LLM-as-a-Judge: Rapid Evaluation of Legal Document Recommendation for Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2509.12382v1
• Date: Mon, 15 Sep 2025 19:20:21 GMT
• Title: LLM-as-a-Judge: Rapid Evaluation of Legal Document Recommendation for Retrieval-Augmented Generation
• Authors: Anu Pradhan, Alexandra Ortan, Apurv Verma, Madhavan Seshadri,
• Abstract summary: This paper investigates a principled approach to evaluating Retrieval-Augmented Generation systems in legal contexts.<n>We find that traditional agreement metrics like Krippendorff's alpha can be misleading in the skewed distributions typical of AI system evaluations.<n>Our findings suggest a path toward scalable, cost-effective evaluation that maintains the precision demanded by legal applications.
• Score: 40.06592175227558
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The evaluation bottleneck in recommendation systems has become particularly acute with the rise of Generative AI, where traditional metrics fall short of capturing nuanced quality dimensions that matter in specialized domains like legal research. Can we trust Large Language Models to serve as reliable judges of their own kind? This paper investigates LLM-as-a-Judge as a principled approach to evaluating Retrieval-Augmented Generation systems in legal contexts, where the stakes of recommendation quality are exceptionally high. We tackle two fundamental questions that determine practical viability: which inter-rater reliability metrics best capture the alignment between LLM and human assessments, and how do we conduct statistically sound comparisons between competing systems? Through systematic experimentation, we discover that traditional agreement metrics like Krippendorff's alpha can be misleading in the skewed distributions typical of AI system evaluations. Instead, Gwet's AC2 and rank correlation coefficients emerge as more robust indicators for judge selection, while the Wilcoxon Signed-Rank Test with Benjamini-Hochberg corrections provides the statistical rigor needed for reliable system comparisons. Our findings suggest a path toward scalable, cost-effective evaluation that maintains the precision demanded by legal applications, transforming what was once a human-intensive bottleneck into an automated, yet statistically principled, evaluation framework.

Related papers

• SAGE: Scalable AI Governance & Evaluation [10.238041570564395]
  textbfSAGE is a framework that operationalizes high-quality human product judgment as a scalable evaluation signal.<n>SAGE was deployed within LinkedIn Search ecosystems and powered policy oversight that measured ramped model variants and detected regressions invisible to engagement metrics.
  arXiv  Detail & Related papers  (2026-02-08T06:42:50Z)
• R-Align: Enhancing Generative Reward Models through Rationale-Centric Meta-Judging [69.96389360650072]
  We show that reasoning fidelity is highly predictive of downstream RLHF outcomes, beyond standard label accuracy.<n>We propose Rationale-Centric Alignment, R-Align, which augments training with gold judgments and explicitly supervises rationale alignment.
  arXiv  Detail & Related papers  (2026-02-06T15:17:11Z)
• Open-World Deepfake Attribution via Confidence-Aware Asymmetric Learning [78.92934995292113]
  We propose a Confidence-Aware Asymmetric Learning (CAL) framework, which balances confidence across known and novel forgery types.<n>CAL consistently outperforms previous methods, achieving new state-of-the-art performance on both known and novel forgery attribution.
  arXiv  Detail & Related papers  (2025-12-14T12:31:28Z)
• Who Judges the Judge? LLM Jury-on-Demand: Building Trustworthy LLM Evaluation Systems [2.9141470183751674]
  We propose a dynamic, learning-based framework for scalable and context-aware evaluation.<n>Our method trains a set of reliability predictors to assess when LLM judges will agree with human experts.<n> Experiments on summarization and RAG benchmarks show that our dynamic jury system achieves significantly higher correlation with human judgment than both single-judge and static-jury baselines.
  arXiv  Detail & Related papers  (2025-12-01T15:26:20Z)
• Reference-Free Rating of LLM Responses via Latent Information [53.463883683503106]
  We study the common practice of asking a judge model to assign Likert-scale scores to free-text responses.<n>We then propose and evaluate Latent Judges, which derive scalar ratings from internal model signals.<n>Across a broad suite of pairwise and single-rating benchmarks, latent methods match or surpass standard prompting.
  arXiv  Detail & Related papers  (2025-09-29T12:15:52Z)
• TrustJudge: Inconsistencies of LLM-as-a-Judge and How to Alleviate Them [58.04324690859212]
  Large Language Models (LLMs) as automated evaluators (LLM-as-a-judge) has revealed critical inconsistencies in current evaluation frameworks.<n>We identify two fundamental types of inconsistencies: Score-Comparison Inconsistency and Pairwise Transitivity Inconsistency.<n>We propose TrustJudge, a probabilistic framework that addresses these limitations through two key innovations.
