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Xiaoming Shi

4 papers hereh-index 8375 citations12 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author3
  • middle author1

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CL4
same name
  • Xiaoming Shi — 5 papers, h 3
  • Xiaoming Shi — 5 papers, h 5
  • Xiaoming Shi — 4 papers, h 2
  • Xiaoming Shi — 4 papers, h 19
  • Xiaoming Shi — 3 papers, h 2
  • Xiaoming Shi — 2 papers, h 10

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedLanguage Models Can Improve Event Prediction by Few-Shot Abductive Reasoning

12 citations · 17 across the 4 of their papers we have counts for

collaborators

4 papers

cs.CL2023★ 2 cited

LLM-Mini-CEX: Automatic Evaluation of Large Language Model for Diagnostic Conversation

Xiaoming Shi, Jie Xu, Jinru Ding +9

There is an increasing interest in developing LLMs for medical diagnosis to improve diagnosis efficiency. Despite their alluring technological potential, there is no unified and co…

cs.CL2023★ 1 cited

MidMed: Towards Mixed-Type Dialogues for Medical Consultation

Xiaoming Shi, Zeming Liu, Chuan Wang +4

Most medical dialogue systems assume that patients have clear goals (medicine querying, surgical operation querying, etc.) before medical consultation. However, in many real scenar…

cs.CL2023★ 2 cited

MedGPTEval: A Dataset and Benchmark to Evaluate Responses of Large Language Models in Medicine

Jie Xu, Lu Lu, Sen Yang +10

METHODS: First, a set of evaluation criteria is designed based on a comprehensive literature review. Second, existing candidate criteria are optimized for using a Delphi method by…

cs.CL2023★ 12 cited

Language Models Can Improve Event Prediction by Few-Shot Abductive Reasoning

Xiaoming Shi, Siqiao Xue, Kangrui Wang +5

Large language models have shown astonishing performance on a wide range of reasoning tasks. In this paper, we investigate whether they could reason about real-world events and hel…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.