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arxiv: 2604.14325 · v1 · submitted 2026-04-15 · 💻 cs.CL · cs.AI

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Faithfulness Serum: Mitigating the Faithfulness Gap in Textual Explanations of LLM Decisions via Attribution Guidance

Bar Alon , Itamar Zimerman , Lior Wolf
Authors on Pith no claims yet

Pith reviewed 2026-05-10 13:43 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords epistemic faithfulnessLLM explanationsattention interventionsattribution methodscounterfactual evaluationtextual rationalespost-hoc interpretabilityexplainability
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The pith

Guiding explanation generation with attribution-based attention interventions improves epistemic faithfulness of LLM rationales.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Large language models produce natural language explanations for their decisions, yet these often fail to reflect the actual internal evidence used by the model. Counterfactual tests in the paper reveal that standard explanations are frequently unfaithful. The authors introduce a training-free technique that extracts token attributions and uses the resulting heatmaps to intervene on attention patterns while the model generates its explanation. This steers the output toward greater alignment with the model's true decision process and delivers measurable gains across models, benchmarks, and prompts.

Core claim

LLM explanations are often unfaithful to the model's internal evidence as measured by counterfactuals, but this gap shrinks when token-level attribution heatmaps guide attention interventions during the generation of the explanation text itself.

What carries the argument

Attribution-guided attention intervention: token heatmaps from a faithful attribution method direct attention adjustments while the LLM produces its natural-language rationale.

If this is right

  • Explanations become more reliable indicators of the specific evidence driving each model decision.
  • The method works on frozen models, so it can be applied to any existing LLM without retraining.
  • Gains appear consistently across different model sizes, datasets, and prompt styles.
  • Better faithfulness supports safer deployment in settings that require verifiable reasoning.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same attention-control idea could be tested on chain-of-thought traces or other multi-step generations to check whether faithfulness improves there too.
  • Attention interventions might serve as a general dial for aligning generated text with internal model states in other interpretability tasks.
  • Pairing the method with human judgments of explanation quality would test whether automated faithfulness scores translate to perceived trustworthiness.

Load-bearing premise

The chosen attribution method must yield heatmaps that accurately identify the tokens the model actually used, and the attention interventions must preserve the original decision without creating new artifacts.

What would settle it

Generate the improved explanations, then alter only the high-attribution tokens in the input; if the model decision flips but the explanations stay largely unchanged and fail to reference the altered evidence, the faithfulness gain does not hold.

Figures

Figures reproduced from arXiv: 2604.14325 by Bar Alon, Itamar Zimerman, Lior Wolf.

Figure 1
Figure 1. Figure 1: Faithfulness Serum. A hidden hint (professor indication) creates a counterfactual scenario where the model’s answer changes. Without Faithfulness Serum, the explanation is epistemically unfaithful, ignoring the hint. With Faithfulness Serum, the explanation becomes faithful, attributing the answer to the injected hint. 2020; Das and Rad, 2020), limiting their adop￾tion in domains that require transparency … view at source ↗
Figure 2
Figure 2. Figure 2: Faithfulness Serum. To nudge the model toward generating faithful explanations, we compute the question (Q1, .., QT ) token-level relevance scores R using LRP (blue), scale them by an intervention strength α (purple), and inject them into the attention scores of the question (A1, .., AT ) at specific layers. model and then encourages attention to focus on those same tokens when generating its explanation. … view at source ↗
Figure 3
Figure 3. Figure 3: Distribution of logits for the chosen answer [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Intervention scaling factor selection for Llama [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: The effect of layer choice on intervention ef [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Intervention scaling factor selection for Qwen [PITH_FULL_IMAGE:figures/full_fig_p022_6.png] view at source ↗
read the original abstract

Large language models (LLMs) achieve strong performance and have revolutionized NLP, but their lack of explainability keeps them treated as black boxes, limiting their use in domains that demand transparency and trust. A promising direction to address this issue is post-hoc text-based explanations, which aim to explain model decisions in natural language. Prior work has focused on generating convincing rationales that appear to be subjectively faithful, but it remains unclear whether these explanations are epistemically faithful, whether they reflect the internal evidence the model actually relied on for its decision. In this paper, we first assess the epistemic faithfulness of LLM-generated explanations via counterfactuals and show that they are often unfaithful. We then introduce a training-free method that enhances faithfulness by guiding explanation generation through attention-level interventions, informed by token-level heatmaps extracted via a faithful attribution method. This method significantly improves epistemic faithfulness across multiple models, benchmarks, and prompts.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper claims that post-hoc textual explanations from LLMs are often epistemically unfaithful to the model's internal decision process, as demonstrated via counterfactual assessments. It introduces a training-free method ('Faithfulness Serum') that extracts token-level attribution heatmaps using an external faithful attribution technique and applies attention-level interventions during explanation generation to close the faithfulness gap, reporting significant improvements across multiple models, benchmarks, and prompts.

