The paper proposes a test‑time method to enhance the faithfulness of large language model (LLM) explanations by removing concepts that are not credited in the model’s explanation before re‑querying the model. This approach directly addresses the incompleteness dimension of unfaithful explanations, unlike prior methods that mainly target unsoundness. Experiments across two datasets, multiple model families, and two faithfulness metrics show that the method improves explanation faithfulness over standard prompting and faithfulness‑encouraging prompts, and it is model‑agnostic and requires no parameter changes.
arXiv:2610.07894v1 Announce Type: cross
Abstract: Large language models (LLMs) are expected to answer questions faithfully based on the provided context, abstaining when the context information is in...
By Zizhuo Zhang, Xiong Peng, Jingwei Sun, Rong Yao, Borui Jiang, Bo Han
arXiv:2606. 28615v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in high-stakes domains, where free-text explanations such as chain-of-thought and post-hoc rationales are used to justify model outputs.
By Nhi Nguyen, Shauli Ravfogel, Rajesh Ranganath
arXiv:2607. 21090v1 Announce Type: cross Abstract: We propose a Reinforcement Learning (RL) method to directly optimize the faithfulness of self-explanations - the extent to which a model's generated reasoning accurately reflects its internal decision-making process.
By Yeoktatt Cheah, Mar\'ia P\'erez-Ortiz, Noah Y. Siegel, Oana-Maria Camburu
The paper introduces Counterfactual Test-Time Augmentation (CTTA) to mitigate shortcut exploitation in large language models. CTTA generates counterfactual edits of an input using an auxiliary model, then averages the LLM’s predictions across these edits, with theoretically optimal weights derived from the training distribution. The authors propose Select-CTTA, which selects among CTTA variants based on tests for assumption violations, and demonstrate that it outperforms other test-time methods and rivals models fine‑tuned on shortcut‑balanced data.
By Katie Matton, Rosalind W. Picard, John Guttag, Maggie Makar
arXiv:2607. 04572v1 Announce Type: new Abstract: Large language model (LLM) tutors often produce fluent step-by-step explanations, but a correct and pedagogically formatted response does not guarantee that the answer was derived from the student-facing problem.
By Bonan Shen, Dingyan Shang, Youting Wang, Tao Ning
arXiv:2607.14109v2 Announce Type: replace
Abstract: Probing the capabilities of Large Language Models (LLMs) and building robust solutions for Multiple-Choice Question Answering (MCQA) remain central...
By Inder Preet, Shuxin Lin, Dhaval Patel
The paper introduces CoT-Interpretability Alignment (CIA), a metric that quantifies how well a large language model’s chain-of-thought (CoT) explanations match its internal reasoning processes. Evaluated on two-hop question answering, hint intervention, and integer multiplication across three LLMs, the study finds limited alignment (44.8–75.9%) and demonstrates that post‑training with a reward combining task accuracy and parametric faithfulness can substantially improve CoT faithfulness without sacrificing accuracy. The authors provide a framework for auditing CoT faithfulness and a pathway to making explicit reasoning more trustworthy, with code and data publicly available.
By Yihuai Hong, Shauli Ravfogel, Chen Zhao, Eunsol Choi
The paper investigates automated failure attribution (AFA) in large language model (LLM)-based agentic systems, where multiple agents collaborate on complex tasks. It finds that LLMs contain sufficient internal knowledge to attribute failures but often fail to output the correct step, revealing a knows‑but‑won’t‑say gap. To address this, the authors introduce grafting—a manual intervention that leverages latent model knowledge—showing it consistently outperforms direct prompting across several LLMs and benchmarks.
By Wendy Zheng, Liang Wu, Yinhan He, Mayank Darbari, Jundong Li, Liangjie Hong
arXiv:2503. 13445v3 Announce Type: replace-cross Abstract: When asked to explain their decisions, LLMs can often give explanations which sound plausible to humans.
By Noah Y. Siegel, Nicolas Heess, Maria Perez-Ortiz, Oana-Maria Camburu
The paper investigates whether the tokens highlighted by large language models (LLMs) as key to their predictions are behaviorally consistent when those tokens are manipulated. Using sufficiency, comprehensiveness, and counterfactuality metrics, the authors evaluate closed‑source GPT‑4o‑mini and open‑source models Gemma3, Granite8B, and DeepSeek on IMDB and Steam review datasets. Results show that GPT‑4o‑mini consistently follows expected patterns, Gemma3 and Granite8B maintain sufficiency but falter under stricter interventions, and DeepSeek variants exhibit structural inconsistencies, indicating that explanation reliability varies across model families and sizes.
By Alisson Rosa Pereira, Bruno Iochins Grisci
arXiv:2606. 16110v1 Announce Type: new Abstract: Machine unlearning has been extensively studied in response to growing privacy concerns and regulatory requirements.
By Dayong Ye, Tianqing Zhu, Ruiding Huang, Xinbo Fu, Jiayang Li, Bo Liu, Huan Huo, Wanlei Zhou