Papers by Sizhe Zhou
Grasping the Essentials: Tailoring Large Language Models for Zero-Shot Relation Extraction (2024.emnlp-main)
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| Challenge: | Existing Relation extraction models require extensive annotated training data, which is costly and labor-intensive to collect. |
| Approach: | They propose a new zero-shot RE task where only relation definitions are provided instead of seen-unseen relation instances. |
| Outcome: | The proposed task significantly improves cost-effective zero-shot performance by large margins. |
Towards End-to-End Open Conversational Machine Reading (2023.findings-eacl)
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| Challenge: | Existing approaches to the problem of open-retrieval conversational machine reading (OR-CMR) use two separate modules to approach the problem's two successive sub-tasks. |
| Approach: | They propose to model OR-CMR as a unified text-to-text task in a fully end-to end style and propose to use a text-based approach to solve the problem. |
| Outcome: | Experiments on the ShARC and OR-ShARC dataset show that the proposed framework can generalize to different backbone models. |
Topic-Oriented Open Relation Extraction with A Priori Seed Generation (2024.emnlp-main)
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| Challenge: | Existing methods for open relation extraction give sub-optimal results on specific topics. |
| Approach: | They propose a method that leverages the built-in knowledge of large language models to maintain a dynamic seed relation dictionary for the topic. |
| Outcome: | The proposed approach empowers better topic-oriented control over the generated relations and improves ORE performance along the five dimensions, especially on specialized and narrow topics. |
Text2DB: Integration-Aware Information Extraction with Large Language Model Agents (2024.findings-acl)
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| Challenge: | Current methods for information extraction (IE) focus on integrating IE output with the database . a long-overlooked question is what counts as "relevant knowledge" |
| Approach: | They propose a task that emphasizes integration of IE output and the database . they introduce a benchmark and an LLM agent framework for this task . |
| Outcome: | The proposed task integrates IE output and the target database (or knowledge base) it meets common demands such as data infilling, row population, and column addition . |