mlsanityData to 7 Oct 2026

DiPT: Enhancing LLM reasoning through diversified perspective-taking

arXiv 2409.06241 ↗First posted 10 Sep 2024cs.LG cs.AIcited by 4

On the map: LLM reasoning → Reasoning benchmarks and evaluation

Abstract (arXiv)

Existing work on improving language model reasoning typically explores a single solution path, which can be prone to errors. Inspired by perspective-taking in social studies, this paper introduces DiPT, a novel approach that complements current reasoning methods by explicitly incorporating diversified viewpoints. This approach allows the model to gain a deeper understanding of the problem's context and identify the most effective solution path during the inference stage. Additionally, it provides a general data-centric AI recipe for augmenting existing data to improve their quality for fine-tuning. Our empirical results demonstrate that DiPT can be flexibly integrated into existing methods that focus on a single reasoning approach, enhancing their reasoning performance and stability when presented with paraphrased problems. Furthermore, we illustrate improved context understanding by maintaining the model's safe outputs against "jailbreaking" prompts intentionally designed to bypass safeguards built into deployed models. Lastly, we show that fine-tuning with data enriched with diverse perspectives can boost the reasoning capabilities of the model compared to fine-tuning with raw data alone.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 28 of its 78 references that are in the map. First posted on arXiv 10 Sep 2024.

Builds on

28 of 78 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024xCoT: Cross-lingual Instruction Tuning for Cross-lingual Chain-of-Thought Reasoning13 Jan

Other references · 27

  1. 2023GPT-4 Technical Reportcited by 18,132
  2. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
  3. 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,203
  4. 2018Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challengecited by 4,710
  5. 2023Mistral 7Bcited by 3,065
  6. 2023Universal and Transferable Adversarial Attacks on Aligned Language Modelscited by 2,914
  7. 2023GPQA: A Graduate-Level Google-Proof Q&A Benchmarkcited by 2,748
  8. 2018Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answeringcited by 2,114
  9. 2022Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Themcited by 1,767
  10. 2023Jailbreaking Black Box Large Language Models in Twenty Queriescited by 1,326
  11. 2019DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphscited by 883
  12. 2023Graph of Thoughts: Solving Elaborate Problems with Large Language Modelscited by 873
15 more
  1. 2023Instruction Tuning with GPT-4cited by 676
  2. 2023Measuring Faithfulness in Chain-of-Thought Reasoningcited by 668
  3. 2023Instruction Tuning for Large Language Models: A Surveycited by 617
  4. 2023Baseline Defenses for Adversarial Attacks Against Aligned Language Modelscited by 608
  5. 2024LESS: Selecting Influential Data for Targeted Instruction Tuningcited by 514
  6. 2023Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruningcited by 408
  7. 2016Solving General Arithmetic Word Problemscited by 353
  8. 2023Chain-of-Verification Reduces Hallucination in Large Language Modelscited by 317
  9. 2023Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselvescited by 123
  10. 2023The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuningcited by 106
  11. 2023Efficient Online Data Mixing For Language Model Pre-Trainingcited by 77
  12. 2021It's All in the Heads: Using Attention Heads as a Baseline for Cross-Lingual Transfer in Commonsense Reasoningcited by 69
  13. 2022Crosslingual Generalization through Multitask Finetuningcited by 26
  14. 2024Walking in Others' Shoes: How Perspective-Taking Guides Large Language Models in Reducing Toxicity and Biascited by 17
  15. 2023EchoPrompt: Instructing the Model to Rephrase Queries for Improved In-context Learningcited by 13

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025Pragmatic Inference Chain (PIC) Improving LLMs' Reasoning of Authentic Implicit Toxic Languagecited by 8
  2. 2025Evaluating Large Language Models for Detecting Antisemitismcited by 3
  3. 2026Confidence-Aware Alignment Makes Reasoning LLMs More Reliablecited by 1
  4. 2026Flout at Your Own Risk: LLMs Struggle with Pragmatic Cooperativity Under Epistemic Asymmetrycited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models
  2. 2026State of Thought Enables Endogenous Reasoning
  3. 2023DetermLR: Augmenting LLM-based Logical Reasoning from Indeterminacy to Determinacy
  4. 2024Inference Scaling vs Reasoning: An Empirical Analysis of Compute-Optimal LLM Problem-Solving
  5. 2025Advancing Reasoning in Large Language Models: Promising Methods and Approaches
  6. 2024LLM Reasoners: New Evaluation, Library, and Analysis of Step-by-Step Reasoning with Large Language Models
  7. 2024textttLM^texttt2: A Simple Society of Language Models Solves Complex Reasoning
  8. 2025A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems