Papers with multi-step reasoning capabilities
A Multi-Agent Framework for Quantitative Finance : An Application to Portfolio Management Analytics (2025.emnlp-industry)
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| Challenge: | Recent advances in Large Language Models (LLMs) have opened up promising new avenues by enhancing reasoning and inference capabilities across diverse data and information sources. |
| Approach: | They propose a multi-agent framework that facilitates mathematical modeling and data analytics by dynamically generating executable code. |
| Outcome: | The proposed framework outperforms existing models on portfolio management tasks and provides human-readable explanations for its predictions. |
FinePrompt: Unveiling the Role of Finetuned Inductive Bias on Compositional Reasoning in GPT-4 (2023.findings-emnlp)
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| Challenge: | Large language models such as GPT-4 have demonstrated impressive capability to solve textual understanding problems at a level parallel to or surpassing state-of-the-art taskspecific models. |
| Approach: | They propose to transfer task-specific inductive biases from finetuned models to prompts to improve GPT-4's compositional reasoning capabilities. |
| Outcome: | The proposed prompt scheme shows competitive zero-shot and few-shot performances compared to existing prompts on complicated reasoning tasks. |
Implicit Reasoning in Transformers is Reasoning through Shortcuts (2025.findings-acl)
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| Challenge: | Language models can perform step-by-step reasoning and achieve high accuracy in both in-domain and out-of-domain tests via implicit reasoning. |
| Approach: | They train GPT-2 from scratch on a curated multi-step mathematical reasoning dataset and conduct analytical experiments to investigate how language models perform implicit reasoning in multi- step tasks. |
| Outcome: | The proposed model performs better on multi-step tasks than the explicit reasoning model. |
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. |
| Outcome: | The proposed framework improves the multi-step reasoning capabilities of Large Language Models in a zero-shot setting. |
Unveiling Narrative Reasoning Limits of Large Language Models with Trope in Movie Synopses (2024.findings-emnlp)
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Hung-Ting Su, Ya-Ching Hsu, Xudong Lin, Xiang-Qian Shi, Yulei Niu, Han-Yuan Hsu, Hung-yi Lee, Winston Hsu
| Challenge: | Large language models (LLMs) equipped with chain-of-thoughts (CoT) prompting have shown significant multi-step reasoning capabilities in factual content like mathematics, commonsense, and logic. |
| Approach: | They introduce a trope-wise querying approach to assess the abstract reasoning abilities of large language models (LLMs) and uncover their low performance. |
| Outcome: | The proposed approach boosts the F1 score by 11.8 points and also reduces the performance of the large language models (LLMs) it also shows that it can cause hallucinations in narrative content, reducing the performance. |
LogicTree: Structured Proof Exploration for Coherent and Rigorous Logical Reasoning with Large Language Models (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) have remarkable multi-step reasoning capabilities, but they still face challenges in complex logical reasoning. |
| Approach: | They propose an algorithm-guided search framework that automates structured proof exploration and ensures logical coherence. |
| Outcome: | The proposed framework outperforms o3-mini and chain-of-thought with average gains of 23.6% and 12.5% on five datasets. |
Dissecting Clinical Reasoning in Natural Language Inference for Large Language Models (2026.findings-acl)
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| Challenge: | Recent studies on large language models (LLMs) have demonstrated the impact of prompting strategies and fine-tuning techniques on their reasoning capabilities. |
| Approach: | They examine four classes of prompting strategies to elicit reasoning in large language models . they then construct demonstrations using a frontier model to distil multi-step reasoning capabilities into smaller models based on Low-Rank Adaptation (LoRA). |
| Outcome: | The proposed model improves in 75% of the models on MedNLI and TREC Clinical Trials. |