Measuring Faithfulness and Abstention: An Automated Pipeline for Evaluating LLM-Generated 3-ply Case-Based Legal Arguments

• URL: http://arxiv.org/abs/2506.00694v2
• Date: Tue, 03 Jun 2025 03:22:48 GMT
• Title: Measuring Faithfulness and Abstention: An Automated Pipeline for Evaluating LLM-Generated 3-ply Case-Based Legal Arguments
• Authors: Li Zhang, Morgan Gray, Jaromir Savelka, Kevin D. Ashley,
• Abstract summary: Large Language Models (LLMs) demonstrate potential in complex legal tasks like argument generation.<n>This paper introduces an automated pipeline to evaluate LLM performance on this task.<n>We define hallucination as the generation of factors not present in the input case materials and abstention as the model's ability to refrain from generating arguments.
• Score: 4.151328330778482
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Large Language Models (LLMs) demonstrate potential in complex legal tasks like argument generation, yet their reliability remains a concern. Building upon pilot work assessing LLM generation of 3-ply legal arguments using human evaluation, this paper introduces an automated pipeline to evaluate LLM performance on this task, specifically focusing on faithfulness (absence of hallucination), factor utilization, and appropriate abstention. We define hallucination as the generation of factors not present in the input case materials and abstention as the model's ability to refrain from generating arguments when instructed and no factual basis exists. Our automated method employs an external LLM to extract factors from generated arguments and compares them against the ground-truth factors provided in the input case triples (current case and two precedent cases). We evaluated eight distinct LLMs on three tests of increasing difficulty: 1) generating a standard 3-ply argument, 2) generating an argument with swapped precedent roles, and 3) recognizing the impossibility of argument generation due to lack of shared factors and abstaining. Our findings indicate that while current LLMs achieve high accuracy (over 90%) in avoiding hallucination on viable argument generation tests (Tests 1 & 2), they often fail to utilize the full set of relevant factors present in the cases. Critically, on the abstention test (Test 3), most models failed to follow instructions to stop, instead generating spurious arguments despite the lack of common factors. This automated pipeline provides a scalable method for assessing these crucial LLM behaviors, highlighting the need for improvements in factor utilization and robust abstention capabilities before reliable deployment in legal settings. Link: https://lizhang-aiandlaw.github.io/An-Automated-Pipeline-for-Evaluating-LLM-Generated-3-ply-Case-Bas ed-Legal-Arguments/

Related papers

• MArgE: Meshing Argumentative Evidence from Multiple Large Language Models for Justifiable Claim Verification [12.449402503089164]
  We introduce MArgE, a novel framework to provide formal structure to the evidence from each large language model.<n>We show experimentally that MArgE can significantly outperform single LLMs.
  arXiv  Detail & Related papers  (2025-08-04T16:40:02Z)
• Verifying the Verifiers: Unveiling Pitfalls and Potentials in Fact Verifiers [59.168391398830515]
  We evaluate 12 pre-trained LLMs and one specialized fact-verifier, using a collection of examples from 14 fact-checking benchmarks.<n>We highlight the importance of addressing annotation errors and ambiguity in datasets.<n> frontier LLMs with few-shot in-context examples, often overlooked in previous works, achieve top-tier performance.
  arXiv  Detail & Related papers  (2025-06-16T10:32:10Z)
• Don't Take the Premise for Granted: Evaluating the Premise Critique Ability of Large Language Models [11.379764847748378]
  Large language models (LLMs) often uncritically accept flawed or contradictory premises, leading to inefficient reasoning and unreliable outputs.<n>This emphasizes the significance of possessing the textbfPremise Critique Ability for LLMs, defined as the capacity to proactively identify and articulate errors in input premises.<n>We introduce the textbfPremise Critique Bench (PCBench), designed by incorporating four error types across three difficulty levels, paired with multi-faceted evaluation metrics.
  arXiv  Detail & Related papers  (2025-05-29T17:49:44Z)
• Unveiling the Magic of Code Reasoning through Hypothesis Decomposition and Amendment [54.62926010621013]
  We introduce a novel task, code reasoning, to provide a new perspective for the reasoning abilities of large language models.<n>We summarize three meta-benchmarks based on established forms of logical reasoning, and instantiate these into eight specific benchmark tasks.<n>We present a new pathway exploration pipeline inspired by human intricate problem-solving methods.
  arXiv  Detail & Related papers  (2025-02-17T10:39:58Z)
• Calling a Spade a Heart: Gaslighting Multimodal Large Language Models via Negation [65.92001420372007]
  This paper systematically evaluates state-of-the-art MLLMs across diverse benchmarks.<n>We introduce the first benchmark GaslightingBench, specifically designed to evaluate the vulnerability of MLLMs to negation arguments.
  arXiv  Detail & Related papers  (2025-01-31T10:37:48Z)
• Aligning Large Language Models for Faithful Integrity Against Opposing Argument [71.33552795870544]
  Large Language Models (LLMs) have demonstrated impressive capabilities in complex reasoning tasks.<n>They can be easily misled by unfaithful arguments during conversations, even when their original statements are correct.<n>We propose a novel framework, named Alignment for Faithful Integrity with Confidence Estimation.
  arXiv  Detail & Related papers  (2025-01-02T16:38:21Z)
• LLM2: Let Large Language Models Harness System 2 Reasoning [65.89293674479907]
  Large language models (LLMs) have exhibited impressive capabilities across a myriad of tasks, yet they occasionally yield undesirable outputs.<n>We introduce LLM2, a novel framework that combines an LLM with a process-based verifier.<n>LLMs2 is responsible for generating plausible candidates, while the verifier provides timely process-based feedback to distinguish desirable and undesirable outputs.
  arXiv  Detail & Related papers  (2024-12-29T06:32:36Z)
• Counterfactual Debating with Preset Stances for Hallucination Elimination of LLMs [45.38821594541265]
  Large Language Models (LLMs) excel in various natural language processing tasks but struggle with hallucination issues.<n>We propose a CounterFactual Multi-Agent Debate (CFMAD) framework to override LLMs' inherent biases for answer inspection.
  arXiv  Detail & Related papers  (2024-06-17T13:21:23Z)
• Think Twice Before Trusting: Self-Detection for Large Language Models through Comprehensive Answer Reflection [90.71323430635593]
  We propose a novel self-detection paradigm that considers the comprehensive answer space beyond LLM-generated answers. Building upon this paradigm, we introduce a two-step framework, which firstly instructs LLM to reflect and provide justifications for each candidate answer. This framework can be seamlessly integrated with existing approaches for superior self-detection.
  arXiv  Detail & Related papers  (2024-03-15T02:38:26Z)
• Assessing the Reasoning Capabilities of LLMs in the context of Evidence-based Claim Verification [22.92500697622486]
  We propose a framework designed to break down any claim paired with evidence into atomic reasoning types.<n>We use this framework to create Reasoning in Evidence-based Claim Verification (RECV), the first claim verification benchmark.<n>We evaluate three state-of-the-art proprietary LLMs under multiple prompt settings.
  arXiv  Detail & Related papers  (2024-02-16T14:52:05Z)
• ReEval: Automatic Hallucination Evaluation for Retrieval-Augmented Large Language Models via Transferable Adversarial Attacks [91.55895047448249]
  This paper presents ReEval, an LLM-based framework using prompt chaining to perturb the original evidence for generating new test cases. We implement ReEval using ChatGPT and evaluate the resulting variants of two popular open-domain QA datasets. Our generated data is human-readable and useful to trigger hallucination in large language models.
  arXiv  Detail & Related papers  (2023-10-19T06:37:32Z)

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.