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ProUIE: A Macro-to-Micro Progressive Learning Method for LLM-based Universal Information Extraction
Wenda Liu, Zhigang Song, Shuai Nie +11
LLM-based universal information extraction (UIE) methods often rely on additional information beyond the original training data, which increases training complexity yet often yield…
SSL: Sweet Spot Learning for Differentiated Guidance in Agentic Optimization
Jinyang Wu, Changpeng Yang, Yuhao Shen +9
Reinforcement learning with verifiable rewards has emerged as a powerful paradigm for training intelligent agents. However, existing methods typically employ binary rewards that fa…
From Imitation to Discrimination: Toward A Generalized Curriculum Advantage Mechanism Enhancing Cross-Domain Reasoning Tasks
Changpeng Yang, Jinyang Wu, Yuchen Liu +9
Reinforcement learning has emerged as a paradigm for post-training large language models, boosting their reasoning capabilities. Such approaches compute an advantage value for each…
Can large language models understand uncommon meanings of common words?
Jinyang Wu, Feihu Che, Xinxin Zheng +5
Large language models (LLMs) like ChatGPT have shown significant advancements across diverse natural language understanding (NLU) tasks, including intelligent dialogue and autonomo…
KS-LLM: Knowledge Selection of Large Language Models with Evidence Document for Question Answering
Xinxin Zheng, Feihu Che, Jinyang Wu +4
Large language models (LLMs) suffer from the hallucination problem and face significant challenges when applied to knowledge-intensive tasks. A promising approach is to leverage ev…