arXiv AI
Jul 23
Self-Preference Bias in Rubric-Based Evaluation of Large Language Models
arXiv:2604. 06996v2 Announce Type: replace-cross Abstract: LLM-as-a-judge has become the de facto approach for evaluating LLM outputs.
JudgmentBench: Comparing Rubric and Preference Evaluation for Quality Assessment
arXiv:2605. 25240v2 Announce Type: replace-cross Abstract: Two methodologies dominate current practices of benchmarking: rubric-based scoring evaluates items against predefined criteria, whereas comparative judgment elicits pairwise preferences between outputs.
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arXiv AI
4d ago
JudgeProfile: Understanding and Steering Subjectivity in LLM Judges
JudgeProfile is a framework that analyzes the subjectivity of large language model (LLM) judges by separating evaluation into perception—how judges compare responses on attributes such as clarity, correctness, and detail—and prioritization—how much each attribute influences the final decision. Using the curated SubjectiveSet dataset of 50,013 response pairs evaluated by 21 judges across 87 attributes, the study finds that judges often agree on attribute judgments even when their overall choices differ. By estimating and adjusting attribute weights, the authors improve agreement with reference labels from 66.48% to 71.97%, outperforming fine‑tuning and rubric prompting.
arXiv AI
2d ago
Legal Research Bench: Measuring End-to-End Reliability in Long-Horizon Legal Research Agents
Legal Research Bench (LRB) is a new benchmark comprising 413 open-ended U.S. legal research questions, each paired with a gold answer, supporting authorities, and a binary grading rubric. The study evaluates thirteen advanced language‑model agents using web search, case‑law search, page parsing, and retrieval tools, scoring responses only when all required criteria are met and cited authorities verify. Results show that even the best model, Claude Opus 4.8, achieves full correctness on only 42.9% of questions, with performance varying by legal area and task complexity, and no clear link between more tool calls or inference cost and higher accuracy.
arXiv AI
Jun 2
BenGER: Benchmarking LLM Systems on Subsumption-Based Legal Reasoning in German Law
arXiv:2605. 28183v2 Announce Type: replace-cross Abstract: We introduce the BenGER (Benchmark for German Law) dataset for evaluating LLM systems on subsumption-based legal reasoning in German law.
arXiv Computation and Language
Sep 25
JEV vs. LLMs as Rubric Judges: Cheaper, Faster, and Wrong in the Same Places
The study investigates whether Jev, a typed classifier that outputs probabilities over allowed answers without generating text, can replace large language model (LLM) rubric judges. Across nine panels from seven benchmarks, Jev’s accuracy differed significantly from LLM judges in only 8 of 27 paired comparisons, performing best on binary criteria and worse only on graded ones, while most other comparisons were inconclusive. In terms of cost and speed, Jev was 29 to 325 times cheaper and 30 to 220 times faster than the flash‑tier LLM judges, and a cascade approach that defers uncertain Jev verdicts to an LLM yielded only modest gains. whyItMatters":"The findings suggest that a lightweight classifier like Jev can serve as an efficient first‑stage evaluator, potentially reducing the reliance on expensive and slow LLM judges in automated grading pipelines."
Hugging Face Trending Papers
Sep 24
JEV vs. LLMs as Rubric Judges: Cheaper, Faster, and Wrong in the Same Places
The study compares Jev, a typed classifier that outputs probabilities over allowed answers, with three flash-tier LLM rubric judges across nine panels from seven benchmarks. Jev’s accuracy differs significantly from an LLM judge in only 8 of 27 paired comparisons, performing best on binary criteria and worse only on graded ones, while most other comparisons are inconclusive. In terms of cost and speed, Jev is 29 to 325 times cheaper and 30 to 220 times faster than the LLM judges, and a cascade that defers uncertain Jev verdicts to an LLM yields only modest gains of up to 2.0 points over the best single judge.
arXiv AI
Jul 3
AI Assistance for Human Review of Default Judgments
arXiv:2607. 01256v1 Announce Type: cross Abstract: Overwhelmed courts in the United States review millions of default judgments each year.
arXiv AI
Jun 18
TW-LegalBench: Measuring Taiwanese Legal Understanding
arXiv:2606. 18699v1 Announce Type: cross Abstract: Large language models (LLMs) have shown impressive capabilities across diverse tasks, yet their performance on jurisdiction-specific legal reasoning remains underexplored.
arXiv Computation and Language
3d ago
LexReward: A Taxonomy-Driven Reward Framework for Legal Language Models
LexReward is a taxonomy-driven reward framework designed for legal language models, evaluating responses across three dimensions: Style (lexical and syntactic quality), Element (legal subjects, facts, statutes, and decisions), and Chain (order, completeness, correctness, and non-redundancy of reasoning). The framework introduces rubrics that define evaluation criteria and quality levels for each dimension, generating pairwise preference data used for Direct Preference Optimization (DPO) and reward-model training. Experiments demonstrate that rubric-based rewards effectively differentiate legal responses of varying quality, and that DPO training improves performance across all dimensions; the resulting reward models (LexRM) enable reinforcement learning to enhance policy performance in each specific dimension without needing reference answers.
arXiv AI
Sep 4
Judging LLM-as-a-Judge: Concerning Rubric Artifacts in LLM-based Automated Text Generation Evaluation
The paper examines LLM-as-a-Judge systems used to assess AI-generated text, questioning the assumption that judgments are derived from reasoning over responses and rubrics. It finds that classifiers trained solely on rubric text can predict judge outputs, indicating that rubrics contain recoverable evaluative signals independent of the responses. Counterfactual experiments show judges often fail to adjust decisions when either the response or rubric criterion is reversed, raising doubts about the reliability of rubric-based LLM evaluation.
arXiv AI
Aug 20
SESSE: Sketch, Expand, Sort, Summarize, Evaluate -- LLM-as-Judge Evaluation via Structured Decomposition
The paper introduces SESSE, a training‑free framework that breaks down LLM‑as‑judge evaluations into five steps—Sketch, Expand, Sort, Summarize, Evaluate—by mining sub‑questions from the judge’s own error cases. It requires no oracle responses, task‑specific rubrics, or fine‑tuning, yet on RewardBench it matches the performance of chain‑of‑thought baselines and rivals a fine‑tuned specialist (RISE‑Judge‑32B). SESSE provides per‑criterion vote evidence, offering an interpretable audit trail that can diagnose label ambiguity and judge failure modes that a single holistic output token cannot reveal.
arXiv AI
4d ago
From Judgment Quality to Downstream Utility: Rethinking LLM-as-a-Judge for Open-Ended Tasks
The paper investigates how the design of LLM-as-a-Judge protocols influences both the intrinsic quality of judgments and their downstream utility in open-ended tasks. By varying verdict granularity, critique usage, and evaluation batching, and by applying Judge guidance to test-time inference methods such as Best-of-N selection, revision, and beam search, the authors find that judgment quality and downstream performance do not always align and that protocol choices significantly affect outcomes. The study highlights the need for comprehensive evaluation of LLM Judges that considers both judgment quality and practical utility.