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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

LLMs can produce plausible explanations that misrepresent the reasons behind their answers, motivating ways to assess explanation faithfulness. This paper compares concepts cited in explanations with concepts that affect answers, using counterfactuals and a Bayesian hierarchical model; across three LLMs and two question-answering datasets, it finds hidden safety-measure influence and misleading medical-evidence claims.

  • Problem

    LLMs can generate plausible explanations that misrepresent the true reasons for their answers, making explanation faithfulness important to assess.

    Source: §1 · p. 1

  • Method

    The method compares explanation-implied concepts with answer effects under concept changes, using a Bayesian hierarchical model to estimate faithfulness across questions.

    Source: §2 · p. 3, §3 · p. 5, §7 · p. 10

  • Results

    Across three LLMs and two question-answering datasets, explanations hid safety-measure influence on a social bias task and made misleading claims about evidence in medical answers.

    Source: §1 · pp. 2–3

  • Takeaways & Limitations

    The method can assess explanation faithfulness while revealing specific patterns in how explanations mislead.

    Source: §1 · p. 2, §7 · p. 10

    Dataset-level faithfulness estimates use a cost-driven subsample of 30 questions, which may not fully represent the entire dataset.

    Source: §6 · p. 10

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 proposes causal concept faithfulness to compare concepts an explanation says influenced an answer with those that actually did, aiming to reveal interpretable patterns of unfaithfulness.

Motivation

  • In over 100 trials, GPT-3.5 preferred the woman 74% of the time before and 70% after candidates’ genders were swapped, while its explanations omitted gender.The example illustrates how plausible explanations can conceal a reason misaligned with user intent, such as avoiding gender bias in hiring.Source: §1 · p. 1, Table 1 · p. 2
  • Faithfulness measures at the question and dataset levels can guide trust and model selection, while semantic patterns can inform context-specific use and targeted improvements.The paper presents its method as the first to provide both a faithfulness score and the semantic patterns underlying it.Source: §1 · p. 2

Definition

  • Causal concept faithfulness compares concepts explanations imply are influential with those empirically influential, using high-level concepts rather than tokens to improve interpretability.The paper grounds its definition in causal inference.Source: §1 · pp. 2–3

Method

  • The method uses an auxiliary LLM to identify concepts and generate realistic counterfactual questions, then a Bayesian hierarchical model to estimate faithfulness by dataset and question.The model shares information across questions while capturing question-specific variation.Source: §1 · pp. 2–3

Results

  • On a social bias task, GPT-4o, GPT-3.5, and Claude-3.5-Sonnet hid safety measures’ influence; on medical QA, they made misleading claims about influential evidence.The method was validated on two question-answering datasets and three LLMs.Source: §1 · pp. 2–3

2 DEFINING CAUSAL CONCEPT FAITHFULNESS

Causal concept faithfulness measures how closely an LLM’s explanations’ claims about influential concepts align with those concepts’ causal effects on its answers. The paper defines this alignment per question and averages it across questions for dataset-level faithfulness.

Problem Setting

  • The framework targets opaque models accessible through input-output inference and discrete response samples, using context-based multiple-choice questions with natural-language explanations.Each response includes an answer choice and an explanation that may imply which parts of the context influenced the choice.Source: §2 · p. 3

Concepts

  • Concepts are disentangled variables that can take multiple values, and each belongs to one shared higher-level category, though question-specific concept sets may differ.The framework focuses on high-level concepts in the context rather than individual tokens or words.Source: §2 · p. 3

Causal Concept Effects

  • A concept’s causal effect is the average KL divergence between the model’s answer distributions for the original question and counterfactuals changing only that concept’s value.The definition considers individual treatment effects: how intervening on one concept changes the answer distribution while other aspects of the question remain fixed.Source: §2 · pp. 3–4

Causal Concept Faithfulness

  • Explanation-implied effect is the probability that explanations for the original question and its counterfactuals indicate that a concept affected the answer.Faithful explanations should frequently cite concepts with large causal effects and infrequently cite concepts with negligible effects.Source: §2 · p. 4
  • Question-level faithfulness is the Pearson correlation between concepts’ causal effects on answers and the effects implied by the model’s explanations.The dataset-level score is the mean of question-level faithfulness scores.Source: §2 · p. 4

3 ESTIMATING CAUSAL CONCEPT FAITHFULNESS

The method estimates causal concept effects by comparing model responses to original and LLM-generated counterfactual questions, then measures faithfulness by relating those effects to concepts identified as influential in explanations. Bayesian hierarchical models pool information across questions and related concepts to improve estimation.

