5 papers
Retrieve-Refine-Calibrate: A Framework for Complex Claim Fact-Checking
Mingwei Sun, Qianlong Wang, Ruifeng Xu
Fact-checking aims to verify the truthfulness of a claim based on the retrieved evidence. Existing methods typically follow a decomposition paradigm, in which a claim is broken dow…
Comprehensive and Efficient Distillation for Lightweight Sentiment Analysis Models
Guangyu Xie, Yice Zhang, Jianzhu Bao +4
Recent efforts leverage knowledge distillation techniques to develop lightweight and practical sentiment analysis models. These methods are grounded in human-written instructions a…
Targeted Distillation for Sentiment Analysis
Yice Zhang, Guangyu Xie, Jingjie Lin +4
This paper explores targeted distillation methods for sentiment analysis, aiming to build compact and practical models that preserve strong and generalizable sentiment analysis cap…
LLMAtKGE: Large Language Models as Explainable Attackers against Knowledge Graph Embeddings
Ting Li, Yang Yang, Yipeng Yu +3
Adversarial attacks on knowledge graph embeddings (KGE) aim to disrupt the model's ability of link prediction by removing or inserting triples. A recent black-box method has attemp…
KDRL: Post-Training Reasoning LLMs via Unified Knowledge Distillation and Reinforcement Learning
Hongling Xu, Qi Zhu, Heyuan Deng +6
Recent advances in large language model (LLM) post-training have leveraged two distinct paradigms to enhance reasoning capabilities: reinforcement learning (RL) and knowledge disti…