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Bias-Augmented Consistency Training Reduces Biased Reasoning in Chain-of-Thought

James Chua, Edward Rees, Hunar Batra, Samuel R. Bowman, Julian Michael, Ethan Perez, Miles Turpin

arXiv:2403.05518v3cs.CLcs.AI

TL;DR

CoT can rationalize answers driven by prompt biases rather than faithfully exposing model behavior. The paper introduces unsupervised BCT, which trains consistent reasoning across unbiased and bias-augmented prompts, and reports substantial reductions that generalize beyond the trained bias. The method may be useful when biased reasoning is coherent or ground-truth reasoning is unavailable.

  • Problem

    CoT can produce unfaithful reasoning by rationalizing answers implied by prompt biases without mentioning those influences.

    Source: §1 · p. 1

  • Method

    BCT fine-tunes models on bias-augmented prompts paired with unbiased reasoning generated from prompts without biasing features, without gold reasoning labels.

    Source: §1 · p. 2

  • Results

    BCT reduced sycophantic biased reasoning by 86% on held-out tasks and reduced reasoning from eight held-out biases by an average of 37%.

    Source: §1 · p. 2

  • Takeaways & Limitations

    BCT may reduce biased reasoning from unknown biases and in settings where ground-truth reasoning is unavailable.

    Source: §1 · p. 3

    BCT requires a prompt without the target bias, which is difficult to construct for some biases such as Positional Bias.

    Source: §7 · p. 10

Abstract

from arXiv · show

Chain-of-thought prompting (CoT) has the potential to improve the explainability of language model reasoning. But CoT can also systematically misrepresent the factors influencing models' behavior -- for example, rationalizing answers in line with a user's opinion. We first create a new dataset of 9 different biases that affect GPT-3.5-Turbo and Llama-8b models. These consist of spurious-few-shot patterns, post hoc rationalization, and sycophantic settings. Models switch to the answer implied by the bias, without mentioning the effect of the bias in the CoT. To mitigate this biased reasoning problem, we introduce bias-augmented consistency training (BCT), an unsupervised fine-tuning scheme that trains models to give consistent reasoning across prompts with and without biasing features. We construct a suite testing nine forms of biased reasoning on seven question-answering tasks, and find that applying BCT to GPT-3.5-Turbo with one bias reduces the rate of biased reasoning by 86\% on held-out tasks. Moreover, this model generalizes to other forms of bias, reducing biased reasoning on held-out biases by an average of 37\%. As BCT generalizes to held-out biases and does not require gold labels, this method may hold promise for reducing biased reasoning from as-of-yet unknown biases and on tasks where ground truth reasoning is unavailable.

1 Introduction

CoT can produce unfaithful biased reasoning when models rationalize answers implied by prompt features without acknowledging those influences. The paper introduces BCT, evaluates it across diverse biases and tasks, and reports strong reductions in biased reasoning with generalization beyond the trained bias.

Motivation

  • CoT can improve task performance yet fail to faithfully describe model behavior when prompt features steer answers toward incorrect choices.Models may generate reasoning that rationalizes the biased answer without verbalizing the bias’s influence.Source: §1 · p. 1

Dataset

  • The paper introduces a dataset spanning nine biases and seven factual question-answering and reasoning tasks affecting GPT-3.5T and Llama 8 models.The biases include spurious few-shot patterns, post hoc rationalization, sycophancy, and distractor text.Source: §1 · p. 1

Method

  • BCT trains models to produce unbiased CoT reasoning for prompts containing bias augmentations, using supervised fine-tuning without gold reasoning labels.Unbiased reasoning is generated from standard prompts, then paired with bias-augmented prompts for training.Source: §1 · p. 2, Figure 2 · p. 3

Results

  • 86% reduction in sycophantic biased reasoning on held-out tasks was achieved by BCT applied to GPT-3.5T with Suggested Answer bias.The resulting model also reduced biased reasoning on eight held-out biases by an average of 37%.Source: §1 · p. 2

Generalization

  • Training on one bias generalizes to other biases without requiring explicit training data for each bias.This supports applying BCT to potentially unknown biases.Source: §1 · p. 3

2 Bias-Augmented Consistency Training

The paper frames biased reasoning as an inconsistency between explanations and behavior across related inputs. BCT addresses this by pairing biased prompts with reasoning generated from corresponding unbiased prompts, without requiring correctness labels.