Extracting Concepts and Concept Values

  • An auxiliary LLM extracts each question’s concepts, identifies their current and plausible alternative values, and assigns concepts to shared higher-level categories.Dataset-specific prompts with few-shot examples guide each step.Source: §3 · p. 4

Estimating Causal Concept Effects

  • Auxiliary-LLM counterfactuals replace concept values or obscure them; the method samples model responses to original and edited questions, then pools estimates across interventions.A Bayesian hierarchical multinomial logistic model shares information across concepts in the same category, assuming similar effect magnitudes but allowing directions to vary by question; pooling addresses noisy small-sample estimates and the cost of larger samples.Source: §3 · pp. 4–5

Estimating Explanation-Implied Effects

  • An auxiliary LLM identifies whether each explanation indicates that a concept causally influenced the model’s answer, yielding empirical explanation-implied effects.Source: §3 · p. 5

Estimating Causal Concept Faithfulness

  • To estimate faithfulness, the method z-score normalizes causal and explanation-implied effects per question, regresses the latter on the former, and hierarchically pools across questions.Posterior question-specific regression coefficients estimate question-level faithfulness; the global regression parameter estimates dataset-level faithfulness.Source: §3 · p. 5

4 EXPERIMENTS

Across BBQ social-bias questions and MedQA patient scenarios, the experiments use counterfactual concept interventions to measure explanation faithfulness. Dataset-level scores reveal differences across models, while concept-level analyses expose explanations that omit influential information or mask social bias.

4.1 SOCIAL BIAS TASK

  • On BBQ, GPT-3.5 had the highest dataset-level faithfulness (F(X) = 0.75; 90% CI = [0.42, 1.00]), versus GPT-4o at 0.56 and Claude-3.5-Sonnet at 0.62.The reported intervals were [0.24, 0.86] for GPT-4o and [0.28, 0.91] for Claude-3.5-Sonnet.Source: Figure 1 · p. 6, §4.1 · p. 6
  • On BBQ, all models aligned better on context concepts than on behavior and identity, while GPT explanations referenced behavior regardless of its causal effect and omitted identity regardless of its effect.Claude’s behavior and identity concepts showed varied causal and explanation-implied effects with low correspondence.Source: §4.1 · p. 6
  • For a BBQ example, faithfulness was lowest for GPT-4o (F(x) = −0.34), followed by Claude (0.45) and GPT-3.5 (0.72), with GPT-4o and GPT-3.5 omitting influential identity information.All three models frequently referenced behavior; it had a large causal effect for GPT-3.5 but near-zero effects for the other two models.Source: §4.1 · p. 7, Table 2 · p. 7
  • BBQ interventions revealed explanations that masked both safety-related refusal and social-bias effects, with the latter more common for GPT-3.5 and the former more pronounced for GPT-4o.Removing wealth information led both GPT models to select the man asking for help more often; swapping wealth status made them more likely to select that person when described as rich, while their explanations did not mention relative income. GPT-3.5’s probability was more than twice as high after the swap; the bias could be stereotype-aligned or not, and the authors note it may be more harmful than other unfaithfulness patterns.Source: §4.1 · pp. 7–8, Figure 2 · p. 8

4.2 MEDICAL QUESTION ANSWERING

  • On MedQA, GPT-3.5 was moderately faithful (F(X) = 0.50; 90% CI = [0.18, 0.77]), compared with GPT-4o at 0.34 and Claude-3.5-Sonnet at 0.30.The 90% CIs were [0.05, 0.65] for GPT-4o and [0.01, 0.59] for Claude-3.5-Sonnet.Source: §4.2 · p. 8, Figure 3 · p. 9
  • For a MedQA patient scenario, Claude’s explanations omitted mental status despite its largest causal effect (CE = 0.32), while GPT-4o showed a similar, weaker omission pattern.Claude’s example faithfulness was F(x) = −0.27 versus GPT-4o’s 0.13; Claude often mentioned vital signs and treatment refusal (EE ≥0.96) despite their smaller causal effects (CE ≤0.10). GPT-4o infrequently mentioned mental status (EE = 0.10), which had one of its largest causal effects (CE = 0.04).Source: §4.2 · p. 9

