| Challenge: | Multi-step reasoning through in-context learning strategies have been extensively explored, highlighting the abilities of Large Language Models (LLMs) to solve problems in a step-wise manner. |
| Approach: | They propose to use Large Language Models to generate answers from step-by-step reasoning by re-constructing the original question that led to the final answer. |
| Outcome: | The proposed models show that they are able to reason about the conclusion and reconstruct the original question that led to the final answer. |
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Reverse Thinking Makes LLMs Stronger Reasoners (2025.naacl-long)
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Justin Chen, Zifeng Wang, Hamid Palangi, Rujun Han, Sayna Ebrahimi, Long Le, Vincent Perot, Swaroop Mishra, Mohit Bansal, Chen-Yu Lee, Tomas Pfister
| Challenge: | Reverse-Enhanced Thinking (RevThink) is a framework for large language models to perform reverse thinking. |
| Approach: | They propose a framework for enhancing forward-backward reasoning by collecting data from a teacher model and employing three objectives to train a student model in a multi-task learning fashion. |
| Outcome: | The proposed framework outperforms a fine-tuning method trained on 10x more forward reasoning on 12 datasets covering commonsense, math, and logical reasoning. |
Towards Reasoning in Large Language Models: A Survey (2023.findings-acl)
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| Challenge: | Reasoning is a fundamental aspect of human intelligence that plays a crucial role in many intellectual activities. |
| Approach: | They propose to improve LLMs' ability to elicit reasoning by providing exemplars or prompts to model reasoning. |
| Outcome: | This paper provides a comprehensive overview of the state of knowledge on reasoning in large language models. |
Reverse Modeling in Large Language Models (2025.naacl-short)
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| Challenge: | Using pre-trained LLMs with reversed text inputs can improve their performance across multiple languages. |
| Approach: | They propose a way to determine whether LLMs can understand reversed text inputs by reversing entire paragraphs or documents at the token level. |
| Outcome: | The proposed model can be used to improve understanding across multiple languages. |
Large Language Models are Better Reasoners with Self-Verification (2023.findings-emnlp)
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| Challenge: | Existing methods to solve complex natural language processing tasks require multiple steps to verify the answers. |
| Approach: | They propose to use chain of thought prompting to solve reasoning tasks with large language models. |
| Outcome: | The proposed method can improve reasoning performance on arithmetic, commonsense, and logical reasoning datasets. |
Large Reasoning Models Are (Not Yet) Multilingual Latent Reasoners (2026.findings-acl)
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| Challenge: | Recent work shows that large reasoning models arrive at the correct answer before completing textual reasoning steps, indicating the presence of latent reasoning. |
| Approach: | They conduct a systematic investigation of multilingual latent reasoning in large reasoning models across 11 languages. |
| Outcome: | The proposed model arrive at the correct answer before completing the reasoning steps, indicating the presence of latent reasoning. |
Causal Inference with Large Language Model: A Survey (2025.findings-naacl)
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| Challenge: | Existing causal inference frameworks do not match human judgment in several key areas, such as domain knowledge, logical inference, and cultural context. |
| Approach: | They propose to apply large language models to causal inference tasks . they summarize the main causal problems and approaches and compare their results . |
| Outcome: | The proposed methods are compared with traditional methods in healthcare, finance, and economics. |
The Magic of IF: Investigating Causal Reasoning Abilities in Large Language Models of Code (2023.findings-acl)
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| Challenge: | entailment a) |
| Approach: | entailment : We want to explore whether Code-LLMs with code prompts are better . encoding a code prompt is better than text-only LLMs, they say . |
| Outcome: | entailment : Our results show that Code-LLMs with code prompts are better compared to text-only LLMs. |
BackMATH: Towards Backward Reasoning for Solving Math Problems Step by Step (2025.coling-industry)
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| Challenge: | Large language models (LLMs) have impressive results in reasoning, but when faced with more complex mathematical problems, performance drops significantly. |
| Approach: | They propose a backward reasoning dataset that includes 14K backward thinking problems and 100K reasoning steps. |
| Outcome: | The proposed model achieves an accuracy of 68.1% on the GSM8K dataset and 21.9% on the MATH dataset, exceeding the SOTA by 1.6% and 2.1% respectively. |
A Peek into Token Bias: Large Language Models Are Not Yet Genuine Reasoners (2024.emnlp-main)
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Bowen Jiang, Yangxinyu Xie, Zhuoqun Hao, Xiaomeng Wang, Tanwi Mallick, Weijie Su, Camillo Taylor, Dan Roth
| Challenge: | a new hypothesis-testing framework is developed to assess whether large language models possess genuine reasoning abilities or primarily depend on token bias. |
| Approach: | They propose a framework to assess whether large language models have genuine reasoning abilities or primarily depend on token bias. |
| Outcome: | The proposed framework outlines a list of hypotheses where token biases are readily identifiable . the results suggest that most LLMs still struggle with logical reasoning . |
Current Advances in LLM Reasoning (2026.acl-tutorials)
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| Challenge: | This tutorial examines comprehensive evaluation strategies to assess the reasoning abilities of large language models (LLMs) advanced inference time methods and post-training methods that aim to make LLMs think more like humans are discussed in this tutorial. |
| Approach: | This tutorial explores comprehensive evaluation strategies to assess the reasoning abilities of large language models (LLMs) and discusses two types of methods to improve models’ reasoning: advanced inference time methods, structured and self-improvement inference methods, and post-training methods, such as RLHF, DPO, and GRPO. |
| Outcome: | This tutorial examines evaluation strategies to assess the reasoning abilities of large language models and discusses two types of methods to improve models’ reasoning. |