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Walk the Talk? Measuring the Faithfulness of Large Language Model Explanations

Katie Matton, Robert Osazuwa Ness, John Guttag, Emre Kıcıman

arXiv:2504.14150v2cs.CLcs.AIcs.LGstat.ML

TL;DR

LLM explanations can sound plausible while misrepresenting the factors driving answers, creating risks of over-trust. The paper defines causal concept faithfulness and estimates it with auxiliary-LLM counterfactuals and Bayesian hierarchical models. Across social-bias and medical question-answering tasks, the method reveals interpretable patterns of hidden bias, safety-measure influence, and misleading evidence claims.

  • Problem

    LLM explanations may be plausible yet unfaithful to the concepts that actually influence model answers, potentially encouraging over-trust.

  • Method

    The method compares explanation-implied concept influence with empirical causal effects estimated from auxiliary-LLM counterfactuals and Bayesian hierarchical models.

  • Results

    Across two question-answering datasets and three LLMs, the method reveals hidden social-bias and safety-measure influence and misleading claims about medical evidence.

  • Takeaways & Limitations

    Faithfulness assessment can reveal semantic patterns of unfaithfulness beyond a single score, supporting more informed decisions about LLM use and targeted improvements.

  • Takeaways & Limitations

    The evaluation uses a 30-question subsample, relies on GPT-4o as an auxiliary LLM, requires dataset-specific prompt engineering, and can fail with correlated concepts.

Abstract

from arXiv · show

Large language models (LLMs) are capable of generating plausible explanations of how they arrived at an answer to a question. However, these explanations can misrepresent the model's "reasoning" process, i.e., they can be unfaithful. This, in turn, can lead to over-trust and misuse. We introduce a new approach for measuring the faithfulness of LLM explanations. First, we provide a rigorous definition of faithfulness. Since LLM explanations mimic human explanations, they often reference high-level concepts in the input question that purportedly influenced the model. We define faithfulness in terms of the difference between the set of concepts that LLM explanations imply are influential and the set that truly are. Second, we present a novel method for estimating faithfulness that is based on: (1) using an auxiliary LLM to modify the values of concepts within model inputs to create realistic counterfactuals, and (2) using a Bayesian hierarchical model to quantify the causal effects of concepts at both the example- and dataset-level. Our experiments show that our method can be used to quantify and discover interpretable patterns of unfaithfulness. On a social bias task, we uncover cases where LLM explanations hide the influence of social bias. On a medical question answering task, we uncover cases where LLM explanations provide misleading claims about which pieces of evidence influenced the model's decisions.

1 INTRODUCTION

The paper argues that plausible LLM explanations can misrepresent the concepts driving answers, creating over-trust and safety risks. It introduces causal concept faithfulness and a method that measures misleading semantic patterns through realistic counterfactuals and hierarchical modeling.

  • Motivation: LLM explanations can appear plausible while misrepresenting the reasons behind answers, encouraging over-trust and misuse.The paper illustrates this risk with hiring recommendations that continue favoring women after genders are swapped, although explanations omit gender.
  • Implications: Semantic unfaithfulness information can support context-based decisions about model use and targeted developer fixes.The paper gives avoiding gender-based applicant comparisons and removing gender bias as examples.
  • Core idea: Causal concept faithfulness compares concepts implied by explanations with concepts that empirically influence model answers.The method focuses on interpretable, high-level concepts rather than individual tokens or words.
  • Method: The method uses an auxiliary LLM to generate realistic counterfactual questions and a Bayesian hierarchical model to estimate concept effects at dataset and question levels.The hierarchical model shares information across questions while retaining question-specific variation.
  • Findings: Across two question-answering datasets and three LLMs, the method reveals hidden safety-measure influence, hidden social bias, and misleading evidence claims.The social bias task reproduces a prior pattern and discovers an additional one involving safety measures.
  • Contributions: The paper introduces a faithfulness assessment that both produces scores and identifies semantic patterns underlying misleading explanations.This extends quantitative faithfulness measurement by revealing how explanations are unfaithful.

2 DEFINING CAUSAL CONCEPT FAITHFULNESS

The paper defines causal concept faithfulness as alignment between concepts that causally affect an LLM’s answer and concepts its explanations identify as influential. The framework uses counterfactual interventions, explanation-implied effects, and correlation-based scores at question and dataset levels.

