67 citations · 164 across the 27 of their papers we have counts for
27 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…
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…
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…
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…
Teach Better or Show Smarter? On Instructions and Exemplars in Automatic Prompt Optimization
Xingchen Wan, Ruoxi Sun, Hootan Nakhost +1
Large language models have demonstrated remarkable capabilities, but their performance is heavily reliant on effective prompt engineering. Automatic prompt optimization (APO) metho…
Fairer Preferences Elicit Improved Human-Aligned Large Language Model Judgments
Han Zhou, Xingchen Wan, Yinhong Liu +3
Large language models (LLMs) have shown promising abilities as cost-effective and reference-free evaluators for assessing language generation quality. In particular, pairwise LLM e…
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…
Survival of the Most Influential Prompts: Efficient Black-Box Prompt Search via Clustering and Pruning
Han Zhou, Xingchen Wan, Ivan Vulić +1
Prompt-based learning has been an effective paradigm for large pretrained language models (LLM), enabling few-shot or even zero-shot learning. Black-box prompt search has received…
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…
Adaptive Batch Sizes for Active Learning A Probabilistic Numerics Approach
Masaki Adachi, Satoshi Hayakawa, Martin Jørgensen +4
Active learning parallelization is widely used, but typically relies on fixing the batch size throughout experimentation. This fixed approach is inefficient because of a dynamic tr…
Bayesian Optimisation of Functions on Graphs
Xingchen Wan, Pierre Osselin, Henry Kenlay +3
The increasing availability of graph-structured data motivates the task of optimising over functions defined on the node set of graphs. Traditional graph search algorithms can be a…
Better Zero-Shot Reasoning with Self-Adaptive Prompting
Xingchen Wan, Ruoxi Sun, Hanjun Dai +2
Modern large language models (LLMs) have demonstrated impressive capabilities at sophisticated tasks, often through step-by-step reasoning similar to humans. This is made possible…
SimSC: A Simple Framework for Semantic Correspondence with Temperature Learning
Xinghui Li, Kai Han, Xingchen Wan +1
We propose SimSC, a remarkably simple framework, to address the problem of semantic matching only based on the feature backbone. We discover that when fine-tuning ImageNet pre-trai…
Universal Self-Adaptive Prompting
Xingchen Wan, Ruoxi Sun, Hootan Nakhost +4
A hallmark of modern large language models (LLMs) is their impressive general zero-shot and few-shot abilities, often elicited through in-context learning (ICL) via prompting. Howe…
Working Memory Capacity of ChatGPT: An Empirical Study
Dongyu Gong, Xingchen Wan, Dingmin Wang
Working memory is a critical aspect of both human intelligence and artificial intelligence, serving as a workspace for the temporary storage and manipulation of information. In thi…
Bayesian Quadrature for Neural Ensemble Search
Saad Hamid, Xingchen Wan, Martin Jørgensen +2
Ensembling can improve the performance of Neural Networks, but existing approaches struggle when the architecture likelihood surface has dispersed, narrow peaks. Furthermore, exist…
AutoPEFT: Automatic Configuration Search for Parameter-Efficient Fine-Tuning
Han Zhou, Xingchen Wan, Ivan Vulić +1
Large pretrained language models are widely used in downstream NLP tasks via task-specific fine-tuning, but such procedures can be costly. Recently, Parameter-Efficient Fine-Tuning…
Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization
Samuel Daulton, Xingchen Wan, David Eriksson +3
Optimizing expensive-to-evaluate black-box functions of discrete (and potentially continuous) design parameters is a ubiquitous problem in scientific and engineering applications.…
Bayesian Generational Population-Based Training
Xingchen Wan, Cong Lu, Jack Parker-Holder +4
Reinforcement learning (RL) offers the potential for training generally capable agents that can interact autonomously in the real world. However, one key limitation is the brittlen…
On Redundancy and Diversity in Cell-based Neural Architecture Search
Xingchen Wan, Binxin Ru, Pedro M. Esperança +1
Searching for the architecture cells is a dominant paradigm in NAS. However, little attention has been devoted to the analysis of the cell-based search spaces even though it is hig…
BOiLS: Bayesian Optimisation for Logic Synthesis
Antoine Grosnit, Cedric Malherbe, Rasul Tutunov +3
Optimising the quality-of-results (QoR) of circuits during logic synthesis is a formidable challenge necessitating the exploration of exponentially sized search spaces. While exper…
Approximate Neural Architecture Search via Operation Distribution Learning
Xingchen Wan, Binxin Ru, Pedro M. Esperança +1
The standard paradigm in Neural Architecture Search (NAS) is to search for a fully deterministic architecture with specific operations and connections. In this work, we instead pro…
Adversarial Attacks on Graph Classification via Bayesian Optimisation
Xingchen Wan, Henry Kenlay, Binxin Ru +3
Graph neural networks, a popular class of models effective in a wide range of graph-based learning tasks, have been shown to be vulnerable to adversarial attacks. While the majorit…
Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces
Xingchen Wan, Vu Nguyen, Huong Ha +3
High-dimensional black-box optimisation remains an important yet notoriously challenging problem. Despite the success of Bayesian optimisation methods on continuous domains, domain…