8 papers
Multi-Agent Design: Optimizing Agents with Better Prompts and Topologies
Han Zhou, Xingchen Wan, Ruoxi Sun +5
Large language models, employed as multiple agents that interact and collaborate with each other, have excelled at solving complex tasks. The agents are programmed with prompts tha…
Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models
Fei Wang, Xingchen Wan, Ruoxi Sun +2
Retrieval augmented generation (RAG), while effectively integrating external knowledge to address the inherent limitations of large language models (LLMs), can be hindered by imper…
From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation
Xingchen Wan, Han Zhou, Ruoxi Sun +3
Recent advances in long-context large language models (LLMs) have led to the emerging paradigm of many-shot in-context learning (ICL), where it is observed that scaling many more d…
Bayesian Optimization of Functions over Node Subsets in Graphs
Huidong Liang, Xingchen Wan, Xiaowen Dong
We address the problem of optimizing over functions defined on node subsets in a graph. The optimization of such functions is often a non-trivial task given their combinatorial, bl…
Batch Calibration: Rethinking Calibration for In-Context Learning and Prompt Engineering
Han Zhou, Xingchen Wan, Lev Proleev +4
Prompting and in-context learning (ICL) have become efficient learning paradigms for large language models (LLMs). However, LLMs suffer from prompt brittleness and various bias fac…
UQE: A Query Engine for Unstructured Databases
Hanjun Dai, Bethany Yixin Wang, Xingchen Wan +7
Analytics on structured data is a mature field with many successful methods. However, most real world data exists in unstructured form, such as images and conversations. We investi…