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arxiv: 2307.13702 · v1 · pith:427643RB · submitted 2023-07-17 · cs.AI · cs.CL· cs.LG

Measuring Faithfulness in Chain-of-Thought Reasoning

Tamera Lanham , Anna Chen , Ansh Radhakrishnan , Benoit Steiner , Carson Denison , Danny Hernandez , Dustin Li , Esin Durmus
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Evan Hubinger Jackson Kernion Kamil\.e Luko\v{s}i\=ut\.e Karina Nguyen Newton Cheng Nicholas Joseph Nicholas Schiefer Oliver Rausch Robin Larson Sam McCandlish Sandipan Kundu Saurav Kadavath Shannon Yang Thomas Henighan Timothy Maxwell Timothy Telleen-Lawton Tristan Hume Zac Hatfield-Dodds Jared Kaplan Jan Brauner Samuel R. Bowman Ethan Perez
This is my paper

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 reserved 2026-05-11 20:46 UTCgrok-4.3pith:427643RBrecord.jsonopen to challenge →

classification cs.AI cs.CLcs.LG
keywords chain-of-thoughtfaithfulnesslarge language modelsreasoningmodel scalinginterpretabilityinterventions
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The pith

Larger language models produce less faithful chain-of-thought reasoning on most tasks studied.

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

The paper tests whether the step-by-step reasoning that large language models generate before answering is a faithful account of how they actually decide. Researchers intervene on this reasoning by introducing errors or rephrasing it and measure whether the final answer changes. They observe that models differ widely by task in their dependence on the provided reasoning chain, and that bigger models tend to depend on it less. The gains in accuracy from using chain-of-thought do not come merely from extra computation time or from the exact words chosen. The findings imply that faithful explanations via chain-of-thought are achievable when model scale and task are selected appropriately.

Core claim

By intervening on chain-of-thought outputs through the addition of mistakes or paraphrasing, models exhibit substantial variation in how much their answers condition on the stated reasoning. Larger and more capable models produce less faithful reasoning across most tasks examined, while the performance advantage of chain-of-thought does not derive solely from added test-time compute or specific phrasing. This indicates that chain-of-thought reasoning can be faithful under carefully chosen conditions of model size and task.

What carries the argument

Controlled interventions on the chain-of-thought, such as inserting mistakes or paraphrasing the reasoning steps, which test the degree to which the final prediction depends on the content of the reasoning.

If this is right

  • CoT performance gains are not explained by test-time compute alone.
  • Faithfulness of reasoning decreases with model scale on most tasks.
  • Task choice strongly influences how much a model relies on its stated reasoning.
  • Faithful CoT is possible by selecting smaller models or suitable tasks.

Where Pith is reading between the lines

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

  • Users seeking interpretable AI outputs may prefer smaller models for tasks where faithfulness matters.
  • The intervention approach could be extended to other explanation formats beyond chain-of-thought.
  • Training objectives that reward consistency between reasoning and answer might improve faithfulness at larger scales.

Load-bearing premise

Intervening on the chain-of-thought by adding mistakes or paraphrasing it measures the model's genuine reliance on that reasoning without otherwise altering how the model processes the input.

What would settle it

A large model whose answers change reliably when critical logical errors are inserted into its chain-of-thought, across multiple tasks, would contradict the claim of decreasing faithfulness with scale.

read the original abstract

Large language models (LLMs) perform better when they produce step-by-step, "Chain-of-Thought" (CoT) reasoning before answering a question, but it is unclear if the stated reasoning is a faithful explanation of the model's actual reasoning (i.e., its process for answering the question). We investigate hypotheses for how CoT reasoning may be unfaithful, by examining how the model predictions change when we intervene on the CoT (e.g., by adding mistakes or paraphrasing it). Models show large variation across tasks in how strongly they condition on the CoT when predicting their answer, sometimes relying heavily on the CoT and other times primarily ignoring it. CoT's performance boost does not seem to come from CoT's added test-time compute alone or from information encoded via the particular phrasing of the CoT. As models become larger and more capable, they produce less faithful reasoning on most tasks we study. Overall, our results suggest that CoT can be faithful if the circumstances such as the model size and task are carefully chosen.

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 / 2 minor

Summary. The manuscript investigates the faithfulness of Chain-of-Thought (CoT) reasoning in large language models through targeted interventions on the generated reasoning steps, such as introducing mistakes or paraphrasing the CoT. The authors measure changes in model predictions to assess reliance on the CoT, finding substantial variation across tasks, that CoT benefits are not solely due to added compute or specific phrasing, and that larger models exhibit less faithful reasoning on most of the studied tasks.