5 RELATED WORK

Prior faithfulness work commonly tests whether perturbations change model outputs consistently with explanations, while newer LLM studies examine explanation-specific failure modes. This work focuses on natural-language LLM explanations and realistic, concept-level counterfactuals.

Explanation Faithfulness

  • Prior faithfulness studies commonly perturb inputs or intermediate layers and check whether output changes match explanations, usually feature scores, attention maps, or extractive rationales.Common perturbations delete or randomly replace tokens or words; this work instead studies natural-language LLM explanations and uses an auxiliary LLM to generate more realistic perturbations.Source: §5 · p. 9

Faithfulness of LLM Explanations

6 LIMITATIONS

The study’s faithfulness estimates may not fully represent the dataset and depend on auxiliary-LLM quality and dataset-specific prompting. The method also struggles with correlated concepts, while the analyzed models are closed-source.

6 LIMITATIONS

  • Dataset-level faithfulness estimates use only 30 questions and may not represent the full dataset.The authors report robustness to sample sizes N ≥15, but use a 30-question subsample because of cost constraints.Source: §6 · p. 10
  • The method depends on GPT-4o outputs that can contain errors and dataset-specific prompt engineering for new datasets.The auxiliary LLM’s outputs are generally high-quality, but sometimes erroneous; prompts share a common structure while requiring some adaptation.Source: §6 · p. 10
  • The method can fail to handle correlated concepts, which the authors suggest addressing with multi-concept interventions.Source: §6 · p. 10
  • The analyzed models are closed-source, though the authors show an initial application to open-source Llama-3.1-8B and plan further analysis.Source: §6 · p. 10

7 CONCLUSION

LLM explanations can sound plausible while being unfaithful, potentially making users overconfident in model decisions. The proposed assessment compares the input concepts that most affect answers with those mentioned in explanations and was validated on three LLMs and two question-answering datasets.

7 CONCLUSION

  • The method assesses explanation faithfulness by comparing the input concepts with the greatest effect on answers to the concepts mentioned in explanations.It is designed both to measure the degree of faithfulness and to reveal how explanations are unfaithful.Source: §7 · p. 10
  • Plausible but unfaithful explanations can lead users to become overconfident in model decisions.Source: §7 · p. 10
  • The authors validated the method on three LLMs and two question-answering datasets, revealing new patterns of LLM unfaithfulness.Source: §7 · p. 10

A MOTIVATING EXAMPLE

The motivating example uses stereotype-sensitive questions with ambiguous evidence to probe unfaithful responses, then analyzes GPT-3.5 explanations for the concepts they imply influenced candidate choices.

Question Selection

  • Questions were designed to elicit unfaithful responses using social stereotypes and weak, nonconclusive evidence about candidate traits and skills.They were inspired by Turpin et al. (2023) on the BBQ dataset (Parrish et al., 2022), and prior findings on gendered occupation assumptions (Kotek et al., 2023).Source: A MOTIVATING EXAMPLE · p. 14

Experimental Settings

  • GPT-3.5 was sampled at temperature 0.7 until 100 responses with clear candidate choices were collected for each question.Refusals were discarded and replaced with further samples.Source: A MOTIVATING EXAMPLE · p. 14

Prompting Details

Explanation Analysis

  • An auxiliary GPT-4o model classified whether explanations implicated candidate traits or skills, age, and gender, and manual checks found it correct on 10 responses per question.The auxiliary model used a dataset-specific few-shot prompt; 20 responses were manually checked in total.Source: A MOTIVATING EXAMPLE · p. 14

B DEFINING CAUSAL CONCEPT EFFECTS

The section defines causal concept effects for a particular question by comparing the LLM’s answer distribution before and after changing a concept while holding other question features fixed. It uses an individual treatment effect, KL divergence for categorical answers, and averages across possible alternative concept values.

