Challenge: Existing benchmarks that rely on final-answer accuracy fail to capture the quality of the reasoning process.
Approach: They propose a fine-grained evaluation framework that assesses logical reasoning across three dimensions: overall accuracy, stepwise soundness, and representation-level probing.
Outcome: The proposed framework assesses logical reasoning across three dimensions: overall accuracy, stepwise soundness, and representation-level probing.

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LogicBench: Towards Systematic Evaluation of Logical Reasoning Ability of Large Language Models (2024.acl-long)

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Challenge: Existing work investigating the logical reasoning ability of large language models has focused only on a couple of inference rules of propositional and first-order logics.
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ReportLogic: Evaluating Logical Quality in Deep Research Reports (2026.acl-long)

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Challenge: Existing evaluation frameworks that evaluate large language models for Deep Research largely ignore this requirement.
Approach: They propose a benchmark that quantifies report-level logical quality through a reader-centric lens of auditability.
Outcome: The proposed model quantifies logical quality through a reader-centric lens of auditability.
Exploring Self-supervised Logic-enhanced Training for Large Language Models (2024.naacl-long)

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Challenge: Traditional attempts to enhance the logical reasoning abilities of language models often rely on supervised fine-tuning, limiting their generalization to new tasks or domains.
Approach: They propose a framework for integrating logical reasoning capabilities into LLMs and activating them via in-context learning.
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Structure Trumps Size: Rethinking Data Quality for LLM Reasoning (2025.findings-emnlp)

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Challenge: Existing methods for fine-tuning Large Language Models rely on heuristic strategies and lack systematic, quantitative frameworks for evaluating data quality.
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There’s No Such Thing as Simple Reasoning for LLMs (2025.findings-acl)

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Challenge: Existing work has focused on relatively complex “many-hop” reasoning problems.
Approach: They analyse the performance of fine-tuned LLMs on simple reasoning problems . they find the models remain highly brittle, being susceptible to seemingly innocent perturbations .
Outcome: The proposed models fail on simple reasoning problems, but are highly brittle . they are susceptible to seemingly innocent perturbations, such as adding duplicates to the set of premises and shuffling the order in which the premises are presented.
Take the essence and discard the dross: A Rethinking on Data Selection for Fine-Tuning Large Language Models (2025.naacl-long)

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Challenge: Existing studies focus on data selection but lack a clear, unified framework . variability in experimental settings complicates systematic comparisons .
Approach: They propose a three-stage scheme to standardize data selection for fine-tuning large language models . they propose unified comparison approach that incorporates ratio-based efficiency and ranking-based feasibility metrics to address inconsistencies across experiments.
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Investigating the Shortcomings of LLMs in Step-by-Step Legal Reasoning (2025.findings-naacl)

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Challenge: Reasoning abilities of LLMs have been a key focus in recent years.
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Elevating Legal LLM Responses: Harnessing Trainable Logical Structures and Semantic Knowledge with Legal Reasoning (2025.naacl-long)

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Challenge: Existing approaches to large language models focus on semantic similarity, neglecting the intricate logical structures and reasoning essential for addressing complex legal issues.
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Can LLMs Reason Like Doctors? Exploring the Limits of Large Language Models in Complex Medical Reasoning (2026.findings-eacl)

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Challenge: Large language models (LLMs) have shown remarkable progress in reasoning across multiple domains, but it remains unclear whether their abilities reflect genuine reasoning or sophisticated pattern matching.
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LLM Tropes: Revealing Fine-Grained Values and Opinions in Large Language Models (2024.findings-emnlp)

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Challenge: Existing approaches to evaluate latent values and opinions in large language models suffer from three notable shortcomings.
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