21 citations · 23 across the 8 of their papers we have counts for
Showing 2023 · cs.CLShow all
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cs.CL2023
Filling the Image Information Gap for VQA: Prompting Large Language Models to Proactively Ask Questions
Ziyue Wang, Chi Chen, Peng Li +1
Large Language Models (LLMs) demonstrate impressive reasoning ability and the maintenance of world knowledge not only in natural language tasks, but also in some vision-language ta…
cs.CL2023
Failures Pave the Way: Enhancing Large Language Models through Tuning-free Rule Accumulation
Zeyuan Yang, Peng Li, Yang Liu
Large Language Models (LLMs) have showcased impressive performance. However, due to their inability to capture relationships among samples, these frozen LLMs inevitably keep repeat…
cs.CL2023★ 21 cited
A Dynamic LLM-Powered Agent Network for Task-Oriented Agent Collaboration
Zijun Liu, Yanzhe Zhang, Peng Li +2
Recent studies show that collaborating multiple large language model (LLM) powered agents is a promising way for task solving. However, current approaches are constrained by using…