, and LoRA refinement.
LLM safety judges flip verdicts on equivalent policy rewrites up to 9.1% of the time and cannot distinguish meaningful from meaningless changes, requiring new invariance-based reliability metrics.
AutoPyVerifier learns compact sets of executable Python verifiers from labeled LLM outputs via LLM synthesis and DAG search, improving objective prediction by up to 55 F1 points and downstream LLM accuracy by up to 17 points.
Both humans and LLMs trust content more when labeled human-authored than AI-generated, with LLMs showing denser attention to labels and higher uncertainty under AI labels, mirroring human heuristic patterns.
The paper introduces the Proxy Compression Hypothesis as a unifying framework explaining reward hacking in RLHF as an emergent result of compressing high-dimensional human objectives into proxy reward signals under optimization pressure.
Proxy RL produces a staged proxy-internalization capability that emerges before and predicts reward hacking in coding environments.
citing papers explorer
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No Hidden Prompts Needed! You Can Game AI Peer Review with Presentation-Only Revisions
Presentation-only revisions guided by AI feedback can boost AI reviewer scores by over 1 point on average with 75% success rate across tested systems.
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RankJudge: A Multi-Turn LLM-as-a-Judge Synthetic Benchmark Generator
RankJudge creates paired multi-turn conversations with isolated single-turn flaws to generate unambiguous benchmarks for LLM-as-a-judge systems across ML, biomedicine, and finance domains.
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MCJudgeBench: A Benchmark for Constraint-Level Judge Evaluation in Multi-Constraint Instruction Following
MCJudgeBench evaluates LLM judges at the constraint level with gold labels and inconsistency metrics, showing that overall performance does not ensure reliable detection of partial or no cases or stability under perturbations.
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Who Broke the System? Failure Localization in LLM-Based Multi-Agent Systems
AgentLocate localizes multi-agent LLM failures to a responsible agent and earliest decisive step via judge hypotheses, confidence-weighted multi-evaluator verification, and LoRA refinement.
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Beyond Accuracy: Policy Invariance as a Reliability Test for LLM Safety Judges
LLM safety judges flip verdicts on equivalent policy rewrites up to 9.1% of the time and cannot distinguish meaningful from meaningless changes, requiring new invariance-based reliability metrics.
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AutoPyVerifier: Learning Compact Executable Verifiers for Large Language Model Outputs
AutoPyVerifier learns compact sets of executable Python verifiers from labeled LLM outputs via LLM synthesis and DAG search, improving objective prediction by up to 55 F1 points and downstream LLM accuracy by up to 17 points.
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Label Effects: Shared Heuristic Reliance in Trust Assessment by Humans and LLM-as-a-Judge
Both humans and LLMs trust content more when labeled human-authored than AI-generated, with LLMs showing denser attention to labels and higher uncertainty under AI labels, mirroring human heuristic patterns.
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Reward Hacking in the Era of Large Models: Mechanisms, Emergent Misalignment, Challenges
The paper introduces the Proxy Compression Hypothesis as a unifying framework explaining reward hacking in RLHF as an emergent result of compressing high-dimensional human objectives into proxy reward signals under optimization pressure.
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Proxy Reward Internalization and Mechanistic Exploitation: A Learned Precursor to Reward Hacking and Its Generalization
Proxy RL produces a staged proxy-internalization capability that emerges before and predicts reward hacking in coding environments.