arXiv AI September 7, 2026 By Qinglan Luo, S M A Nahian, John Guttag, S. Mazdak Abulnaga, Katie Matton

A Removal Based Approach to Improve LLM Faithfulness at Test-Time

The paper proposes a test‑time method to enhance the faithfulness of large language model (LLM) explanations by removing concepts not credited in the model’s explanation before re‑querying the model. This approach targets incompleteness—omissions of influential factors—rather than unsoundness, and is model‑agnostic, requiring no changes to model weights. Experiments across two datasets and multiple model families show improved faithfulness compared to standard prompting and faithfulness‑encouraging prompts.

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