activity
20232026
most citedRetrieving Supporting Evidence for Generative Question Answering

62 citations · 66 across the 4 of their papers we have counts for

collaborators

5 papers

cs.IR2026

A Sequential Decision Framework for Evaluating LLM-Guided Search

Siqing Huo, Andrew Parry, Debasis Ganguly +2

Recent systems applying retrieval-augmented generation (RAG) and "deep research" iteratively generate queries, retrieve documents, and examine their contents, using the resulting i…

cs.CY2026

Evaluation of Large Language Models in Legal Applications: Challenges, Methods, and Future Directions

Yiran Hu, Huanghai Liu, Chong Wang +15

Large language models (LLMs) are being increasingly integrated into legal applications, including judicial decision support, legal practice assistance, and public-facing legal serv…

cs.CL2024

Assessing and Verifying Task Utility in LLM-Powered Applications

Negar Arabzadeh, Siqing Huo, Nikhil Mehta +5

The rapid development of Large Language Models (LLMs) has led to a surge in applications that facilitate collaboration among multiple agents, assisting humans in their daily tasks.…

cs.IR2023★ 62 cited

Retrieving Supporting Evidence for Generative Question Answering

Siqing Huo, Negar Arabzadeh, Charles L. A. Clarke

Current large language models (LLMs) can exhibit near-human levels of performance on many natural language-based tasks, including open-domain question answering. Unfortunately, at…

cs.IR2023★ 4 cited

Retrieving Supporting Evidence for LLMs Generated Answers

Siqing Huo, Negar Arabzadeh, Charles L. A. Clarke

Current large language models (LLMs) can exhibit near-human levels of performance on many natural language tasks, including open-domain question answering. Unfortunately, they also…