54 citations · 124 across the 43 of their papers we have counts for
9 papers · 1 filter
Large Language Models show both individual and collective creativity comparable to humans
Luning Sun, Yuzhuo Yuan, Yuan Yao +6
Artificial intelligence has, so far, largely automated routine tasks, but what does it mean for the future of work if Large Language Models (LLMs) show creativity comparable to hum…
Blind Spot Navigation in Large Language Model Reasoning with Thought Space Explorer
Jinghan Zhang, Fengran Mo, Tharindu Cyril Weerasooriya +4
Large language models have shown strong reasoning capabilities through chain-structured methods such as Chain-of-Thought. Recent studies optimize thought structures by generating p…
BSharedRAG: Backbone Shared Retrieval-Augmented Generation for the E-commerce Domain
Kaisi Guan, Qian Cao, Yuchong Sun +2
Retrieval Augmented Generation (RAG) system is important in domains such as e-commerce, which has many long-tail entities and frequently updated information. Most existing works ad…
See or Guess: Counterfactually Regularized Image Captioning
Qian Cao, Xu Chen, Ruihua Song +3
Image captioning, which generates natural language descriptions of the visual information in an image, is a crucial task in vision-language research. Previous models have typically…
Prototypical Reward Network for Data-Efficient RLHF
Jinghan Zhang, Xiting Wang, Yiqiao Jin +3
The reward model for Reinforcement Learning from Human Feedback (RLHF) has proven effective in fine-tuning Large Language Models (LLMs). Notably, collecting human feedback for RLHF…
RATT: A Thought Structure for Coherent and Correct LLM Reasoning
Jinghan Zhang, Xiting Wang, Weijieying Ren +3
Large Language Models (LLMs) gain substantial reasoning and decision-making capabilities from thought structures. However, existing methods such as Tree of Thought and Retrieval Au…