A Removal Based Approach to Improve LLM Faithfulness at Test-Time
Qinglan Luo, S M A Nahian, John Guttag, S. Mazdak Abulnaga, Katie Matton
cs.AI · cs.CL
Quick verdict
Test-time wrapper removes uncredited input concepts to fix incomplete LLM explanations; strong on hinted QA, modest on BBQ, and answer accuracy is unexamined.
Summary
The paper introduces the 'removal wrapper,' a test-time method for improving the faithfulness of LLM explanations. It starts from the observation that unfaithful explanations come in two forms: incompleteness (the explanation omits factors that influenced the answer) and unsoundness (it cites factors that did not). Unlike most test-time work that targets unsoundness, the wrapper attacks incompleteness: an auxiliary LLM extracts concepts from the question, determines which ones the explanation credits, deletes the uncredited concepts from the input, and re-queries the target model in a fresh context, so unmentioned 'hidden' factors can no longer influence the returned answer. On hint-augmented multiple-choice QA with Qwen3.5-9B and Gemma-4-12B, causal concept faithfulness rises from 0.274 to 0.873 and from 0.335 to 0.924 respectively, and normalized simulatability gain turns from negative to positive; hint verbalization reaches 1.0 but is guaranteed by construction. On a 100-item BBQ sample with three target models, gains are smaller: removal alone changes little on the Qwen models, and the best results usually come from combining removal with modular prompting. The authors honestly analyze when the method fails (inconsistent crediting, as on Qwen3.5-9B). Main limitations: no evaluation of answer accuracy or utility, reliance on a correct auxiliary model, and no significance testing behind overlapping confidence intervals.
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Assessment at a glance
5
risk flags identified
● high 1
● medium 3
● low 1
5 strengths · 5 weaknesses · 5 author questions
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Key claims under review
- LLM explanation unfaithfulness decomposes into incompleteness (omitted influential factors) and unsoundness (cited non-influential factors), and existing test-time methods address mostly the latter.
- The removal wrapper — extract concepts, identify the explanation's credited set, delete uncredited concepts, re-query in a fresh context — provably prevents uncredited concepts from influencing the returned answer, given correct auxiliary outputs and consistent crediting (Section 2, Appendix B.1).
- On hint-augmented QA, the wrapper raises causal concept faithfulness from 0.274 to 0.873 (Qwen3.5-9B) and from 0.335 to 0.924 (Gemma-4-12B), beating standard and faithfulness-encouraging prompting on all three metrics.
- On BBQ, the Combined removal+modular-prompting method is best or near-best on both metrics across three target models, although removal alone clearly helps only Gemma-4-12B.
- Method effectiveness depends on the target model's credit consistency across repeated queries, which quantitatively explains its failure on Qwen3.5-9B (Appendix E).
Strengths
Crisp problem decomposition with a precise formal target
Section 2 formalizes completeness vs soundness via a credit set cr(E) ⊆ C(x) and 'hidden concepts', so the method has an exact goal (uncredited concepts must have zero causal effect) that prior test-time work — Factored Decomposition and Formal Symbolic Reasoning — does not enforce, as Section 1 argues.
Multi-metric, multi-dataset evaluation with real baselines
Three independent metrics (hint verbalization, causal concept faithfulness reworked with TVD, NSG) across hint-augmented QA (Table 1) and BBQ (Table 2), three target models, a re-implemented modular-prompting baseline (Appendix C.3), and a Combined removal+modular variant.
Unusually honest failure and tautology analysis
Strong in-text reproducibility package
Careful metric plumbing
Weaknesses
No answer-accuracy or utility evaluation
Tautological headline metric
Abstract overclaims relative to Table 2
Thin statistical treatment
Auxiliary-target circularity unexamined
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Scope & Claims
This preprint (arXiv 2609.04343) targets **incompleteness** — explanations that omit factors which causally influenced the answer ('hidden concepts') — as distinct from **unsoundness** (citing non-influential factors). Section 2 formalizes this with a credit set cr(E) ⊆ C(x): an explanation is complete when no uncredited concept causally affects the answer. The central claims: (1) a four-step 'removal wrapper' (concept identification → attribution → removal → re-query in a fresh context) prevents uncredited concepts from influencing the returned answer, conditional on a correct auxiliary model and consistent crediting; (2) it beats standard prompting and faithfulness-encouraging prompting on two benchmarks, three target models, and three metrics; (3) it composes with modular prompting for further gains.
The remaining sections cover Methodology Assessment, Statistical Evidence, Results & Reproducibility, Limitations & Risks, and Verdict & Recommendations.
- All 5 strengths & 5 weaknesses, analyzed in detail
- 5 risk flags with severity, section & reviewer questions
- Methods & statistical evidence assessment
- 5 questions a peer reviewer would ask
- Full six-dimension evaluation report & recommendations
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Frequently asked questions
What is the removal wrapper for LLM faithfulness?
It is a test-time technique: an auxiliary LLM lists the concepts in the question, checks which of them the model's explanation actually credits, deletes the uncredited concepts from the input, and re-asks the model in a fresh context. The returned answer plus explanation then cannot have been influenced by factors the explanation never mentioned, which fixes 'incomplete' explanations without touching model weights.
How well does the removal wrapper work?
On hint-augmented multiple-choice QA it roughly triples causal concept faithfulness (0.274 to 0.873 on Qwen3.5-9B; 0.335 to 0.924 on Gemma-4-12B) and turns the simulatability metric from negative to positive. On a 100-question BBQ sample gains are smaller: removal alone changes little on Qwen models, and the best results usually come from combining removal with modular prompting.
Does the method fix all unfaithful LLM explanations?
No. It targets only incompleteness (influences the explanation omits). It does nothing for unsoundness (citing factors that did not matter), and it assumes the auxiliary model extracts and credits concepts correctly and that the target model credits concepts consistently across repeated queries.
What models and datasets were used in the paper?
Target models: Qwen3.5-9B, Qwen3.5-27B and Gemma-4-12B-it, with Qwen3.5-27B as the auxiliary. Datasets: 2,000 hint-augmented multiple-choice questions, and a Turpin et al. variant of the BBQ bias benchmark, sampled to 100 items and rewritten with neutral Person 1/2 labels.
About this review
This is an AI-generated preliminary review by Evolve.Pub, produced from the openly available text of the paper. It is not peer review and does not represent the authors' views. The canonical record of the paper is at the publisher link above.
- The scatter-plot figures (Figure 4 and Figures 6-8) are represented only by captions in the extracted text; the per-concept points behind the reported correlations and hidden/unsound counts could not be inspected directly.
- The verbalization metric's normalization factor α is deferred to Appendix B.1 in the main text, but no numeric value or formula for α appears anywhere in the provided text.
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