A Judge Should Know What Changed:Construct Validity for LLM-as-a-Judge Evaluation

arXiv:2608.24419 · cs.AI · Submitted 2026-08-25 · Read on arXiv

cs.AI

Submitted: 2026-08-25

Updated: 2026-08-25

Code: https://github.com/Airjiannan05/TriDeHall

Terminology

• Accuracy — 21× in this paper · explained in 1 episode
• Provenance — 16× in this paper · explained in 3 episodes
• Benchmark — 15× in this paper · explained in 2 episodes
• Verifier — 15× in this paper · explained in 2 episodes
• Calibration — 14× in this paper · explained in 7 episodes
• Consistency — 10× in this paper · explained in 3 episodes
• Verification — 9× in this paper · explained in 2 episodes
• Association for Computational Linguistics — 8× in this paper
• LLM-as-a-Judge — 6× in this paper · explained in 3 episodes
• Self-consistency — 5× in this paper · explained in 2 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• Benchmarking — 3× in this paper · explained in 3 episodes
• Admissibility — 2× in this paper · explained in 2 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Core — 2× in this paper · explained in 2 episodes
• Hallucination — 2× in this paper · explained in 3 episodes
• LLM judge — 2× in this paper · explained in 2 episodes
• Positional Bias — 2× in this paper · explained in 2 episodes
• Reward Hacking — 2× in this paper · explained in 8 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Accountability — 1× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Adversarial Robustness — 1× in this paper · explained in 3 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Arbiter — 1× in this paper · explained in 1 episode
• Clustering — 1× in this paper · explained in 2 episodes
• Commentary — 1× in this paper · explained in 2 episodes
• Empirical Methods in Natural Language Processing — 1× in this paper
• Failure Modes — 1× in this paper · explained in 3 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Synthetic Data Generation — 1× in this paper · explained in 5 episodes

Sources

• Rubrics as Rewards: Reinforcement Learning Beyond Verifiable Domains
• Calibrating Verbal Uncertainty as a Linear Feature to Reduce Hallucinations
• Conformal Linguistic Calibration: Trading-off between Factuality and Specificity
• RewardBench: Evaluating Reward Models for Language Modeling
• RM-Bench: Benchmarking Reward Models of Language Models with Subtlety and Style
• A Question Answering Framework for Decontextualizing User-facing Snippets from Scientific Documents
• Direct Preference Optimization: Your Language Model is Secretly a Reward Model
• Rectifying Shortcut Behaviors in Preference-based Reward Learning

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