B DEFINING CAUSAL CONCEPT EFFECTS

  • The per-question DGP treats concepts and remaining question features as mediators from world state to LLM answer, allowing concept interactions while assuming concepts can be intervened on separately from other features.An unobserved variable accounts for model stochasticity.Source: B DEFINING CAUSAL CONCEPT EFFECTS · pp. 14–15, Figure 4 · p. 17
  • The method focuses on individual treatment effects, fixing the observed world state to capture a concept’s effect on the answer to the particular question being explained.This contrasts with average treatment effects across counterfactual questions generated from different world states.Source: B DEFINING CAUSAL CONCEPT EFFECTS · pp. 15–16
  • A concept’s direct effect compares expected answers before and after changing that concept, while fixing all other concepts and remaining question features.The resulting intervention corresponds to a counterfactual question that changes only the target concept.Source: B DEFINING CAUSAL CONCEPT EFFECTS · p. 16
  • Because the LLM’s answer is categorical and non-binary, the method measures the difference between answer distributions using Kullback–Leibler divergence.The definition could use other distances, such as Wasserstein, instead.Source: B DEFINING CAUSAL CONCEPT EFFECTS · p. 16
  • The causal effect of a concept averages its effects across all possible alternative values for that concept.This accommodates concepts with multiple counterfactual values, such as different candidate-gender configurations.Source: B DEFINING CAUSAL CONCEPT EFFECTS · p. 16

C IMPLEMENTATION DETAILS … D.1 AUXILIARY LLM OUTPUTS

The appendices detail how the authors generated auxiliary-LLM outputs, estimated causal concept effects and faithfulness, and collected primary-model responses. They also describe limitations and processing choices that shaped which counterfactuals and explanations entered the analyses.

C.1 AUXILIARY LLM STEPS

  • GPT-4o-2024-05-13 generated auxiliary outputs at temperature 0, and few-shot examples helped it follow the required concept-extraction response format.The authors broadened narrow concept categories to support information sharing: BBQ’s 40 categories became three, while MedQA’s 82 categories were merged into higher-level groups.Source: §C.1 · p. 17
  • Small sample size contributed to narrow categories; BBQ’s race category appeared in only 6 of 30 analyzed questions despite applying to over 800 questions overall.The authors note that category broadening may be unnecessary with larger datasets and could be automated if needed.Source: §C.1 · p. 17
  • The authors used alternative concept values only for BBQ, generated removal counterfactuals for both datasets, and did not examine replacement counterfactuals for MedQA.They excluded MedQA alternatives because plausibility was difficult to verify without domain knowledge. GPT-4o’s coherence judgments were unreliable, and counterfactual explanations were omitted from explanation-implied-effect estimates to reduce calls; removed concepts were not expected to appear in those explanations.Source: §C.1 · p. 18

C.2 ESTIMATING CAUSAL CONCEPT EFFECTS

  • A Bayesian hierarchical multinomial model estimates intervention-specific causal concept effects while pooling across same-category concepts to improve estimates with limited data.The shared zero-mean Gaussian prior allows related concepts to have similar effect magnitudes without assuming they act in the same direction.Source: §C.2 · pp. 18–19

C.3 ESTIMATING FAITHFULNESS

  • Faithfulness is estimated as the slope between per-question z-normalized causal and explanation-implied effects, with a hierarchical regression pooling information across questions.The question-level coefficient represents each question’s Pearson correlation, while the shared mean represents dataset-level faithfulness; both are reported with 90% credible intervals.Source: §C.3 · p. 20

C.4 COLLECTING PRIMARY LLM RESPONSES

  • Primary models received few-shot chain-of-thought prompts at temperature 0.7, with 50 responses collected per original and counterfactual question.BBQ yielded 14,900 responses per model and MedQA 17,200; GPT models used a 256-token limit, while Claude-3.5-Sonnet used 512 after 256-token responses were often cut off. The BBQ prompt was borrowed from Turpin et al. (2023).Source: §C.4 · pp. 20–21

D.1 AUXILIARY LLM OUTPUTS

  • For BBQ, the appendix reports the number of identified concepts and generated counterfactuals, plus random samples of concept values and removal- and replacement-based counterfactual questions.These outputs are presented in Tables 30–33.Source: §D.1 · p. 21

D.2 QUESTION-LEVEL FAITHFULNESS RESULTS

Across three question-level examples, explanations sometimes concealed concept effects on answers, while faithfulness varied sharply by model and question.

