Papers by Shiyu Tian
Explicit Alignment and Many-to-many Entailment Based Reasoning for Conversational Machine Reading (2023.findings-emnlp)
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| Challenge: | Recent research has explored how to improve the abilities of decision-making and question generation. |
| Approach: | They propose a pipeline framework that aligns the document and user-provided information in an explicit way, makes decisions using a lightweight many-to-many entailment reasoning module and generates follow-up questions based on the document. |
| Outcome: | The proposed framework achieves state-of-the-art in micro-accuracy and ranks the first place on the public leaderboard of the CMR benchmark dataset ShARC. |
KG-Adapter: Enabling Knowledge Graph Integration in Large Language Models through Parameter-Efficient Fine-Tuning (2024.findings-acl)
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| Challenge: | Large language models (LLMs) are criticized for lack of expertise and knowledge conflict . KG-Adapter is a parameter-level KG integration method for decoder-only LLMs . |
| Approach: | They propose a parameter-level KG integration method based on parameter-efficient fine-tuning . they use KG-Adapter to integrate knowledge graphs with LLMs and perform joint reasoning . |
| Outcome: | The proposed method outperforms the current state-of-the-art method on four datasets for two different tasks. |
EvoMemKG: An Evolvable Memory Agent for Multi-hop Knowledge Graph Reasoning (2026.findings-acl)
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| Challenge: | Existing methods for integrating knowledge graphs with large language models lack continuous learning capabilities. |
| Approach: | They propose an agent framework with a dynamic, evolvable memory mechanism specifically designed for KG reasoning. |
| Outcome: | EvoMemKG achieves state-of-the-art performance without training or tools . it achieves improvements of up to 20% over baseline on multi-hop queries . |