Significance. If the central findings hold, this work offers a valuable empirical framework for evaluating when CoT can be considered a faithful explanation of model behavior. The observation that faithfulness tends to decrease with scale on many tasks has important implications for the use of LLMs in high-stakes reasoning applications and for the development of more interpretable AI systems. The direct-intervention design is a strength, as it avoids reliance on post-hoc explanations or parameter fitting.

major comments (2)
  1. [Methods (intervention design)] Methods section, intervention design: the assumption that adding mistakes to the CoT isolates the model's reliance on specific reasoning steps is load-bearing for the scale-related claims. However, larger models may detect and override factual inconsistencies introduced by the intervention independently of their original dependence on those steps, which could explain lower answer-change rates without implying reduced faithfulness. No explicit control experiment (e.g., adding consistent but irrelevant information) is described to separate these effects.
  2. [Results (scale analysis)] Results section, scale analysis: the claim that 'as models become larger and more capable, they produce less faithful reasoning on most tasks' relies on aggregated trends across tasks. Without per-task statistical significance tests or controls for baseline performance differences, it is unclear whether the observed decrease is robust or driven by a subset of tasks where larger models simply handle perturbations differently.
minor comments (2)
  1. [Methods] The faithfulness metric (answer-change rate under intervention) would benefit from an explicit equation or pseudocode definition in the methods to improve reproducibility.
  2. [Figures] Figure captions for the main intervention results should report the number of examples per condition and whether error bars represent standard error or confidence intervals.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback. We address each major comment below and outline planned revisions to improve clarity and rigor.

read point-by-point responses

  1. Referee: Methods section, intervention design: the assumption that adding mistakes to the CoT isolates the model's reliance on specific reasoning steps is load-bearing for the scale-related claims. However, larger models may detect and override factual inconsistencies introduced by the intervention independently of their original dependence on those steps, which could explain lower answer-change rates without implying reduced faithfulness. No explicit control experiment (e.g., adding consistent but irrelevant information) is described to separate these effects.

    Authors: We agree this potential confound merits attention. The mistake-insertion intervention is designed to test whether models condition on the specific content of the CoT steps. If larger models detect inconsistencies and answer correctly anyway, this may still reflect reduced reliance on the provided reasoning (falling back to parametric knowledge instead). To address the concern directly, we will add a control condition with consistent but irrelevant information inserted into the CoT and report results in the revision. We will also highlight that the paraphrasing intervention (which introduces no factual errors) produces qualitatively similar scale trends, providing convergent evidence. revision: partial

  2. Referee: Results section, scale analysis: the claim that 'as models become larger and more capable, they produce less faithful reasoning on most tasks' relies on aggregated trends across tasks. Without per-task statistical significance tests or controls for baseline performance differences, it is unclear whether the observed decrease is robust or driven by a subset of tasks where larger models simply handle perturbations differently.

    Authors: We appreciate the call for greater statistical rigor. The manuscript already disaggregates results by task (see Figure 3 and Appendix), with the decrease in faithfulness appearing on the majority of tasks. In the revision we will add per-task linear regressions of faithfulness metrics on model size (with p-values) and control for baseline performance differences by (a) reporting normalized answer-change rates and (b) restricting analysis to tasks where all model sizes achieve comparable accuracy. These additions will confirm the trend is not driven by a small subset of tasks. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical intervention study

full rationale

The paper conducts an empirical analysis of CoT faithfulness via direct interventions (adding mistakes, paraphrasing) and measures changes in model predictions across scales and tasks. No derivation chain, equations, fitted parameters, or self-citations are used to derive claims; results follow from observable experimental outcomes rather than any self-referential construction. The work is self-contained against external benchmarks of intervention effects and does not reduce any prediction or result to its inputs by definition.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that the chosen interventions validly probe internal model reliance without major side effects on input processing.

axioms (1)
  • domain assumption Intervening on the stated CoT by adding mistakes or paraphrasing measures the model's actual dependence on that reasoning for its answer.
    This assumption underpins the entire experimental approach described in the abstract.

pith-pipeline@v0.9.0 · 5602 in / 1198 out tokens · 56457 ms · 2026-05-11T20:46:22.158354+00:00 · methodology

discussion (0)

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Forward citations

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