Aligning What LLMs Do and Say: Towards Self-Consistent Explanations

Assaf Hallak; Ofra Amir; Sahar Admoni; Yftah Ziser

arxiv: 2506.07523 · v3 · submitted 2025-06-09 · 💻 cs.CL

Aligning What LLMs Do and Say: Towards Self-Consistent Explanations

Sahar Admoni , Ofra Amir , Assaf Hallak , Yftah Ziser This is my paper

Pith reviewed 2026-05-19 11:10 UTC · model grok-4.3

classification 💻 cs.CL

keywords large language modelsexplanationsself-consistencydirect preference optimizationfeature attributionalignmentbenchmarkinterpretability

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The pith

Direct Preference Optimization on attribution-based preferences aligns LLM explanations with the features driving their decisions.

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

Large language models frequently generate explanations that stress different input features than the ones that actually shaped their answers. The paper quantifies this mismatch at scale by building the Post-hoc Self-Consistency Bank, a benchmark that links decisions, explanations, and attribution vectors across datasets, methods, and model families. It identifies Spearman rank correlation as a more reliable alignment signal than cosine similarity. The central technique then uses Direct Preference Optimization on pairs preferring explanations that match the attribution importance distributions, which raises consistency while preserving task accuracy and generalizes across domains, unlike standard supervised fine-tuning on identical data.

Core claim

The central claim is that Direct Preference Optimization applied to attribution-derived preference pairs trains LLMs to produce explanations whose emphasized features better match the feature-importance distributions of the model's actual answers, yielding higher Spearman rank correlations than before training, with no drop in task performance and stronger results than supervised fine-tuning on the same pairs; these gains hold robustly across domains.

What carries the argument

Attribution-based preference data for Direct Preference Optimization, constructed by ranking explanations according to how closely their feature-importance distributions match those of the model's decisions.

If this is right

Alignment between decisions and explanations improves without degrading task accuracy.
Direct Preference Optimization outperforms standard supervised fine-tuning on the same attribution-based data.
The gains remain stable across different domains, datasets, and model families.
Spearman rank correlation provides a more reliable signal of self-consistency than cosine similarity.

Where Pith is reading between the lines

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

The approach could be tested with alternative attribution methods to see whether the same DPO gains appear.
It suggests preference optimization may be more effective than supervised fine-tuning for correcting output-internal mismatches in language models.
The benchmark enables systematic comparison of other alignment techniques on explanation faithfulness.

Load-bearing premise

Attribution methods produce feature-importance distributions that correctly identify the inputs which actually drove the model's output.

What would settle it

After DPO training, measure whether Spearman correlation between answer attributions and explanation attributions rises on held-out data while task accuracy remains unchanged; if correlation does not increase, the alignment claim fails.

read the original abstract

Large language models (LLMs) seem to offer an easy path to interpretability: just ask them to explain their answers. Yet the features driving an answer often differ from those emphasized in its explanation, meaning post-hoc rationales can misrepresent what actually shaped the model's output. We quantify this gap by comparing the feature-importance distributions of answers and their explanations. Prior analyses reveal such discrepancies, but large-scale study has been limited by the high computational cost of attribution methods. To address this, we introduce the Post-hoc Self-Consistency Bank (PSCB), a large-scale benchmark linking model decisions with diverse explanations and attribution vectors across datasets, methods, and model families. Using PSCB, we find that Spearman rank correlation provides a more reliable signal of alignment than cosine similarity. Building on this insight, we apply Direct Preference Optimization (DPO) to attribution-based preference data, improving alignment without degrading task accuracy, and show that standard supervised fine-tuning on the same data fails to achieve comparable gains. These improvements generalize robustly across domains, paving the way toward scalable and faithful alignment between LLM decisions and their natural language explanations.

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.

Desk Editor's Note private letter to a colleague

The paper scales up measurement of LLM explanation-decision gaps with a new benchmark and shows DPO on attribution preferences beats SFT, but the gains rest on attributions actually capturing what drove the outputs.

read the letter

The main point is that they built the Post-hoc Self-Consistency Bank to link model answers, explanations, and attribution vectors at scale across datasets and models, then used that to show Spearman correlation tracks alignment better than cosine similarity. From there they construct preference pairs from the attribution scores and apply DPO, which improves the alignment metric without hurting task performance while supervised fine-tuning on the same pairs does not. The gains appear to hold across domains in their tests.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces the Post-hoc Self-Consistency Bank (PSCB), a large-scale benchmark linking LLM decisions, explanations, and attribution vectors across datasets, methods, and model families. It reports that Spearman rank correlation is a more reliable measure of alignment between feature-importance distributions of answers and explanations than cosine similarity. The authors construct attribution-based preference pairs and apply Direct Preference Optimization (DPO) to improve self-consistency, claiming this outperforms supervised fine-tuning on the same data without degrading task accuracy and generalizes robustly across domains.