D.2 QUESTION-LEVEL FAITHFULNESS RESULTS

  • On the party question, GPT-4o and Claude concealed gender’s influence on refusals, while Claude’s and GPT-3.5’s explanations also hid opposing gender biases.Both models more often selected “Unknown” when gender was present, but never mentioned gender as influential; Claude selected the softly talking man 26% of the time, versus 5% when described as a woman. GPT-3.5 selected the woman more often than the man regardless of gender swapping. Claude selected the softly talking person with 84% probability when gender information was removed.Source: §D.2 · p. 21, Figure 5 · p. 23
  • For the office-worker question, faithfulness was F(x) = 0.84 for GPT-3.5, 0.80 for GPT-4o, and 0.82 for Claude-3.5-Sonnet.All three explanations consistently referenced behavior (EE = 1.00) and omitted identity and context (EE = 0); GPT-3.5 also omitted identity despite its relatively large causal effect (CE = 0.13).Source: §D.2 · p. 22
  • Interventions on physical-ability information revealed that GPT-3.5 favored the wheelchair user as productive, but its explanations never acknowledged that influence.GPT-3.5 selected the laptop-using wheelchair user with 22% probability and the jogger with 0.04 probability; removing ability information more than halved the former selection probability, and describing that person as a jogger made selection more than five times less likely.Source: §D.2 · p. 22
  • For the drug-stereotype question, faithfulness was F(x) = −0.24 for GPT-4o, 0.35 for Claude-3.5-Sonnet, and 0.70 for GPT-3.5.GPT-4o omitted gender and social-identity concepts despite CE ≥ 0.51 for both; GPT-3.5 omitted social identity despite CE = 0.06. Claude often cited social identity for refusing, although its effect was small because it consistently refused even without that information. An auxiliary-LLM error caused race and gender interventions to overlap in the social-identities concept.Source: §D.2 · p. 22
  • On the drug-stereotype question, GPT-4o and GPT-3.5 concealed identity’s influence on answers, including both safety-related refusals and stereotype-aligned selections.All three models selected “Can’t answer” with probability close to 90% on the original question; removing identity information raised GPT-4o’s selection of the person asking about prices from 8% to 95%, while Claude’s refusal rate stayed the same. Describing that person as a Black man rather than a White woman increased selection by GPT-4o and GPT-3.5, and both explanations hid social identity’s influence.Source: §D.2 · pp. 22–23
  • For the party-stereotype question, faithfulness was F(x) = −0.21 for GPT-4o, −0.33 for Claude-3.5-Sonnet, and 0.81 for GPT-3.5.Table 5 characterizes GPT-4o and Claude’s explanations as unfaithfully omitting identity and context concepts with high causal effects and low explanation-implied effects.Source: §D.2 · p. 21, Table 5 · p. 23

D.3 ANALYSIS ON OBJECTIVE QUESTIONS … E.2 DATASET-LEVEL FAITHFULNESS RESULTS WITH ALL CATEGORIES

The analyses test faithfulness on objective BBQ questions, robustness to sample size and prompting, an open-source model, and MedQA concepts across categories.

D.3 ANALYSIS ON OBJECTIVE QUESTIONS

  • For two objective questions, all models scored F(x) ≥0.93 and cited behavior as influential (EE = 1.00), while assigning EE = 0.00 to every other concept.Behavior had the largest causal effect for each model and question, matching the explanations’ emphasis on individuals’ behavior.Source: §D.3 · p. 25, Table 8 · p. 26

E.1 AUXILIARY LLM OUTPUTS; E.2 DATASET-LEVEL FAITHFULNESS RESULTS WITH ALL CATEGORIES

  • MedQA counterfactual questions and concept lists are reported, while Treatment effects are small across models; Behavioral health and Health background effects are low for GPT models but more variable for Claude.Across all models, the three additional categories span a range of explanation-implied effects; GPT-3.5 explanations are moderately faithful overall, while the other models are less faithful.Source: §E.2 · p. 27, §E.1 · p. 27, Figure 13 · p. 31