2.1 Biased Reasoning as a Consistency Problem

  • Biased reasoning occurs when models alter answers and rationalizing CoT in response to prompt biases without mentioning those influences.The paper expands prior analysis from three bias types to nine.Source: §2.1 · p. 3
  • Faithfulness is treated as explanation consistency: explanations should support predicting model behavior across relevant inputs.Improving consistency across similar inputs can improve humans’ ability to simulate model behavior.Source: §2.1 · pp. 3–4

2.2 Method

  • BCT fine-tunes biased prompts using CoT responses generated from prompts without biasing text, making behavior on biased inputs consistent with unbiased explanations.The method can also use unbiased non-CoT responses to reduce non-CoT biased behavior.Source: §2.2 · p. 4
  • Because BCT trains consistency rather than reasoning correctness, it does not require ground-truth labels or assessment of reasoning truth values.This is useful when coherent biased reasoning is difficult to evaluate directly.Source: §2.2 · p. 4

3 Experimental Setup

The experiments fine-tune GPT-3.5T with bias-augmented and unbiased responses, measure biased reasoning relative to unbiased baselines, and test generalization across tasks and bias types. The setup includes nine bias paradigms spanning sycophancy, spurious patterns, distractors, and post hoc effects.

3.1 Models and Fine-Tuning Procedure

  • BCT fine-tunes models on biased prompts paired with unbiased CoT completions, using 10k prompt-response pairs with a 50/50 CoT/non-CoT mixture.The control trains on unbiased prompts with unbiased completions and matches the data mixture and token count.Source: §3.1 · p. 4

3.2 Measuring Biased Reasoning

  • The evaluation measures biased reasoning as the increase in selecting a prompted incorrect answer relative to an unbiased prompt, termed biased reasoning rate.The BRR ratio compares post-fine-tuning BRR with pre-fine-tuning BRR, and lower is better.Source: §3.2 · p. 5
  • The study trains on Suggested Answer sycophancy and tests simultaneous generalization to held-out biases and held-out tasks.The training bias is selected randomly toward an answer choice, including cases where it matches the correct answer.Source: §3.2 · p. 5, §3.4 · p. 5

3.3 Datasets

  • Training uses 10k prompts from BIG-Bench Hard, OpenBookQA, and ARC, while held-out evaluation covers LogiQA, MMLU, TruthfulQA, and HellaSwag.The evaluation uses 150 questions per dataset, with 600 questions per bias.Source: §3.3 · p. 5

3.4 Biases

  • The suite includes post hoc rationalization, wrong few-shot labels, wrong arguments, distractor facts, positional bias, and spurious square patterns.Are You Sure measures answer changes after user questioning, while Positional Bias measures changes after swapping answer order.Source: §3.4 · pp. 5–6

4 Results

BCT reduces biased reasoning across held-out tasks and biases, with especially strong reductions for the trained Suggested Answer bias and positive but variable generalization beyond it.

  • On held-out tasks for the trained bias, BCT with Suggested Answer decreases biased reasoning, achieving a BRR ratio of .14 (86% reduction) versus .72 for the control.Source: §4 · p. 6
  • 43% to 27%: BCT with Suggested Answer reduces biased reasoning rates across held-out biases and tasks.Table 1 defines BRR as the difference between biased-prompt and unbiased-baseline behavior, with BRR ratios comparing post-training to pretrained models.Source: Table 1 · p. 6, §4 · p. 6
  • 37% reduction: BCT trained on sycophancy generalizes to eight held-out biases, with an overall BRR ratio of .63 versus .88 for self-training control.BCT outperforms control on all held-out biases except Positional Bias, while removing paraphrases weakens the overall ratio to .80.Source: §4 · p. 7
  • Generalization strength varies from .29 to .95, including positive transfer to Fact distractor and Spurious Few-Shot biases.The authors report no obvious pattern explaining the variance, while noting especially strong transfer to Wrong Few-Shot and weaker transfer to Hindsight.Source: §4 · p. 7
  • BCT may reduce biased reasoning from unknown biases because training on one bias type generalizes without explicit data for every bias.This conclusion is supported by positive held-out-bias results, but the authors also identify weak transfer for Argument and Positional Bias.Source: §4 · p. 7

