Search Results for author:
Found 10 papers, 7 papers with code
Legal Rule Induction: Towards Generalizable Principle Discovery from Analogous Judicial Precedents
To address this gap, we formalize Legal Rule Induction (LRI) as the task of deriving concise, generalizable doctrinal rules from sets of analogous precedents, distilling their shared preconditions, normative behaviors, and legal consequences.
From Automation to Autonomy: A Survey on Large Language Models in Scientific Discovery
Large Language Models (LLMs) are catalyzing a paradigm shift in scientific discovery, evolving from task-specific automation tools into increasingly autonomous agents and fundamentally redefining research processes and human-AI collaboration.
Top Ten Challenges Towards Agentic Neural Graph Databases
This paper introduces Agentic Neural Graph Databases (Agentic NGDBs), which extend NGDBs with three core functionalities: autonomous query construction, neural query execution, and continuous learning.
Enhancing Transformers for Generalizable First-Order Logical Entailment
This paper investigates the generalizable first-order logical reasoning ability of transformers with their parameterized knowledge and explores ways to improve it.
Persona Knowledge-Aligned Prompt Tuning Method for Online Debate
Debate is the process of exchanging viewpoints or convincing others on a particular issue.
GoldCoin: Grounding Large Language Models in Privacy Laws via Contextual Integrity Theory
Privacy issues arise prominently during the inappropriate transmission of information between entities.
Text-Tuple-Table: Towards Information Integration in Text-to-Table Generation via Global Tuple Extraction
The task of condensing large chunks of textual information into concise and structured tables has gained attention recently due to the emergence of Large Language Models (LLMs) and their potential benefit for downstream tasks, such as text summarization and text mining.
NegotiationToM: A Benchmark for Stress-testing Machine Theory of Mind on Negotiation Surrounding
Large Language Models (LLMs) have sparked substantial interest and debate concerning their potential emergence of Theory of Mind (ToM) ability.
Gold: A Global and Local-aware Denoising Framework for Commonsense Knowledge Graph Noise Detection
Commonsense Knowledge Graphs (CSKGs) are crucial for commonsense reasoning, yet constructing them through human annotations can be costly.
Neural Correction Model for Open-Domain Named Entity Recognition
In this work, to address the low precision and recall problems, we first utilize DBpedia as the source of distant supervision to annotate abstracts from Wikipedia and design a neural correction model trained with a human-annotated NER dataset, DocRED, to correct the false entity labels.