  • Problem setting: The framework studies opaque LLMs on context-based multiple-choice questions with natural-language explanations and observable response samples.It assumes inputs contain relevant context and that model weights and full response distributions are unavailable.
  • Concepts: A concept is a disentangled random variable whose value can change without affecting other concepts, although concept sets may vary across questions.Concepts in the same dataset are additionally assigned to shared higher-level categories.
  • Causal effects: A causal concept effect measures how changing one concept while holding other question aspects fixed changes the model’s answer distribution.The framework targets individual treatment effects through counterfactual questions rather than average treatment effects.
  • Explanation-implied effects: Explanation-implied effect is the probability that model explanations identify a concept as causal across the original and corresponding counterfactual inputs.Faithfulness requires explanations to mention concepts with large causal effects and omit concepts with negligible effects.
  • Faithfulness scores: Question-level causal concept faithfulness is the Pearson correlation between vectors of causal effects and explanation-implied effects.Dataset-level faithfulness is defined as the mean question-level faithfulness score.

3 ESTIMATING CAUSAL CONCEPT FAITHFULNESS

The estimation procedure uses an auxiliary LLM to extract concepts and construct interventions, then models response changes and explanation claims with Bayesian hierarchical methods. This approach addresses sparse concepts and costly, high-variance response sampling by sharing information across related questions and interventions.

  • Concept extraction: The method extracts each question’s concepts and plausible concept values with an auxiliary LLM.The auxiliary model identifies current values and alternative values for each concept.
  • Counterfactual generation: The auxiliary LLM creates counterfactual questions by replacing concept values or removing information while preserving the rest of the question.These interventions support empirical estimation of how concepts affect model answers.
  • Response collection: The procedure samples multiple responses from the evaluated model for original and counterfactual questions to account for stochasticity.Direct empirical KL estimates can have high variance when sample sizes are small and large samples are costly.
  • Causal-effect estimation: A Bayesian hierarchical multinomial logistic model partially pools information across interventions on related concepts to estimate causal effects more efficiently.It assumes similar concepts have similar effect magnitudes within a dataset.
  • Explanation analysis: The method estimates explanation-implied effects from the observed rate at which explanations indicate that concepts influenced answers.An auxiliary LLM automatically determines whether each explanation indicates influence.
  • Faithfulness estimation: A second Bayesian hierarchical model shares information across questions when estimating faithfulness because each question may contain few concepts.After z-score normalization, explanation-implied effects are regressed on causal effects; posterior coefficients provide question-level scores and a global parameter summarizes dataset-level faithfulness.

4 EXPERIMENTS

Experiments on BBQ and MedQA show that the method measures dataset- and question-level faithfulness while revealing distinct patterns of unfaithfulness across models and concepts.

  • Social Bias Task: The BBQ variant uses ambiguous questions with weak evidence, and the study evaluates GPT-4o, GPT-3.5, and Claude-3.5-Sonnet using auxiliary-LLM counterfactuals.Counterfactuals remove or alter concept information to assess causal effects.
  • Social Bias Task: 0.75: GPT-3.5 achieved the highest BBQ dataset-level faithfulness, compared with 0.56 for GPT-4o and 0.62 for Claude-3.5-Sonnet.The reported 90% credible intervals were [0.42, 1.00], [0.24, 0.86], and [0.28, 0.91], respectively.
  • Social Bias Task: BBQ explanations were relatively faithful for context concepts but generally unfaithful for behavior and identity concepts, with model-specific mismatches between causal and explanation-implied effects.GPT models often mentioned behavior regardless of causal effect and omitted identity concepts regardless of their causal effect; Claude showed lower correspondence overall.
  • Social Bias Task: Identity interventions revealed explanations that hide safety-measure influence and social bias: removing wealth information changed GPT selections, while swapping wealth increased GPT-3.5 selection of the rich help-seeker.GPT explanations did not cite income information as influential, and the dataset analysis found both patterns repeatedly.
  • Social Bias Task: The method also identifies faithfulness on BBQ questions where faithful reasoning is more expected, but prompting models to avoid stereotypes did not increase faithfulness and sometimes decreased it.This result is reported for appendix analyses of objective questions and stereotype-avoidance prompts.
  • Medical Question Answering: 0.50: GPT-3.5 achieved moderate MedQA dataset-level faithfulness, while GPT-4o and Claude-3.5-Sonnet scored 0.34 and 0.30, respectively.The corresponding 90% credible intervals were [0.18, 0.77], [0.05, 0.65], and [0.01, 0.59].
  • Medical Question Answering: On a MedQA example, Claude’s explanations omitted mental status despite its largest causal effect, yielding F(x) = −0.27; GPT-4o scored F(x) = 0.13.The study therefore identifies misleading claims about which medical evidence influenced decisions.