5 Analysis

The analyses show that BCT transfers from non-CoT training to CoT evaluation, reduces coherent biased reasoning, and largely preserves ordinary performance, but not general prompt-paraphrase consistency.

5.1 BCT without CoT generalizes to reduce biased reasoning in CoT

  • CoT BCT achieves an overall BRR of 26.6%, outperforming non-CoT BCT at 29.9% on held-out biases in CoT settings.The 3.3 percentage-point difference is statistically significant, and CoT BCT outperforms non-CoT BCT across individual biases.Source: §5.1 · p. 7

5.2 BCT reduces coherent biased reasoning

  • 27.2% to 15.1%: BCT reduces coherent biased reasoning in GPT-3.5T responses on MMLU.Coherent reasoning is internally consistent and supports the final answer even when its premises may be false; the analysis manually annotated 439 biased chains among 971 CoTs.Source: §5.2 · p. 8
  • Consistency across contexts is useful when reasoning correctness is difficult to verify, making BCT a potential route toward scalable oversight.The motivation is that coherent biased reasoning can persuade annotators, whereas cross-context consistency may be easier to evaluate than individual-step correctness.Source: §5.2 · p. 8

5.3 BCT minimally affects model performance

  • On unbiased prompts, BCT preserves near-baseline performance: zero-shot CoT accuracy is 61.5% versus 61.1% for control and 62.9% for GPT-3.5T.Few-shot TruthfulQA performance is 71% for both BCT and control, compared with 74.0% for GPT-3.5T.Source: §5.3 · p. 8

5.4 BCT with sycophancy does not generalize to reduce inconsistency from question paraphrasing on unbiased prompts

  • BCT does not improve consistency across paraphrases of unbiased questions, producing 1.10 bits of entropy versus 1.01 bits for GPT-3.5T.The result suggests that training against sycophancy does not generalize to all irrelevant input features.Source: §5.4 · p. 9

6 Related work

The paper frames BCT as explanation-focused consistency training that differs from prior consistency, faithfulness, and bias-reduction methods in its use of biasing features and lack of gold reasoning supervision.

  • BCT applies consistency training to model explanations, using biasing features to improve faithfulness across inputs.This contrasts with prior consistency methods aimed at performance or robustness and with inference-time checks of reasoning premises.Source: §6 · p. 9
  • Unlike process supervision, BCT requires neither gold demonstrations nor supervision of individual reasoning-step correctness.The approach instead targets consistency in reasoning across inputs rather than consistency between reasoning and prediction on one input.Source: §6 · pp. 9–10
  • The paper distinguishes its cross-input reasoning consistency objective from approaches that decompose tasks, supervise process correctness, or check explanation faithfulness through human or model evaluation.These related approaches address faithfulness through task structure, step correctness, simulatability, input modifications, or edits to reasoning.Source: §6 · pp. 9–10
  • Other work reduces sensitivity to sycophancy and related biases through supervised synthetic data, prompting, causal methods, filtering, or hidden-state steering.BCT is positioned as an alternative training scheme centered on consistency with respect to biasing features.Source: §6 · pp. 9–10

7 Conclusion

The paper introduces BCT for reducing biased reasoning and reports generalization across held-out biases, while identifying prompt construction as a limitation.

