Papers by Xin Quan
Enhancing Ethical Explanations of Large Language Models through Iterative Symbolic Refinement (2024.eacl-long)
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| Challenge: | Recent studies have focused on the application and evaluation of Large Language Models (LLMs) but LLMs are still prone to factual errors and inconsistencies in their explanations, offering limited control and interpretability for inference in complex domains. |
| Approach: | They propose an abductive-deductive framework that integrates Large Language Models with an external backward-chaining solver to refine step-wise natural language explanations. |
| Outcome: | The proposed framework improves explanations generated via in-context learning methods and Chain-of-Thought (CoT) on ethical NLI tasks while producing formal proofs describing and supporting models’ reasoning. |
Consistent Autoformalization for Constructing Mathematical Libraries (2024.emnlp-main)
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| Challenge: | Autoformalization is the task of automatically translating mathematical content written in natural language to a formal language expression. |
| Approach: | They propose to use three mechanisms to improve autoformalization quality . they propose to combine most-similar retrieval augmented generation, denoising steps and auto-correction with syntax error feedback to improve syntactic, terminological and semantic control. |
| Outcome: | The proposed mechanisms can deliver syntactically, terminologically and semantically more consistent results across different models. |
CharacterEval: A Chinese Benchmark for Role-Playing Conversational Agent Evaluation (2024.acl-long)
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| Challenge: | CharacterEval is a benchmark for comprehensive RPCA assessment in Chinese . authors show that Chinese LLMs exhibit more promising capabilities than GPT-4 in role-playing conversation. |
| Approach: | They propose a Chinese benchmark for comprehensive RPCA assessment . they use a dataset of Chinese role-playing dialogues and character profiles . |
| Outcome: | The proposed benchmark demonstrates that Chinese LLMs exhibit more promising capabilities than GPT-4 in Chinese role-playing conversation. |
ODE Transformer: An Ordinary Differential Equation-Inspired Model for Sequence Generation (2022.acl-long)
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Bei Li, Quan Du, Tao Zhou, Yi Jing, Shuhan Zhou, Xin Zeng, Tong Xiao, JingBo Zhu, Xuebo Liu, Min Zhang
| Challenge: | Residual networks are an Euler discretization of solutions to Ordinary Differential Equations (ODE). |
| Approach: | They propose a residual block of layers in Transformer that can be described as a higher-order solution to ODE. |
| Outcome: | The proposed architecture can gain large improvements over strong baselines at a slight cost in inference efficiency. |
Verification and Refinement of Natural Language Explanations through LLM-Symbolic Theorem Proving (2024.emnlp-main)
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| Challenge: | Existing methods for assessing the validity of explanations for NLI are time-consuming and prone to logical errors. |
| Approach: | They propose a framework that integrates Large Language Models and Theorem Provers to verify and refine natural language explanations through crowd-sourcing . they propose to use TPs to generate and formalise explanatory sentences and suggest potential inference strategies for NLI. |
| Outcome: | The proposed framework generates and formalises explanatory sentences and suggests potential inference strategies for NLI. |
Faithful and Robust LLM-Driven Theorem Proving for NLI Explanations (2025.acl-long)
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| Challenge: | Recent work has shown that the interaction of large language models (LLMs) with theorem provers (TPs) can help verify and improve the validity of NLI explanations. |
| Approach: | They propose to use logical expressions to guide LLMs in generating structured proof sketches and to use them to improve their accuracy. |
| Outcome: | The proposed strategies improve autoformalisation, syntactic errors and explanation refinement over the state-of-the-art model. |
PEIRCE: Unifying Material and Formal Reasoning via LLM-Driven Neuro-Symbolic Refinement (2025.acl-demo)
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| Challenge: | Large Language Models (LLMs) are capable of material inference but lack formal rigour and verifiability. |
| Approach: | They propose a framework to unify material and formal inference through an iterative conjecture–criticism process. |
| Outcome: | The proposed framework unifies material and formal inference through an iterative conjecture–criticism process. |
Transform Retrieval for Textual Entailment in RAG (2025.naacl-short)
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| Challenge: | Existing retrieval methods prioritize relevance without ensuring the retrieved documents semantically support answering the queries. |
| Approach: | They propose a novel approach to improve Textual Entailment Retrieval within the framework of Retri-Augmented Generation (RAG) they transform query embeddings to better align with semantic entailment without re-encoding the document corpus. |
| Outcome: | The proposed approach consistently approaches the skyline across multiple datasets, demonstrating its strength in many-to-many retrieval scenarios. |