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BELL: Benchmarking the Explainability of Large Language Models

arXiv 2504.18572 ↗First posted 22 Apr 2025cs.AI cs.CLcited by 0

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Abstract (arXiv)

Large Language Models have demonstrated remarkable capabilities in natural language processing, yet their decision-making processes often lack transparency. This opaqueness raises significant concerns regarding trust, bias, and model performance. To address these issues, understanding and evaluating the interpretability of LLMs is crucial. This paper introduces a standardised benchmarking technique, Benchmarking the Explainability of Large Language Models, designed to evaluate the explainability of large language models.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 24 references that are in the map. First posted on arXiv 22 Apr 2025.

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References · 16

  1. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  3. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
  4. 2022Large Language Models are Zero-Shot Reasonerscited by 4,374
  5. 2023Code Llama: Open Foundation Models for Codecited by 2,139
  6. 2023Jailbroken: How Does LLM Safety Training Fail?cited by 1,651
  7. 2022STaR: Bootstrapping Reasoning With Reasoningcited by 940
  8. 2023Graph of Thoughts: Solving Elaborate Problems with Large Language Modelscited by 886
  9. 2023Orca: Progressive Learning from Complex Explanation Traces of GPT-4cited by 379
  10. 2023Chain-of-Verification Reduces Hallucination in Large Language Modelscited by 324
  11. 2022Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanationscited by 159
  12. 2023The Impact of Large Language Models on Scientific Discovery: a Preliminary Study using GPT-4cited by 112
4 more
  1. 2023Thread of Thought Unraveling Chaotic Contextscited by 72
  2. 2023Re-Reading Improves Reasoning in Large Language Modelscited by 37
  3. 2024Logic-of-Thought: Injecting Logic into Contexts for Full Reasoning in Large Language Modelscited by 23
  4. 2023Soft-prompt Tuning for Large Language Models to Evaluate Biascited by 7

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  1. 2025Explainability of Large Language Models: Opportunities and Challenges toward Generating Trustworthy Explanations
  2. 2024Towards Uncovering How Large Language Model Works: An Explainability Perspective
  3. 2026Towards Intrinsic Interpretability of Large Language Models:A Survey of Design Principles and Architectures
  4. 2025Domain Specific Benchmarks for Evaluating Multimodal Large Language Models
  5. 2024metabench -- A Sparse Benchmark of Reasoning and Knowledge in Large Language Models
  6. 2024Benchmarking Benchmark Leakage in Large Language Models
  7. 2024Rethinking Interpretability in the Era of Large Language Models
  8. 2025Towards Transparent AI: A Survey on Explainable Large Language Models