Papers with multi-step problems
Confidence-Aware Reasoning: Optimizing Self-Guided Thinking Trajectories in Large Reasoning Models (2025.emnlp-industry)
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| Challenge: | Chain-of-thought enables large reasoning models to reason through multi-step problems but often leads to unnecessarily long or redundant reasoning traces, a phenomenon known as overthinking. |
| Approach: | They propose an inference-time framework that selectively prunes low-utility reasoning blocks and halts early when sufficient confidence has been achieved. |
| Outcome: | The proposed framework improves answer accuracy by up to +13.3% while reducing average reasoning length by 40%–50%. |
BioPlanner: Automatic Evaluation of LLMs on Protocol Planning in Biology (2023.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have impressive capabilities on a wide range of tasks, such as question answering and the generation of coherent text and code. |
| Approach: | They propose a framework for automatic evaluation of large language models on open-ended planning problems and a dataset of biology protocols with corresponding pseudocode representations. |
| Outcome: | The proposed framework evaluates an LLM on a dataset of biology protocols with corresponding pseudocode representations. |
RESPROMPT: Residual Connection Prompting Advances Multi-Step Reasoning in Large Language Models (2024.naacl-long)
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Song Jiang, Zahra Shakeri, Aaron Chan, Maziar Sanjabi, Hamed Firooz, Yinglong Xia, Bugra Akyildiz, Yizhou Sun, Jinchao Li, Qifan Wang, Asli Celikyilmaz
| Challenge: | Chain-of-thought (CoT) has impressively unlocked the reasoning potential of large language models (LLMs), but it falls short when tackling problems that require multiple reasoning steps. |
| Approach: | They propose a new prompting strategy that advances multi-step reasoning in LLMs by integrating necessary connections into prompts. |
| Outcome: | The proposed strategy improves multi-step reasoning accuracy and improves reasoning accuracy across math, sequential, and commonsense domains. |
SPaRC: A Spatial Pathfinding Reasoning Challenge (2025.emnlp-main)
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| Challenge: | Existing reasoning datasets saturate and fail to test abstract, multi-step problems, especially pathfinding and complex rule constraint satisfaction. |
| Approach: | They propose to use a spatial few-shot grid to evaluate spatial and rule-based reasoning with 1,000 2D grid puzzles. |
| Outcome: | The proposed model can be used to evaluate spatial reasoning and improve its accuracy. |
When is Tree Search Useful for LLM Planning? It Depends on the Discriminator (2024.acl-long)
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| Challenge: | Existing methods to build language agents that can plan efficiently and accurately have not met the needs of advanced planning methods to achieve such improvements. |
| Approach: | They propose to use iterative correction and tree search to solve multi-step problems in a language agent framework with three components: a generator, a discriminator, and a planning method. |
| Outcome: | The proposed methods improve performance on two tasks, text-to-SQL parsing and mathematical reasoning, while using discriminators with 90% accuracy. |
Hopping Too Late: Exploring the Limitations of Large Language Models on Multi-Hop Queries (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) can solve complex multi-step problems, but little is known about how these computations are implemented internally. |
| Approach: | They propose a "back-patching" analysis method to solve multi-hop queries . they propose resolving the bridge entity into the bridge and the second hop into the target entity into latent steps. |
| Outcome: | The proposed method solves multi-hop queries that require two information extraction steps . it shows that the later layers lack the necessary knowledge to correctly generate the answer . |