Papers with FOLIO
LTRAG: Enhancing Autoformalization and Self-refinement for Logical Reasoning with Thought-Guided RAG (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have shown promise in natural language reasoning, especially with techniques like chain-of-thought prompting. |
| Approach: | They propose a framework to enhance autoformalization and self-refinement for logical reasoning with Retrieval-Augmented Generation (RAG) by building knowledge bases of thought-guided examples. |
| Outcome: | The proposed framework outperforms Logic-LM and LINC on FOLIO and AR-LSAT, and achieves an accuracy gain of 13% over Logic LM and the proposed methods on GPT-4 and AR LSAT. |
Concise and Organized Perception Facilitates Reasoning in Large Language Models (2025.findings-naacl)
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| Challenge: | Extensive experimental results on several popular logical benchmarks (ProofWriter, PrOntoQA, PrONtoQA-OOD, and FOLIO) and mathematical benchmark (DI-GSM) show that COP significantly outperforms previous state-of-the-art methods. |
| Approach: | They propose a reasoning approach called Concise and Organized Perception (COP) that carefully analyzes the given statements to identify the most pertinent information while eliminating redundancy efficiently. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on several popular logical benchmarks and mathematical benchmarks. |
Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning (2023.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have shown human-like reasoning abilities but struggle with complex logical problems. |
| Approach: | They propose a framework which integrates large language models with symbolic solvers to improve logical problem-solving by combining them with a self-refinement module. |
| Outcome: | The proposed framework improves logical problem-solving by integrating LLMs with symbolic solvers. |
LINC: A Neurosymbolic Approach for Logical Reasoning by Combining Language Models with First-Order Logic Provers (2023.emnlp-main)
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Theo Olausson, Alex Gu, Ben Lipkin, Cedegao Zhang, Armando Solar-Lezama, Joshua Tenenbaum, Roger Levy
| Challenge: | Logical reasoning is an important task for artificial intelligence, says a new study . many prompting-based strategies to enable large language models fail in subtle and unpredictable ways. |
| Approach: | They propose to reformulate logical reasoning tasks by leveraging large language models . they use a modular neurosymbolic programming approach to translate premises and conclusions from natural language to logic . |
| Outcome: | The proposed approach outperforms open-source models on FOLIO and ProofWriter while showing distinct failure modes. |
DetermLR: Augmenting LLM-based Logical Reasoning from Indeterminacy to Determinacy (2024.acl-long)
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| Challenge: | Recent advances in large language models (LLMs) have revolutionized the landscape of reasoning tasks. |
| Approach: | They propose a new approach that rethinks the reasoning process as an evolution from indeterminacy to determinacy. |
| Outcome: | The proposed model surpasses all baselines on various logical reasoning benchmarks. |
PrefRAG: Correcting Semantic Errors in Auto-Formalization for Logical Reasoning with Program Preference RAG (2026.findings-acl)
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| Challenge: | Existing auto-formalization methods for logical reasoning are prone to syntactic and semantic errors. |
| Approach: | They propose a neuro-symbolic paradigm for logical reasoning based on auto-formalization . they propose 'programme preference retrieval-augmented generation' to detect and repair syntactic and semantic errors. |
| Outcome: | The proposed approach outperforms baselines on in-distribution and out-of-difference datasets. |
LSEG: A Fine-tuning Free Method for NL2FOL via Logic-Structure and Entropy Guided Inference Controlling (2026.findings-acl)
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| Challenge: | Large language models struggle with natural language to first order logic (NL2FOL) translation due to logical hallucination. |
| Approach: | They propose a fine-tuning free framework to correct hidden state deviation by leveraging logical stability across logic preserving perturbations of the input. |
| Outcome: | The proposed framework improves logical consistency during inference and improves accuracy over baselines. |
FOLIO: Natural Language Reasoning with First-Order Logic (2024.emnlp-main)
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Simeng Han, Hailey Schoelkopf, Yilun Zhao, Zhenting Qi, Martin Riddell, Wenfei Zhou, James Coady, David Peng, Yujie Qiao, Luke Benson, Lucy Sun, Alexander Wardle-Solano, Hannah Szabó, Ekaterina Zubova, Matthew Burtell, Jonathan Fan, Yixin Liu, Brian Wong, Malcolm Sailor, Ansong Ni, Linyong Nan, Jungo Kasai, Tao Yu, Rui Zhang, Alexander Fabbri, Wojciech Kryscinski, Semih Yavuz, Ye Liu, Xi Lin, Shafiq Joty, Yingbo Zhou, Caiming Xiong, Rex Ying, Arman Cohan, Dragomir Radev
| Challenge: | Existing benchmarks for logical reasoning in large language models lack language naturalness or limited complexity. |
| Approach: | They propose to use first-order logic annotations to evaluate logical reasoning capabilities of large language models. |
| Outcome: | The proposed dataset evaluates the FOL reasoning ability of supervised fine-tuning on medium-sized language models. |
Logical Reasoning with Outcome Reward Models for Test-Time Scaling (2025.emnlp-main)
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| Challenge: | Logical reasoning is a critical benchmark for evaluating the capabilities of large language models (LLMs), but it is under-explored in deductive reasoning. |
| Approach: | They propose to use Chain-of-Thought to generate data using single and multiple samples to train ORMs. |
| Outcome: | The proposed model expands the type of errors covered in the training dataset, covering previously unexplored error types. |
Enhancing Logical Reasoning in Language Models via Symbolically-Guided Monte Carlo Process Supervision (2025.emnlp-main)
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| Challenge: | Large language models have shown strong performance in many reasoning benchmarks, but lack robust planning or symbolic abstractions. |
| Approach: | They propose to synthesize high-quality symbolic reasoning trajectories with stepwise pseudo-labels at scale via Monte Carlo estimation. |
| Outcome: | The proposed method can be trained on high-quality symbolic reasoning trajectories with stepwise pseudo-labels at scale using Monte Carlo estimation. |