Significance. If the empirical results and causal validity of the attribution step hold, this would be a meaningful contribution to LLM explainability research by providing a practical, non-training-based intervention that directly targets the mismatch between generated rationales and model evidence, potentially improving trustworthiness in applications requiring transparency.

major comments (2)
  1. Abstract: the central claim of 'significant improvement' in epistemic faithfulness is not supported by any quantitative results, metrics, baseline comparisons, or details on counterfactual construction in the provided text, preventing assessment of effect sizes or robustness.
  2. Method section (attribution guidance): the approach assumes without validation that the chosen attribution method produces causally faithful token heatmaps (i.e., high-attribution tokens are those whose removal most affects the original prediction); no ablation or intervention experiments are described to confirm this on the target models and tasks, which is load-bearing because unfaithful heatmaps would cause the attention interventions to steer explanations toward a different but still unfaithful pattern.
minor comments (1)
  1. Abstract: expand to include at least one key faithfulness metric and the specific attribution method used, to allow readers to gauge the scope of the claimed gains.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed review and the recommendation for major revision. We address each major comment point by point below, providing clarifications from the full manuscript and outlining planned revisions to improve clarity and rigor.

read point-by-point responses

  1. Referee: Abstract: the central claim of 'significant improvement' in epistemic faithfulness is not supported by any quantitative results, metrics, baseline comparisons, or details on counterfactual construction in the provided text, preventing assessment of effect sizes or robustness.

    Authors: We agree that the abstract, being a concise summary, does not embed the specific quantitative results or methodological details. The full manuscript reports these in the Experiments section, including faithfulness metrics (e.g., counterfactual consistency scores), baseline comparisons (e.g., standard prompting and other explanation methods), effect sizes across models and benchmarks, and the counterfactual construction protocol. To enable standalone assessment, we will revise the abstract to incorporate key quantitative highlights and a brief description of the evaluation approach. revision: yes

  2. Referee: Method section (attribution guidance): the approach assumes without validation that the chosen attribution method produces causally faithful token heatmaps (i.e., high-attribution tokens are those whose removal most affects the original prediction); no ablation or intervention experiments are described to confirm this on the target models and tasks, which is load-bearing because unfaithful heatmaps would cause the attention interventions to steer explanations toward a different but still unfaithful pattern.

    Authors: We acknowledge the importance of explicitly validating the causal faithfulness of the attribution heatmaps on the specific models and tasks. The manuscript employs an external attribution technique drawn from established methods previously shown to correlate with causal impact (e.g., via token removal or gradient-based approaches). However, dedicated ablation studies confirming this on the target LLMs were not included in the initial version. We will add a validation subsection with intervention experiments demonstrating that high-attribution tokens, when perturbed, produce measurable changes in the original predictions, thereby supporting their use for attention guidance. revision: yes

Circularity Check

0 steps flagged

No circularity: derivation relies on external attribution and counterfactual checks

full rationale

The paper's core chain—assessing epistemic faithfulness via counterfactuals, then applying attention interventions guided by token heatmaps from an external attribution method—is self-contained and does not reduce to self-definition, fitted inputs renamed as predictions, or load-bearing self-citations. No equations or steps equate outputs to inputs by construction. The attribution step is treated as given from prior work without the paper re-deriving or fitting it circularly. This matches the default expectation of non-circularity for papers using established external techniques.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Review limited to abstract; the approach assumes prior attribution methods are faithful and that counterfactuals validly probe internal model evidence.

axioms (1)
  • domain assumption Counterfactual input changes can accurately reveal whether an explanation reflects the model's actual decision evidence.
    The paper uses counterfactuals as the primary assessment tool for epistemic faithfulness.

pith-pipeline@v0.9.0 · 5461 in / 1081 out tokens · 61957 ms · 2026-05-10T13:43:12.163851+00:00 · methodology

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Reference graph

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