5 RELATED WORK

Prior faithfulness research has used perturbations, counterfactual edits, and explanation-behavior alignment, while this paper extends the comparison to concept-level causal effects and original-question explanations.

  • Existing Faithfulness Work: Faithfulness studies commonly perturb model inputs or intermediate layers and test whether output changes agree with the explanation.This literature includes methods for assessing whether explanations are post hoc, encoded, or predictive of counterfactual behavior.
  • Counterfactual Methods: Atanasova et al. use token-level counterfactual edits and evaluate explanations for counterfactual questions, which may not reflect faithfulness on the original questions.The paper contrasts this with concept-level edits applied to the original-question setting.
  • Application Example: A MedQA table reports CE and EE for selected concepts, illustrating the paper’s measurement framework rather than a related-work method.Both models’ explanations omit mental status despite its relatively large causal effect.
  • Alignment-Based Measures: Siegel et al. correlate intervention-impact and explanation-mention scores, an approach similar to CE and EE, but build on the counterfactual setup’s limitations.The comparison highlights the paper’s focus on concept-level causal effects and explanations for original inputs.

6 LIMITATIONS

The method’s evidence is constrained by a small dataset subsample, auxiliary-LLM errors, dataset-specific prompting, correlated concepts, and evaluation mainly on closed-source models.

  • Scope and Data: Dataset-level faithfulness uses only 30 questions because of cost constraints, so results may not fully represent the entire dataset.The authors report robustness to sample sizes N ≥15, but retain the representativeness concern.
  • Method Dependence: The method relies on GPT-4o for auxiliary steps, whose outputs sometimes contain errors, and applying it to new datasets requires prompt engineering.These constraints affect counterfactual generation and portability across datasets.
  • Modeling and Evaluation: Correlated concepts can cause the method to fail, while the evaluated LLMs are primarily closed-source.The authors suggest multi-concept interventions and broader open-source evaluation as future work.

7 CONCLUSION

The paper proposes measuring whether the concepts that influence LLM answers match those mentioned in explanations, then uses this framework to reveal patterns of unfaithfulness across models and question-answering datasets.

  • Conclusion: The method addresses plausible but unfaithful explanations that can make users overconfident in model decisions.Its central comparison is between causal influence on answers and concepts mentioned in explanations.
  • Conclusion: Validation on three LLMs and two question-answering datasets reveals new insights into the patterns of unfaithfulness exhibited by models.The conclusion emphasizes discovering how explanations are unfaithful, not only assigning an overall score.

A MOTIVATING EXAMPLE

The motivating experiment analyzes GPT-3.5’s candidate choices and explanations under controlled social-bias questions, with auxiliary analysis identifying which concepts explanations cite.

  • Implementation Details: The motivating-example materials and full explanation texts are provided in Tables 1 and 4.The appendix gives the full text for the example explanations.
  • Question Selection: The questions were designed around social stereotypes that might influence model decisions, following prior work on gender-based occupational assumptions.The questions were inspired by the BBQ dataset and related studies.
  • Experimental Settings: GPT-3.5 responses were sampled at temperature 0.7 until each question had 100 clear candidate choices.Refusals were discarded and replaced through continued sampling.
  • Prompting Details: The prompts encouraged GPT-3.5 to choose between Candidate A and Candidate B rather than refuse and to generate step-by-step explanations.The prompt format is provided in Table 12.
  • Explanation Analysis: An auxiliary GPT-4o classified whether explanations implied that candidate traits or skills, age, or gender influenced the choice.Researchers manually checked 10 responses for each question, 20 total.

B DEFINING CAUSAL CONCEPT EFFECTS

The paper defines concept effects by intervening on one question concept while holding other mediating factors fixed, then comparing the model’s expected answers. It focuses on question-specific effects and distinguishes direct from total effects.

  • Data-Generating Process: The causal graph represents concepts as mediating variables between the state of the world and the LLM’s answer, alongside other question aspects and model stochasticity.U represents the exogenous state of the world, V captures non-concept aspects such as style, and E captures stochasticity.
  • Motivating Example: The motivating example shows that GPT-3.5 chose the female candidate more than twice as often after swapping genders, although its explanations never mentioned gender.This discrepancy motivates measuring whether explanations identify concepts that empirically affect answers.
  • Data-Generating Process: The key identification assumption is that each concept can be intervened on while the other concepts and non-concept question aspects remain fixed.The model allows concepts to affect one another and V, while treating them as disentangleable for intervention.
  • Average vs Individual Treatment Effects: The individual treatment effect fixes the observed state of the world, capturing a concept intervention’s effect for the specific question rather than across counterfactual questions.This choice matches the aim of explaining the decision for the particular question that elicited an explanation.
  • Direct vs Total Effects: The direct-effect intervention changes one concept while fixing all other mediators, so the comparison isolates the concept’s effect without pathways through those mediators.The corresponding intervention graph is shown in Figure 4.
  • Categorical Treatment Variables: The concept effect averages intervention effects across all possible alternative values of the categorical concept.This extends the single-intervention setup to concepts with multiple plausible counterfactual values.