D.3 ANALYSIS ON OBJECTIVE QUESTIONS

  • Across five objective BBQ questions, mean faithfulness was 0.95 for GPT-3.5 (90% CI [0.72, 1.00]), GPT-4o (90% CI [0.72, 1.00]), and Claude-3.5-Sonnet (90% CI [0.70, 1.00]).These near-perfect scores align with the expectation that objective questions elicit more faithful explanations than ambiguous, bias-prone questions.Source: §D.3 · p. 25, Figure 8 · p. 29

D.4 ANALYSIS OF ROBUSTNESS TO DATASET SIZE

  • With N ≥15, mean faithfulness scores varied by no more than 0.03 across sample sizes; model rankings stayed constant, while scores rose and then plateaued.The increase was most pronounced at small N, consistent with estimates moving away from the zero-mean prior as more evidence became available.Source: §D.4 · p. 25, Figure 9 · p. 29

D.5 ANALYSIS WITH ANTI-BIAS PROMPT

  • With the anti-bias prompt, F(X) was 0.51 for GPT-4o versus 0.56 standard, 0.64 for Claude-3.5-Sonnet versus 0.62 standard, and 0.61 for GPT-3.5 versus 0.75 standard.GPT-4o and Claude results were largely unchanged, while GPT-3.5 faithfulness decreased.Source: §D.5 · p. 25, Figure 10 · p. 29
  • For GPT-3.5, anti-bias prompting reduced behavior concepts’ causal effects while their explanation-implied effects stayed high, partly because the model more often answered “undetermined.”Across prompts, Context concepts had low causal and explanation-implied effects, Identity had low explanation-implied but variable causal effects, and Behavior had high explanation-implied but variable causal effects.Source: §D.5 · p. 26, Figure 11 · p. 30

D.6 ANALYSIS ON OPEN SOURCE MODEL

  • Llama-3.1-8B had the highest social-bias-task faithfulness score among the studied models, F(X) = 0.81, compared with GPT-3.5 at F(X) = 0.75.Its explanations were relatively faithful for Context concepts but less faithful for Identity and Behavior, often citing Behavior regardless of causal effect and omitting Identity regardless of effect.Source: §D.6 · p. 27, Figure 12 · p. 30

E.3 COMPLETE RESULTS FOR QUESTION IN TABLE 3

In the Table 3 MedQA example, all three models score low on faithfulness, with Claude showing the clearest mismatch between concepts affecting decisions and concepts named in explanations.

E.3 COMPLETE RESULTS FOR QUESTION IN TABLE 3

  • Claude never mentions mental status (EE = 0), despite its largest causal effect (CE = 0.32 versus 0.10 next largest), and always mentions vital signs (EE = 1; CE = 0.07).Source: §E.3 · p. 32
  • GPT-4o also infrequently mentions mental status (EE = 0.10), despite its large causal effect, while more frequently mentioning vital signs and treatment refusal (EE ≥0.30) despite equal or lower effects.Source: §E.3 · p. 32
  • Removing mental-status information changes Claude’s preferred answer from (A) to (B), while removing vital signs raises the probability of (A) less; GPT-4o also selects (B) more often after mental-status removal.The interventions’ effects need not be incorrect, but the mismatch with Claude’s explanations could lead users to overestimate vital signs’ influence and overlook mental status.Source: §E.3 · p. 32, Figure 14 · p. 32
  • Eating-disorder and weight-self-perception concepts had CE ≤0.07 for every model; correlated concepts may blunt removal interventions, motivating future joint interventions on correlated concepts.The paper hypothesizes that models can infer a concept’s value from correlated information even when its explicit statement is removed.Source: §E.3 · pp. 32–33
  • Faithfulness was low for all models: GPT-3.5 F(x) = −0.07, GPT-4o F(x) = 0.13, and Claude-3.5-Sonnet F(x) = −0.27.These are the complete question-level results for the example question.Source: §E.3 · p. 32, Table 9 · p. 33

E.4 ADDITIONAL QUESTION-LEVEL FAITHFULNESS RESULTS … F.2 CORRELATED CONCEPTS

Additional MedQA questions show that explanations can omit concepts with large causal effects while mentioning weaker ones, although correlated concepts can complicate this interpretation. The discussion explains why dataset-level correlations may conceal question-level unfaithfulness and why single-concept interventions can fail when concepts are correlated.

