52 citations · 172 across the 38 of their papers we have counts for
11 papers · 1 filter
Tool Zero: Training Tool-Augmented LLMs via Pure RL from Scratch
Yirong Zeng, Xiao Ding, Yutai Hou +9
Training tool-augmented LLMs has emerged as a promising approach to enhancing language models' capabilities for complex tasks. The current supervised fine-tuning paradigm relies on…
Com: A Causal-Guided Benchmark for Exploring Complex Commonsense Reasoning in Large Language Models
Kai Xiong, Xiao Ding, Yixin Cao +7
Large language models (LLMs) have mastered abundant simple and explicit commonsense knowledge through pre-training, enabling them to achieve human-like performance in simple common…
CrossICL: Cross-Task In-Context Learning via Unsupervised Demonstration Transfer
Jinglong Gao, Xiao Ding, Lingxiao Zou +2
In-Context Learning (ICL) enhances the performance of large language models (LLMs) with demonstrations. However, obtaining these demonstrations primarily relies on manual effort. I…
ExpeTrans: LLMs Are Experiential Transfer Learners
Jinglong Gao, Xiao Ding, Lingxiao Zou +3
Recent studies provide large language models (LLMs) with textual task-solving experiences via prompts to improve their performance. However, previous methods rely on substantial hu…
Self-Route: Automatic Mode Switching via Capability Estimation for Efficient Reasoning
Yang He, Xiao Ding, Bibo Cai +5
While reasoning-augmented large language models (RLLMs) significantly enhance complex task performance through extended reasoning chains, they inevitably introduce substantial unne…
Information Gain-Guided Causal Intervention for Autonomous Debiasing Large Language Models
Zhouhao Sun, Xiao Ding, Li Du +5
Despite significant progress, recent studies indicate that current large language models (LLMs) may still capture dataset biases and utilize them during inference, leading to the p…