67 citations · 109 across the 14 of their papers we have counts for
5 papers · 1 filter
Inference-time Alignment in Continuous Space
Yige Yuan, Teng Xiao, Li Yunfan +5
Aligning large language models with human feedback at inference time has received increasing attention due to its flexibility. Existing methods rely on generating multiple response…
Incentivizing Strong Reasoning from Weak Supervision
Yige Yuan, Teng Xiao, Shuchang Tao +4
Large language models (LLMs) have demonstrated impressive performance on reasoning-intensive tasks, but enhancing their reasoning abilities typically relies on either reinforcement…
ToolCoder: A Systematic Code-Empowered Tool Learning Framework for Large Language Models
Hanxing Ding, Shuchang Tao, Liang Pang +5
Tool learning has emerged as a crucial capability for large language models (LLMs) to solve complex real-world tasks through interaction with external tools. Existing approaches fa…
On the Diminishing Returns of Complex Robust RAG Training in the Era of Powerful LLMs
Hanxing Ding, Shuchang Tao, Liang Pang +5
Retrieval-augmented generation (RAG) systems traditionally employ sophisticated training strategies to enhance robustness against retrieval noise. In this work, we investigate a cr…
When to Trust LLMs: Aligning Confidence with Response Quality
Shuchang Tao, Liuyi Yao, Hanxing Ding +6
Despite the success of large language models (LLMs) in natural language generation, much evidence shows that LLMs may produce incorrect or nonsensical text. This limitation highlig…