FaithfulPersona: Balancing Faithfulness and Personalization in Code Explanations through Self-Critique (2025.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods for generating faithful code explanations face challenges balancing faithfulness to the original code and personalization for diverse user needs. |
| Approach: | They propose a benchmark and method for generating faithful personalized code explanations using code samples and user profiles. |
| Outcome: | The proposed method achieves 3.7% improvement in Pass@5 compared to the strong baseline method, Self-Consistency, while maintaining high personalization with a 61.08% win rate in the LLM-as-a-Judge evaluation. |
Similar Papers
Faithful Serum: Mitigating the Faithfulness Gap in Textual Explanations of LLM Decisions via Attribution Guidance (2026.acl-long)
Copied to clipboard
| Challenge: | Prior work has focused on generating convincing rationales that appear to be subjectively faithful, but it remains unclear whether these explanations are epistemic faithful. |
| Approach: | They propose a method that enhances epistemic faithfulness by guiding explanation generation through attention-level interventions, informed by token-level heatmaps. |
| Outcome: | The proposed method significantly improves epistemic faithfulness across multiple models, benchmarks, and prompts. |
Are self-explanations from Large Language Models faithful? (2024.findings-acl)
Copied to clipboard
| Challenge: | Instruction-tuned Large Language Models excel at many tasks and will explain their reasoning, so-called self-explanations. |
| Approach: | They propose to employ self-consistency checks to measure faithfulness to LLMs to determine if they are model-dependent and if their reasoning is convincing and wrong. |
| Outcome: | The proposed measures show that self-explanations are explanation, model, and task-dependent and should not be trusted in general. |
Self-Critique and Refinement for Faithful Natural Language Explanations (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing work has demonstrated that Large Language Models (LLMs) can self-critique and refine their initial outputs, but this capability remains unexplored for improving explanation faithfulness. |
| Approach: | They propose a framework that enables models to improve the faithfulness of their own explanations through an iterative critique and refinement process without external supervision. |
| Outcome: | The proposed framework reduces unfaithfulness rates in three datasets and four state-of-the-art LLMs by 36% compared to 54.81% for baseline. |
FaithLM: Towards Faithful Explanations for Large Language Models (2026.eacl-long)
Copied to clipboard
Yu-Neng Chuang, Guanchu Wang, Chia-Yuan Chang, Ruixiang Tang, Shaochen Zhong, Fan Yang, Andrew Wen, Mengnan Du, Xuanting Cai, Vladimir Braverman, Xia Hu
| Challenge: | Large language models (LLMs) produce natural language explanations, but they lack faithfulness and do not reflect the evidence the model uses to decide. |
| Approach: | They propose a model-agnostic framework that evaluates and improves the faithfulness of LLM explanations without token masking or task-specific heuristics. |
| Outcome: | The proposed framework improves faithfulness of large language models without masking or heuristics. |
Towards Faithfully Interpretable NLP Systems: How Should We Define and Evaluate Faithfulness? (2020.acl-main)
Copied to clipboard
| Challenge: | Current approaches to interpretability evaluation focus on faithfulness criteria . current approaches focus on readability, plausibility and faithfulness . |
| Approach: | They argue that current binary definition of faithfulness sets unrealistic standards . they argue that a more graded definition would be of greater practical utility . |
| Outcome: | The proposed approach is based on three assumptions and lacks a graded definition of faithfulness. |
Towards Faithful Natural Language Explanations: A Study Using Activation Patching in Large Language Models (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are capable of generating persuasive Natural Language Explanations (NLEs) however, the faithfulness of these explanations should not be readily trusted at face value. |
| Approach: | They propose to use a causal mediation technique called activation patching to measure the faithfulness of an explanation towards supporting the explained answer. |
| Outcome: | The proposed metric, Causal Faithfulness, quantifies the consistency of causal attributions between explanations and the corresponding model outputs as the indicator of faithfulness. |
A Necessary Step toward Faithfulness: Measuring and Improving Consistency in Free-Text Explanations (2025.emnlp-main)
Copied to clipboard
| Challenge: | a measure of faithful free-text explanations is difficult to generate by language models and assess by humans. |
| Approach: | They propose a measure of Prediction-EXplanation consistency by extending the concept of weight of evidence. |
| Outcome: | The proposed measure improves explanation faithfulness by up to 9.7%, the authors show . they show that applying preference optimization improves the consistency of generated explanations across three model families. |
CodeArena: Evaluating and Aligning CodeLLMs on Human Preference (2025.emnlp-main)
Copied to clipboard
Jian Yang, Jiaxi Yang, Wei Zhang, Jin Ke, Yibo Miao, Lei Zhang, Liqun Yang, Zeyu Cui, Yichang Zhang, Zhoujun Li, Binyuan Hui, Junyang Lin
| Challenge: | Code large language models (codeLLMs) focus on synthesizing the correct code snippet, ignoring the alignment with human preferences. |
| Approach: | They propose a benchmark code-based on 40 categories and 44 programming languages to emulate real-world coding tasks. |
| Outcome: | The proposed benchmarks show that open-source code LLMs perform better than open-sourced ones. |
Self-Edit: Fault-Aware Code Editor for Code Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing Large language models (LLMs) have low pass rates and accuracy on competitive programming tasks. |
| Approach: | They propose a generate-and-edit approach that uses execution results of generated code from LLMs to improve code quality on competitive programming tasks. |
| Outcome: | The proposed method improves pass@1 by 89% on APPS-dev, 31% on apps-test, and 48% on HumanEval over nine popular code generation LLMs with parameter sizes ranging from 110M to 175B. |
A Causal Lens for Evaluating Faithfulness Metrics (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) offer natural language explanations as an alternative to feature attribution methods for model interpretability, but they may not reflect the model’s truereasoning faithfully. |
| Approach: | They propose a testbed framework for evaluating faithfulness metrics for natural language explanations using diagnosticity and model-editing methods. |
| Outcome: | The proposed framework evaluates faithfulness metrics for natural language explanations on four tasks including fact-checking, analogy, object counting, and multi-hop reasoning. |