  arXiv  Detail & Related papers  (2025-09-25T13:04:29Z)
• Expert Preference-based Evaluation of Automated Related Work Generation [54.29459509574242]
  We propose GREP, a multi-turn evaluation framework that integrates classical related work evaluation criteria with expert-specific preferences.<n>For better accessibility, we design two variants of GREP: a more precise variant with proprietary LLMs as evaluators, and a cheaper alternative with open-weight LLMs.
  arXiv  Detail & Related papers  (2025-08-11T13:08:07Z)
• Towards Robust Offline Evaluation: A Causal and Information Theoretic Framework for Debiasing Ranking Systems [6.540293515339111]
  offline evaluation of retrieval-ranking systems is crucial for developing high-performing models.<n>We propose a novel framework for robust offline evaluation of retrieval-ranking systems.<n>Our contributions include (1) a causal formulation for addressing offline evaluation biases, (2) a system-agnostic debiasing framework, and (3) empirical validation of its effectiveness.
  arXiv  Detail & Related papers  (2025-04-04T23:52:57Z)
• DAFE: LLM-Based Evaluation Through Dynamic Arbitration for Free-Form Question-Answering [12.879551933541345]
  We propose the Dynamic Arbitration Framework for Evaluation (DAFE) to evaluate large language models.<n>DAFE employs two primary LLM-as-judges and engages a third arbitrator only in cases of disagreements.<n>We show DAFE's ability to provide consistent, scalable, and resource-efficient assessments.
  arXiv  Detail & Related papers  (2025-03-11T15:29:55Z)
• JudgeRank: Leveraging Large Language Models for Reasoning-Intensive Reranking [81.88787401178378]
  We introduce JudgeRank, a novel agentic reranker that emulates human cognitive processes when assessing document relevance. We evaluate JudgeRank on the reasoning-intensive BRIGHT benchmark, demonstrating substantial performance improvements over first-stage retrieval methods. In addition, JudgeRank performs on par with fine-tuned state-of-the-art rerankers on the popular BEIR benchmark, validating its zero-shot generalization capability.
  arXiv  Detail & Related papers  (2024-10-31T18:43:12Z)
• VERA: Validation and Evaluation of Retrieval-Augmented Systems [5.709401805125129]
  VERA is a framework designed to enhance the transparency and reliability of outputs from large language models (LLMs) We show how VERA can strengthen decision-making processes and trust in AI applications.
  arXiv  Detail & Related papers  (2024-08-16T21:59:59Z)
• A Normative Framework for Benchmarking Consumer Fairness in Large Language Model Recommender System [9.470545149911072]
  This paper proposes a normative framework to benchmark consumer fairness in LLM-powered recommender systems. We argue that this gap can lead to arbitrary conclusions about fairness. Experiments on the MovieLens dataset on consumer fairness reveal fairness deviations in age-based recommendations.
  arXiv  Detail & Related papers  (2024-05-03T16:25:27Z)
• Aligning with Human Judgement: The Role of Pairwise Preference in Large Language Model Evaluators [48.54465599914978]
  Large Language Models (LLMs) have demonstrated promising capabilities as automatic evaluators in assessing the quality of generated natural language.<n>LLMs still exhibit biases in evaluation and often struggle to generate coherent evaluations that align with human assessments.<n>We introduce Pairwise-preference Search (PAIRS), an uncertainty-guided search-based rank aggregation method that employs LLMs to conduct pairwise comparisons locally and efficiently ranks candidate texts globally.
  arXiv  Detail & Related papers  (2024-03-25T17:11:28Z)
• Towards a multi-stakeholder value-based assessment framework for algorithmic systems [76.79703106646967]
  We develop a value-based assessment framework that visualizes closeness and tensions between values. We give guidelines on how to operationalize them, while opening up the evaluation and deliberation process to a wide range of stakeholders.
  arXiv  Detail & Related papers  (2022-05-09T19:28:32Z)
• Re-Examining System-Level Correlations of Automatic Summarization Evaluation Metrics [64.81682222169113]
  How reliably an automatic summarization evaluation metric replicates human judgments of summary quality is quantified by system-level correlations. We identify two ways in which the definition of the system-level correlation is inconsistent with how metrics are used to evaluate systems in practice.
  arXiv  Detail & Related papers  (2022-04-21T15:52:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.