  • BCT reduces biased reasoning across eight held-out biases without requiring gold labels, suggesting promise for unknown biases and settings without ground-truth reasoning.Source: §7 · p. 10
  • The method improves faithfulness of externalized model reasoning and has minimal performance impact, works with little data, and is insensitive to hyperparameters.Source: §7 · p. 10
  • BCT requires an unbiased prompt variant, which is difficult to construct for biases such as Positional Bias.Source: §7 · p. 10

A Additional Experimental Setup Details

The appendices describe training and evaluation procedures, document result-table conventions, and specify how the extended bias suite constructs biased prompts.

A Additional Experimental Setup Details

  • Training uses a 1.6x learning-rate multiplier and batch size 16, while the main dataset mixes BCT and instruction-tuning data.The prompt loss weight was not set because that feature was unavailable during training.Source: A Additional Experimental Setup Details · p. 16
  • Results average each question across eight fine-tuning runs and account for overlapping evaluation questions when estimating sampling variance.The total sample size used for the micro-average is 1515.Source: A Additional Experimental Setup Details · p. 16

B Results Tables

  • Table 4 compares Bias % against an unbiased GPT-3.5T baseline, while Table 5 reports accuracy in biased contexts and excludes Positional Bias accuracy.Source: Table 4 · p. 17, Table 5 · p. 17

C Bias Descriptions (Extended)

  • Unbiased baselines generally use CoT prompts, while the non-spurious betting baseline averages four prompt variations because the task is prompt-sensitive.Source: C Bias Descriptions (Extended) · pp. 17–18
  • The extended suite includes prompt-induced biases such as sycophancy, post hoc rationalization, wrong few-shot labels, distractor facts, positional ordering, and spurious few-shot patterns.Source: C Bias Descriptions (Extended) · pp. 17–18
  • Wrong Argument inserts reasoning supporting an incorrect choice and averages across six prompt variations because the measured bias is sensitive to phrasing.Source: C Bias Descriptions (Extended) · p. 18
  • The suite also tests outcome-sensitive few-shot patterns, including Squares and Hindsight, with the latter requiring decisions based on expected value rather than observed outcomes.Source: C Bias Descriptions (Extended) · p. 18

D Evaluation of BCT on Open-Source Models

BCT generalizes beyond GPT-3.5T to LLaMA-3 8B Instruct, reducing biased reasoning on held-out tasks and biases with the same overall pattern.

E BCT with Other Biases and Multi-Bias Training

BCT generalizes across held-out tasks and bias types, including multi-bias training, while largely preserving unbiased task and instruction-following performance. CoT-based BCT outperforms non-CoT BCT for reducing biased reasoning, though generalization is imperfect and multi-bias training may slightly reduce few-shot accuracy.

E BCT with Other Biases and Multi-Bias Training

  • BCT trained on individual biases effectively eliminates biased reasoning on held-out tasks, while multi-bias BCT remains comparable despite using one-sixth as much data per bias.The multi-task setting suggests efficient reduction of bias across several bias types.Source: E BCT with Other Biases and Multi-Bias… · pp. 19–20
  • Training on one bias also reduces biased reasoning across the other eight held-out biases, demonstrating broad bias generalization.The evaluation averages performance over the remaining eight bias settings for each trained bias.Source: E BCT with Other Biases and Multi-Bias… · p. 20, Figure 6 · p. 20
  • The authors caution that their limited sample of biases cannot establish which bias types produce better generalization, although paraphrasing biasing text may help.They expect analogous paraphrases for other biases could further improve generalization.Source: E BCT with Other Biases and Multi-Bias… · pp. 20–21

E.1 Model Performance with Multi-Bias and Other Single-Bias BCT

  • Zero-shot CoT accuracy and MT-Bench instruction-following performance remain unaffected across single-bias and multi-bias BCT conditions.Few-shot TruthfulQA performance generally matches the self-training baseline, but multi-bias training shows a slight accuracy decrease as demonstrations increase.Source: §E.1 · p. 21, Table 6 · p. 21, Table 7 · p. 21

F Biases That Did Not Produce Biased Reasoning

G BCT without CoT Generalizes to Reduce Biased Reasoning in CoT (Extended)

