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AutoRD: An Automatic and End-to-End System for Rare Disease Knowledge Graph Construction Based on Ontologies-enhanced Large Language Models
We quantitatively evaluate our system on entity extraction, relation extraction, and the performance of knowledge graph construction.
PILOT: Legal Case Outcome Prediction with Case Law
We demonstrate the importance of accurately identifying precedent cases and mitigating the temporal shift when making predictions for case law, as our method shows a significant improvement over the prior methods that focus on civil law case outcome predictions.
Learn to Refuse: Making Large Language Models More Controllable and Reliable through Knowledge Scope Limitation and Refusal Mechanism
To achieve this, we utilize a structured knowledge base to represent all the LLM's understanding of the world, enabling it to provide traceable gold knowledge.
AutoAM: An End-To-End Neural Model for Automatic and Universal Argument Mining
Argument mining is to analyze argument structure and extract important argument information from unstructured text.
GraphReason: Enhancing Reasoning Capabilities of Large Language Models through A Graph-Based Verification Approach
By evaluating these graphs, models can yield more accurate and reliable results. Our experimental results show that our graph-based verification method not only significantly enhances the reasoning abilities of LLMs but also outperforms existing verifier methods in terms of improving these models' reasoning performance.
DiagGPT: An LLM-based and Multi-agent Dialogue System with Automatic Topic Management for Flexible Task-Oriented Dialogue
A significant application of Large Language Models (LLMs), like ChatGPT, is their deployment as chat agents, which respond to human inquiries across a variety of domains.