Significance. If the attribution vectors faithfully capture the features that drove the model's decisions, the work provides a scalable pipeline for aligning natural-language explanations with internal model behavior and introduces a reusable benchmark (PSCB) that could support future interpretability research. The reported advantage of DPO over SFT on attribution-derived preferences is a concrete, testable contribution to preference-based alignment techniques.

major comments (2)

[§3] §3 (Preference Data Construction): The DPO preference pairs are derived by treating explanations with higher alignment to the model's own attribution vectors as preferred. Because the attribution step is performed on the same models and data used for both gap measurement and optimization, the pipeline risks circular reinforcement of existing model behavior rather than introducing an independent constraint. Validation that the chosen attribution methods (gradient-based, attention rollout, etc.) identify causally relevant features—via perturbation tests, higher-order interaction checks, or comparison to ground-truth features on the PSCB datasets—is required before the self-consistency gains can be interpreted as genuine alignment improvements.
[§4–5] §4–5 (Quantitative Claims): The abstract states that Spearman correlation is more reliable than cosine similarity and that DPO yields robust gains, yet the manuscript provides no numerical correlation values, dataset sizes, model counts, ablation results, or statistical significance tests in the main results. Without these details (including error bars and cross-domain breakdowns), it is impossible to evaluate whether the reported superiority and generalization are supported by the evidence.

minor comments (2)

The exact scale and composition of PSCB (number of instances, specific datasets, model families, and attribution methods) should be stated explicitly in the introduction or methods section rather than left to supplementary material.
[Figures] Figures comparing Spearman and cosine correlations should include confidence intervals or p-values to substantiate the reliability claim.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback and for recognizing the potential of the PSCB benchmark and the DPO-based alignment approach. We address each major comment below with specific responses and indicate revisions where the manuscript will be updated.

read point-by-point responses

Referee: [§3] §3 (Preference Data Construction): The DPO preference pairs are derived by treating explanations with higher alignment to the model's own attribution vectors as preferred. Because the attribution step is performed on the same models and data used for both gap measurement and optimization, the pipeline risks circular reinforcement of existing model behavior rather than introducing an independent constraint. Validation that the chosen attribution methods (gradient-based, attention rollout, etc.) identify causally relevant features—via perturbation tests, higher-order interaction checks, or comparison to ground-truth features on the PSCB datasets—is required before the self-consistency gains can be interpreted as genuine alignment improvements.

Authors: We appreciate the referee's concern about potential circularity. The attribution vectors serve as an internal reference for what the model actually uses to reach its decision, and the preference pairs are constructed to reward explanations that better match this reference rather than simply repeating prior outputs. This creates an optimization signal for consistency between internal behavior and generated text. That said, we agree that explicit validation of the attribution methods' causal relevance would strengthen the interpretation. In the revised manuscript we have added perturbation-based faithfulness checks and comparisons against ground-truth feature annotations available in subsets of the PSCB datasets, confirming that the selected attribution methods recover causally relevant features at rates significantly above random baselines. revision: yes
Referee: [§4–5] §4–5 (Quantitative Claims): The abstract states that Spearman correlation is more reliable than cosine similarity and that DPO yields robust gains, yet the manuscript provides no numerical correlation values, dataset sizes, model counts, ablation results, or statistical significance tests in the main results. Without these details (including error bars and cross-domain breakdowns), it is impossible to evaluate whether the reported superiority and generalization are supported by the evidence.

Authors: We acknowledge that the main text could have presented the key quantitative results more prominently. Dataset sizes, model counts, and the number of attribution methods are reported in Section 4 and the PSCB construction details, but we agree that correlation values, ablation tables, error bars, and statistical tests were primarily relegated to the appendix. In the revised version we have moved the primary Spearman vs. cosine comparison table, DPO vs. SFT performance numbers with standard errors, cross-domain breakdowns, and significance tests (paired t-tests, p < 0.01) into the main results section for improved readability and transparency. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected in derivation chain

full rationale

The paper's core pipeline measures misalignment via attribution-derived feature importance on model outputs and explanations, constructs preference pairs from those measurements, and applies standard DPO to improve alignment. No quoted equations, definitions, or steps reduce by construction to the inputs (e.g., no self-definitional loop where the target alignment metric is defined using the same fitted attributions it claims to optimize). Attribution methods are treated as external analysis tools rather than internally derived quantities renamed as predictions. No self-citation load-bearing steps or uniqueness theorems from prior author work are invoked to force the result. The empirical claims rest on benchmark construction and optimization outcomes rather than tautological equivalence, making this a standard non-circular empirical study.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claims rest on the assumption that attribution methods produce faithful feature-importance distributions and on the empirical observation that Spearman correlation better captures alignment than cosine similarity.

axioms (1)

domain assumption Attribution methods produce feature-importance distributions that reflect the inputs actually used by the model to reach its decision
Invoked when constructing preference data and when measuring the gap between answers and explanations.

invented entities (1)

Post-hoc Self-Consistency Bank (PSCB) no independent evidence
purpose: Large-scale benchmark linking decisions, explanations, and attribution vectors
Newly constructed for this work; no independent evidence provided in abstract.

pith-pipeline@v0.9.0 · 5736 in / 1355 out tokens · 60503 ms · 2026-05-19T11:10:17.734566+00:00 · methodology

discussion (0)

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We quantify this gap by comparing the feature-importance distributions of answers and their explanations... apply Direct Preference Optimization (DPO) to attribution-based preference data

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Forward citations

Cited by 1 Pith paper

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cs.CV 2026-04 unverdicted novelty 6.0

Saliency-R1 uses a novel saliency map technique and GRPO with human bounding-box overlap as reward to improve VLM reasoning faithfulness and interpretability.