E.4 ADDITIONAL QUESTION-LEVEL FAITHFULNESS RESULTS

  • On Table 10, faithfulness scores are F(x) = 0.29 (GPT-4o), 0.15 (GPT-3.5), and −0.29 (Claude-3.5-Sonnet), whose explanations often omit the strongest-effect concept.For Claude, living conditions has CE = 1.32 and EE = 0.16; the next-largest effect, medical history, has CE = 0.38 and EE = 0.64, while physical-examination findings have CE = 0.05.Source: §E.4 · p. 34
  • For GPT-3.5, physical-examination findings have the largest causal effect but are omitted a majority of the time (EE = 0.40), while medical history has CE = 0.01 and EE = 0.64.Source: §E.4 · p. 34
  • GPT-4o’s explanations are more faithful than the other models’, often mentioning vital signs (EE = 1.00) and physical-examination findings (EE = 0.92), but also medical history (CE = 0.02, EE = 1.00).Source: §E.4 · p. 34
  • Frequent references to medication history, mental status, and visit reason despite small effects may reflect correlated-concept limitations rather than unfaithfulness.Removing the phrase that the patient was brought in “because of altered mental status” may not prevent inferring the visit reason from the remaining symptoms.Source: §E.4 · p. 34
  • On Table 11, Claude-3.5-Sonnet scores F(x) = −0.25 and often omits symptoms, the largest-effect concept (CE = 0.38, EE = 0.12), while always mentioning imaging (CE = 0.13).Source: §E.4 · p. 34
  • On Table 11, GPT-3.5 and GPT-4o score F(x) = 0.95 and F(x) = 0.85; imaging has the largest causal effect and is always mentioned, while other concepts have EE ≤0.02.Source: §E.4 · p. 34

F.1 DATASET-LEVEL FAITHFULNESS DEFINITION

  • Dataset-level faithfulness uses mean question-level scores; aggregate PCC can be high even when explanations incorrectly order causal effects within questions, as in Simpson’s Paradox.Source: §F.1 · p. 34

F.2 CORRELATED CONCEPTS

  • Single-concept interventions can fail for correlated concepts because an LLM may infer a removed concept from another; replacement-based counterfactuals pose less risk.The paper proposes exploring joint interventions, such as changing both race and name, to address this limitation.Source: §F.2 · p. 35

F.3 AUXILIARY LLM ERRORS

The authors assess occasional errors in GPT-4o’s concept identification and counterfactual generation, finding that concept entanglement and counterfactual errors are limited but could affect dataset-level faithfulness estimates.

Concept Identification Errors

  • Because questions can support multiple reasonable concept sets, the authors assessed extracted concepts for referential validity and disentanglement rather than against one reference set.Disentanglement means concepts can be manipulated independently.Source: §F.3 · p. 35
  • In random samples of 15 questions per task, all extracted concepts were referentially valid, but two questions per task contained entangled concepts.The social-bias questions had 2–3 entangled concepts each; the medical QA questions had two each.Source: §F.3 · p. 35
  • Entangled concepts can create redundant but coherent counterfactuals; other disentangled concepts were unaffected, so their expected impact on faithfulness analysis is minimal.The authors note that entanglement warrants further study.Source: §F.3 · p. 35

Counterfactual Generation Errors

  • For each experiment, the authors reviewed 50 generated counterfactuals for coherence, completeness, and whether edits affected only the target concept.The review sample was random.Source: §F.3 · pp. 35–36
  • Counterfactual error rates were 6% for BBQ and 10% for MedQA; only one of 100 reviewed counterfactuals was incoherent.The authors also found a few incomplete and non-minimal edits.Source: §F.3 · p. 36
  • Errors may affect dataset-level faithfulness despite question-level checks; the authors expect small impact and propose better prompting and LLM-assisted error checking.They also expect ongoing LLM capability advances to help reduce errors.Source: §F.3 · p. 36

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