| Challenge: | Existing approaches to generate responses using few-shot examples depend on the provided examples, limiting the model’s reasoning capabilities. |
| Approach: | They propose a model that emulates human reasoning during response generation by using curated few-shot prompts instead of manually crafted few-shot examples. |
| Outcome: | The proposed model achieves strong performance on diverse LLMs benchmarks with reasonable rationales. |
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| Challenge: | Large language models (LLMs) are currently dominating the field of natural language processing, but spatial reasoning ability is lacking in LLMs. |
| Approach: | They propose a prompting technique that integrates bullet lists, coordinates, and visualizations into the reasoning process and integrates them into planning tasks. |
| Outcome: | The proposed technique boosts LLMs' spatial reasoning abilities compared to previous prompting techniques. |
Let’s Be Self-generated via Step by Step: A Curriculum Learning Approach to Automated Reasoning with Large Language Models (2025.findings-acl)
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| Challenge: | Existing efforts to improve CoT prompting have limitations that require extensive human effort or performance needs to be improved. |
| Approach: | They propose a prompt approach for automatic reasoning called LBS3 inspired by curriculum learning which better reflects human learning habits. |
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Language Models of Code are Few-Shot Commonsense Learners (2022.emnlp-main)
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| Challenge: | Existing approaches to generate graphs using pre-trained language models hinder their ability to generate them correctly. |
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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. |
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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. |
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LLMCrit: Teaching Large Language Models to Use Criteria (2024.findings-acl)
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| Challenge: | Current research on using criteria to provide feedback on tasks is limited . a general framework that can be used to teach large language models to use criteria is lacking . |
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An Improved, Strong Baseline for Pre-Trained Large Language Models as Task-Oriented Dialogue Systems (2025.findings-emnlp)
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| Challenge: | Recent studies have shown that Large Language Models perform insufficiently as TOD systems. |
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Retrieval-Augmented Code Generation for Situated Action Generation: A Case Study on Minecraft (2024.findings-emnlp)
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Complex Reasoning in Natural Language (2023.acl-tutorials)
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| Challenge: | Recent research shows that pretrained language models are often brittle for complex reasoning tasks. |
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Structure Guided Prompt: Instructing Large Language Model in Multi-Step Reasoning by Exploring Graph Structure of the Text (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) excel at straightforward reasoning tasks, but struggle when faced with complex multi-step reasoning. |
| Approach: | They propose a framework that converts unstructured text into a graph and instructs LLMs to navigate this graph using task-specific strategies. |
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