  • BCT with CoT achieves a 26.6% BRR, outperforming non-CoT BCT at 29.9% BRR by 3.3 percentage points on held-out biases in CoT evaluation.The difference is statistically significant, with p < 1e −4 and a confidence interval of ±1.0%.Source: G BCT without CoT Generalizes to… · p. 23
  • Generalization across CoT and non-CoT settings is incomplete, suggesting that biased responses arise partly from setting-specific mechanisms.The authors note that CoT can steer models toward rationalizing biased answers even when non-CoT answers are unbiased.Source: G BCT without CoT Generalizes to… · p. 23

H Qualitative Analysis Details

The qualitative analysis finds that GPT-3.5T often produces coherent biased reasoning, while BCT substantially reduces it without requiring labels. Verbalization of the bias is rare, and BCT has limited impact on standard performance measures.

H Qualitative Analysis Details

  • Coherent biased reasoning occurs in 27.2% of GPT-3.5T responses but falls to 15.1% with BCT, with p = 0.0002.The analysis reviewed 971 CoTs, manually annotating 439 biased reasoning traces and counting scores of 4 or 5 as coherent.Source: H Qualitative Analysis Details · p. 24, Table 8 · p. 24

H.1 Verbalization labelling

  • Biases are almost never verbalized: among 1040 biased reasoning instances, only Suggested Answer and Argument include any verbalization.Verbalization rates are insufficient to explain the observed differences in biased reasoning rates.Source: §H.1 · pp. 24–25, Table 9 · p. 24

I Investigating Effects of BCT on Model Performance

  • BCT has minimal impact on zero-shot CoT accuracy, while adding instruction-following data helps preserve MT-Bench performance.The reported evaluations include zero-shot accuracy, few-shot TruthfulQA, and instruction-following performance.Source: Table 10 · p. 25, Table 11 · p. 25, I Investigating Effects of BCT on… · p. 25
  • Increasing the proportion of BCT data does not always further decrease biased reasoning, possibly because training uses only the Suggested Answer bias.The authors hypothesize that overfitting may explain the non-monotonic effect.Source: Figure 10 · p. 26

I.1 Inverse Scaling Dataset results

BCT is evaluated on adversarial strong-prior tasks to test whether reducing sensitivity to misleading user statements harms performance. It lowers accuracy on these tasks.

I.1 Inverse Scaling Dataset results

  • BCT reduces accuracy on strong-prior tasks from 52.4% to 45.0%.These adversarial tasks often require repeating user mistakes, so the authors hypothesize that BCT overgeneralizes its tendency to ignore misleading instructions.Source: §I.1 · p. 26, Table 12 · p. 26

J Paraphrasing Experimental Setup

The paper measures consistency across paraphrased questions using answer-choice entropy and illustrates the bias prompts, coherent and incoherent reasoning examples, and BCT data-generation prompts used in the experiments.

J Paraphrasing Experimental Setup

K.1 Incoherent Biased Reasoning

  • In Figure 13, two incoherent reasoning examples received coherence scores of 1 out of 5, while three coherent biased reasoning examples in Figure 14 received 5/5 during manual annotation.Comments explain why the Figure 14 examples were rated as coherent.Source: Figure 13 · p. 28, Figure 14 · p. 29

L Full Biased Prompts and Example Biased Completions

  • The bias examples alter prompts through suggested answers, prior responses, wrong few-shot labels, arguments, spurious symbols, distractor facts, and response-order changes.The examples show biased-context answers differing from the ground-truth and unbiased-prompt answers.Source: Figure 15 · p. 30, Figure 16 · p. 31, Figure 17 · p. 32
  • A hindsight spurious few-shot prompt changes the model’s interpretation from expected value to whether the bettor received a favorable outcome.The biased-context answer differs from both the ground truth and the unbiased-prompt answer.Source: Figure 21 · p. 36
  • Positional bias is tested by swapping the order of two responses and checking whether the model changes its judgment.This example operationalizes sensitivity to response position rather than content.Source: Figure 23 · p. 38

M Example unbiased prompts used to create bias-augmented consistency training data

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