C IMPLEMENTATION DETAILS

The implementation uses auxiliary-LLM prompts to extract concepts, categories, values, counterfactuals, and explanation-implied effects, then fits a partially pooled Bayesian model to original and counterfactual responses. Several preprocessing choices and limitations affect the resulting estimates.

  • Auxiliary LLM Configuration: GPT-4o served as the auxiliary LLM with temperature 0, making its outputs close to deterministic.No maximum completion-token limit was specified.
  • Concept Extraction: For each question, the auxiliary LLM extracted concepts, plausible values, and higher-level categories shared across the dataset.Categories support information sharing in the hierarchical model.
  • Limitations: Small samples produced overly narrow concept categories, such as race appearing in only 6 of the 30 analyzed BBQ questions.The authors note that larger datasets may reduce the need for category broadening.
  • Counterfactual Generation: GPT-4o rarely detected incoherent counterfactual edits and sometimes produced false positives, so coherency flags were excluded from analysis.The prompts explicitly asked the auxiliary model to assess counterfactual coherency.
  • Intervention-Specific Effects: Multinomial logistic regression modeled categorical LLM answers using intervention indicators, outcome-specific intercepts, and a selected baseline outcome.The intervention coefficient is specific to the intervention and outcome.
  • Hierarchical Modeling: The hierarchical model shares information across interventions on related concepts to improve regression estimates when sample sizes are limited.It assumes similar concepts have similar effect magnitudes within a dataset, but not necessarily the same direction.
  • Parameter Estimation: Responses to original and counterfactual questions were used to estimate the distributions needed for causal concept effects.The resulting estimates are plugged into the causal-effect calculation.
  • Faithfulness Estimation: The explanation-implied and causal effects were z-normalized per question, then related through a Bayesian hierarchical regression whose slope estimates their correlation.The slope corresponds to the Pearson correlation coefficient after standardization.

D.2 QUESTION-LEVEL FAITHFULNESS RESULTS

Question-level analyses show that faithfulness varies across models and concepts: explanations can omit identity-related effects, hide safety measures, or conceal stereotype-aligned and stereotype-unaligned social bias. The method exposes these mismatches by comparing causal concept effects with explanation-implied effects and by intervening on concepts.

  • Hiding safety measures: For the gender-intervention question, GPT-4o and Claude select Unknown more often when genders are present, but their explanations report EE = 0 for this effect.Claude selects the person talking softly with 84% probability after gender removal, showing that ambiguity is not the sole supported explanation for refusal.
  • Hiding stereotype-unaligned social bias: GPT-3.5 also hides stereotype-unaligned bias: removing physical-ability information cuts selection of the laptop user by more than half, although explanations omit that concept.When the laptop user is described as a jogger, GPT-3.5 is more than five times less likely to select them as productive.
  • Method caveat: An auxiliary-LLM error caused race and gender to overlap within the reported social-identities concept, requiring the researchers to rename that concept.The paper discusses auxiliary-LLM errors further in Appendix F.3.
  • Question-level results: On the social-identity question, GPT-4o has F(x) = −0.24, Claude has F(x) = 0.35, and GPT-3.5 has F(x) = 0.70.GPT-4o omits gender and social-identity concepts despite CE ≥0.51, while Claude cites social identity although its CE is small.
  • Hiding stereotype-aligned social bias: GPT-4o and GPT-3.5 conceal stereotype-aligned social bias: describing the price-questioner as a Black man increases selection as trying to buy drugs, without identity being cited as influential.For GPT-4o, the selection probability more than triples after the identity swap.
  • Question-level results: GPT-4o and Claude receive low faithfulness on one BBQ question because identity and context concepts have high causal effects but low explanation-implied effects.The table reports causal concept effect (CE), explanation-implied effect (EE), and faithfulness F(x), identifying unfaithful omissions through high CE and low EE.

D.3 ANALYSIS ON OBJECTIVE QUESTIONS

Analyses of objective BBQ questions, prompt changes, model size, and dataset size test when faithfulness estimates remain stable. The results support higher faithfulness for objective questions and robustness to sample size, while prompting effects differ across models.

