mlsanityData to 7 Oct 2026

Mitigating Misleading Chain-of-Thought Reasoning with Selective Filtering

arXiv 2403.19167 ↗First posted 28 Mar 2024cs.CL cs.AIcited by 9

On the map: LLM reasoning → Prompting and chain-of-thought reasoning

Abstract (arXiv)

Large language models have manifested remarkable capabilities by leveraging chain-of-thought (CoT) reasoning techniques to solve intricate questions through step-by-step reasoning chains. Despite its success, the efficacy of such reasoning is inherently contingent upon the quality of CoT. However, flawless CoT reasoning cannot be guaranteed due to the presence of indecomposable questions and the potential for erroneous reasoning chains, particularly in the case of small-scale language models. To tackle this challenge, we propose a novel approach called the selective filtering reasoner (SelF-Reasoner) that assesses the entailment relationship between the question and the candidate reasoning chain. Then, we proceed with CoT reasoning when the reasoning chain demonstrates confidence; otherwise, we opt to predict the answer directly. SelF-Reasoner improves the fine-tuned T5 baseline consistently over the ScienceQA, ECQA, and LastLetter tasks. Code is available at texttthttps://github.com/LibroWu/SelF-Reasoner.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Placed in Prompting and chain-of-thought reasoning, one of 17 directions in LLM reasoning. Builds on 27 of its 38 references that are in the map. First posted on arXiv 28 Mar 2024.

Builds on

27 of 38 references.

Closest ancestors · 12

Same direction, newest first

  1. 2023Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting7 May
  2. 2023Verify-and-Edit: A Knowledge-Enhanced Chain-of-Thought Framework5 May
  3. 2023Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models23 Apr
  4. 2023Active Prompting with Chain-of-Thought for Large Language Models23 Feb
  5. 2022Large Language Models Are Reasoning Teachers20 Dec
  6. 2022Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters20 Dec
  7. 2022Teaching Small Language Models to Reason16 Dec
  8. 2022Automatic Chain of Thought Prompting in Large Language Models7 Oct
  9. 2022Large Language Models are Zero-Shot Reasoners24 May
  10. 2022Least-to-Most Prompting Enables Complex Reasoning in Large Language Models21 May
  11. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Models21 Mar
  12. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Models28 Jan

Other references · 15

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  3. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
  4. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  5. 2023Let's Verify Step by Stepcited by 3,598
  6. 2022Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answeringcited by 1,881
  7. 2022LaMDA: Language Models for Dialog Applicationscited by 1,218
  8. 2021Scaling Language Models: Methods, Analysis & Insights from Training Gophercited by 1,199
  9. 2016ConceptNet 5.5: An Open Multilingual Graph of General Knowledgecited by 1,065
  10. 2023Multimodal Chain-of-Thought Reasoning in Language Modelscited by 740
  11. 2023Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizescited by 710
  12. 2020UnifiedQA: Crossing Format Boundaries With a Single QA Systemcited by 475
3 more
  1. 2023Chameleon: Plug-and-Play Compositional Reasoning with Large Language Modelscited by 352
  2. 2022Explanations from Large Language Models Make Small Reasoners Bettercited by 123
  3. 2022PINTO: Faithful Language Reasoning Using Prompt-Generated Rationalescited by 56

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 5; 2026: 4.

9 papers.

  1. 2025Evaluating Step-by-step Reasoning Traces: A Surveycited by 62
  2. 2025Task Matters: Knowledge Requirements Shape LLM Responses to Context-Memory Conflictcited by 15
  3. 2025Learning to Rank Chain-of-Thought: Using a Small Modelcited by 5
  4. 2025Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decompositioncited by 3
  5. 2025The Imitation Game: Turing Machine Imitator is Length Generalizable Reasonercited by 3
  6. 2026TDA-RC: Task-Driven Alignment for Knowledge-Based Reasoning Chains in Large Language Modelscited by 2
  7. 2026AgentSim: A Platform for Verifiable Agent-Trace Simulationcited by 1
  8. 2026Can LLM Coding Agents Reason About Time Series?cited by 1
  9. 2026HANIA: Planner-Guided Multimodal Graph Evidence Selection for Grounded Question Answeringcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026OS-Pruner: Pruning Chains-of-Thought of Reasoning Models via Optimal Stopping
  2. 2024A Theoretical Understanding of Chain-of-Thought: Coherent Reasoning and Error-Aware Demonstration
  3. 2024A Hopfieldian View-based Interpretation for Chain-of-Thought Reasoning
  4. 2025Early Stopping Chain-of-thoughts in Large Language Models
  5. 2025Reasoning Beyond Language: A Comprehensive Survey on Latent Chain-of-Thought Reasoning
  6. 2026Revisiting Chain-of-Thought Reasoning under Limited Supervision: Semi-supervised Chain-of-Thought Learning
  7. 2025Reasoning Efficiently Through Adaptive Chain-of-Thought Compression: A Self-Optimizing Framework
  8. 2024A Chain-of-Thought Is as Strong as Its Weakest Link: A Benchmark for Verifiers of Reasoning Chains