Papers with backward reasoning
GeoEval: Benchmark for Evaluating LLMs and Multi-Modal Models on Geometry Problem-Solving (2024.findings-acl)
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| Challenge: | Recent advances in large language models (LLMs) and multi-modal models (MMs) have demonstrated remarkable capabilities in problem-solving, but their proficiency in tackling geometry math problems has not been thoroughly evaluated. |
| Approach: | They propose a benchmark to evaluate the performance of large language models and multi-modal models in solving geometry math problems. |
| Outcome: | The proposed model achieves 55.67% accuracy on main subset but only 6.00% accuracy on hard subset. |
BAR: A Backward Reasoning based Agent for Complex Minecraft Tasks (2025.findings-acl)
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| Challenge: | Existing studies focus on forward reasoning based planning, but this paradigm doesn't work well for complex tasks. |
| Approach: | They propose to decompose a task into easily executed steps by planning and use a backward reasoning based agent to make the planning starting from the terminal state. |
| Outcome: | The proposed model outperforms existing methods and the proposed modules in a virtual environment that simulates complex tasks based on real-world scenarios. |
Exploring Backward Reasoning in Large Language Models (2025.findings-naacl)
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| 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. |
Forward-Backward Reasoning in Large Language Models for Mathematical Verification (2024.findings-acl)
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| Challenge: | Extensive experiments on six standard mathematical data sets and three LLMs show that FOBAR achieves state-of-the-art performance. |
| Approach: | They propose to combine forward and backward reasoning to verify candidate answers . they propose to use a template to mask a number and ask the LLM to answer a backward question . |
| Outcome: | Experiments on mathematical data show that proposed backward reasoning outperforms Self-Consistency. |
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. |
Enhancing Code Generation Performance of Smaller Models by Distilling the Reasoning Ability of LLMs (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) have made significant advances in code generation through the ‘Chain-of-Thought’ prompting technique. |
| Approach: | They propose a framework which aims to transfer LLMs’ reasoning capabilities to smaller models through distillation. |
| Outcome: | The proposed framework improves the smaller model's code generation performance by over 130% on the APPS benchmark. |
Beyond Context to Cognitive Appraisal: Emotion Reasoning as a Theory of Mind Benchmark for Large Language Models (2025.findings-acl)
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| Challenge: | Recent studies have shown that large language models (LLMs) reason about others' emotional states using contextual information, within a Theory-of-Mind framework. |
| Approach: | They propose to use large language models to reason about others’ emotional states using contextual information within a Theory-of-Mind framework. |
| Outcome: | The proposed models can reason about situations and appraisals, but are poor at associating situational outcomes and appraisal with specific emotions. |