Papers by Yeliang Xiu
LTRAG: Enhancing Autoformalization and Self-refinement for Logical Reasoning with Thought-Guided RAG (2025.findings-acl)
Copied to clipboard
| 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. |
MultiLogicNMR(er): A Benchmark and Neural-Symbolic Framework for Non-monotonic Reasoning with Multiple Extensions (2025.emnlp-main)
Copied to clipboard
| Challenge: | Non-monotonic reasoning is widely used in daily life and legal reasoning. |
| Approach: | They propose a neural-symbolic framework for multi-extension NMR and propose to build two variants with more extensions or text diversity. |
| Outcome: | The proposed framework outperforms prompt-based methods and outperformed some fine-tuning methods. |
LogicNMR: Probing the Non-monotonic Reasoning Ability of Pre-trained Language Models (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing work examines the non-monotonic reasoning ability of pre-trained language models. |
| Approach: | They construct a non-monotonic reasoning benchmark with explicit default rules and iterative updates. |
| Outcome: | The proposed model achieves a higher accuracy than the benchmark, but performs poorly on the benchmark. |