Papers by Yunxiao Zhao
Leibniz: Theory-of-Mind Driven Neuro-Symbolic Logical Reasoning via Multi-Agent Collaboration (2026.acl-long)
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| Challenge: | Existing methods for logical reasoning with large language models suffer from insufficient rule semantic grounding and weak rule application mechanisms. |
| Approach: | They propose a theory-of-mind driven neuro-symbolic reasoning framework that integrates natural language and symbolic representations throughout the reasoning process. |
| Outcome: | The proposed model surpasses state-of-the-art models in reasoning accuracy and flexibility. |
Suggest-Verify-Revise: A Three-Stage Document-Level Event Causality Identification with Narrative Consistency (2026.acl-long)
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| Challenge: | Existing methods for document-level Event Causality Identification rely on local semantic similarity for independent event-pair discrimination . Existing approaches ignore the influence of the overall narrative backbone in the propagation of causal dependencies and the role differentiation of events within multi-cause/multi-effect structures. |
| Approach: | They propose a suggest-verify-revise approach for document-level Event Causality Identification with narrative consistency (SVRECI) they integrate heuristic causal suggestions generated by an LLM with structural suggestions derived from hypergraph modeling . |
| Outcome: | The proposed approach outperforms existing methods on event-storylines and Causal-TimeBank datasets. |
GCRC: A New Challenging MRC Dataset from Gaokao Chinese for Explainable Evaluation (2021.findings-acl)
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| Challenge: | Existing machine reading comprehension datasets lack an explainable evaluation of systems' reasoning capabilities. |
| Approach: | They propose a dataset with multi-choice questions that evaluates MRC systems' reasoning process . they use sentence-level relevant supporting facts, error reason of distractors to evaluate MRC . |
| Outcome: | The proposed dataset is more challenging and useful for identifying limitations of existing MRC systems in an explainable way. |
LOG: A Local-to-Global Optimization Approach for Retrieval-based Explainable Multi-Hop Question Answering (2025.coling-main)
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| Challenge: | Existing approaches to multi-hop question answering emphasize single-step and multi-step iterative decomposition or retrieval, which are susceptible to failure in long-chain reasoning due to the progressive accumulation of erroneous information. |
| Approach: | They propose a Local-tO-Global optimized retrieval method to discover more beneficial information and improve tuplet objective loss. |
| Outcome: | The proposed method outperforms state-of-the-art models and significantly improves multi-hop reasoning. |
AGR: Reinforced Causal Agent-Guided Self-explaining Rationalization (2024.acl-short)
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| Challenge: | Existing rationalization approaches are susceptible to degeneration due to lack of effective control over the learning direction of the model during training. |
| Approach: | They propose an agent-guided rationalization approach that guides the next step of the model based on its current training state. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on BeerAdvocate and HotelReview datasets. |