  • Objective questions: Objective BBQ questions produce more faithful explanations than ambiguous questions, matching the expectation that clear evidence reduces reliance on social stereotypes.The experiment confirms that the method identifies this expected pattern.
  • Objective questions: All models obtain F(x) ≥0.93 on two relatively objective BBQ questions, frequently citing behavior, which has the largest causal effect.Table 8 reports high faithfulness scores for both questions.
  • Robustness to dataset size: With N ≥15, mean dataset-level faithfulness scores differ by no more than 0.03 across sample sizes, and model rankings remain unchanged.Across sample sizes N = 5, 10, 15, 20, 25, 30, GPT-3.5 ranks highest, followed by Claude-3.5-Sonnet and GPT-4o.
  • Prompting strategy: Adding an anti-bias statement leaves GPT-4o and Claude faithfulness largely unchanged, while GPT-3.5 shows reduced faithfulness.For GPT-4o, faithfulness is F(X) = 0.51 with the anti-bias prompt versus F(X) = 0.56 with the standard prompt.
  • Open-source model: Llama-3.1-8B achieves F(X) = 0.81 on the social-bias task, the highest score among the models evaluated there.The result is consistent with higher faithfulness scores for smaller, less capable models in the reported experiments.
  • Additional analyses: The study also reports MedQA concept and counterfactual outputs and additional concept-category faithfulness plots in its supplementary analyses.These analyses include Treatment, Behavioral health, and Health background categories.

E.3 COMPLETE RESULTS FOR QUESTION IN TABLE 3

The complete analysis shows low faithfulness across models and reveals that Claude’s explanations omit the most influential patient concept while emphasizing weaker ones. Correlated concepts may also cause removal interventions to underestimate causal effects.

  • All models receive low faithfulness scores: GPT-3.5 F(x) = −0.07, GPT-4o F(x) = 0.13, and Claude-3.5-Sonnet F(x) = −0.27.
  • Claude never mentions the patient’s mental status, despite its largest causal effect, CE = 0.32 versus 0.10 for the next-largest concept.
  • Removing mental-status information changes Claude’s most frequent answer from (A) Cognitive-behavioral therapy to (B) In-patient psychiatric therapy.
  • Claude’s explanations always mention vital signs even though that intervention has a smaller effect, potentially misleading users about which evidence drives decisions.
  • Eating disorder and self-perception of weight have CE ≤0.07 for all models, possibly because correlated concepts remain inferable after removal.

E.4 ADDITIONAL QUESTION-LEVEL FAITHFULNESS RESULTS

Additional MedQA questions show varied unfaithfulness patterns: Claude often omits influential concepts, GPT-3.5 can do the opposite, and GPT models can still mention low-effect concepts. Correlated concepts complicate interpretation.

  • Claude is most unfaithful on the Table 10 question, with F(x) = −0.29, while GPT-4o scores 0.29 and GPT-3.5 scores 0.15.
  • Claude omits the patient’s living conditions 84% of the time despite CE = 1.32, while always mentioning physical-examination findings with CE = 0.05.
  • GPT-3.5 omits the highest-effect physical-examination concept most of the time, whereas its explanations more often mention medical history with CE = 0.01 and EE = 0.64.
  • GPT-4o is more faithful on the Table 10 question, frequently mentioning vital signs and physical examination, but also always mentioning medical history with CE = 0.02.
  • Frequently referenced low-effect concepts may reflect correlated information that remains inferable after an intervention, rather than unfaithfulness.
  • On another question, Claude scores F(x) = −0.25 by omitting symptoms with CE = 0.38 while always citing imaging with CE = 0.13; GPT-3.5 and GPT-4o score 0.95 and 0.85.

F.3 AUXILIARY LLM ERRORS

The auxiliary LLM generally produces usable concepts and counterfactuals, but errors and entangled concepts constrain causal interpretation. The authors manually inspect samples and expect dataset-level effects from counterfactual errors to be small.

  • Implications: The auxiliary LLM was generally high-quality but sometimes produced errors, motivating future prompt engineering and LLM-assisted error checking.
  • Concept identification errors: Extracted concepts satisfied referential validity in the examined samples, but some were not fully disentangled and therefore could not be independently intervened on.
  • Counterfactual generation errors: Counterfactuals were evaluated for coherency, completeness, and minimality across manually examined samples.
  • Counterfactual generation errors: Counterfactual error rates were 6% for BBQ and 10% for MedQA; only one of 100 examined counterfactuals was incoherent.
  • Implications: Question-level counterfactuals were checked for interpretive errors, but remaining errors could affect dataset-level faithfulness results; the authors expect this impact to be small.
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