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Xuezhi Wang

4 papers here

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

author position
  • first author1
  • middle author1

Across the 2 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CL4
ORCID 0000-0001-7592-2358
same name
  • Xuezhi Wang — 16 papers, h 12
  • Xuezhi Wang — 15 papers, h 19
  • Xuezhi Wang — 9 papers, h 12
  • Xuezhi Wang — 7 papers, h 9
  • Xuezhi Wang — 5 papers, h 12
  • Xuezhi Wang — 3 papers, h 8

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

activity
20212023
most citedGemini: A Family of Highly Capable Multimodal Models

843 citations · 903 across the 4 of their papers we have counts for

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2023★ 843 cited

Gemini: A Family of Highly Capable Multimodal Models

Gemini Team, Rohan Anil, Sebastian Borgeaud +1340

This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding. The Gemini family consist…

cs.CL2022★ 31 cited

Recitation-Augmented Language Models

Zhiqing Sun, Xuezhi Wang, Yi Tay +2

We propose a new paradigm to help Large Language Models (LLMs) generate more accurate factual knowledge without retrieving from an external corpus, called RECITation-augmented gEne…

cs.CL2022★ 29 cited

Rationale-Augmented Ensembles in Language Models

Xuezhi Wang, Jason Wei, Dale Schuurmans +3

Recent research has shown that rationales, or step-by-step chains of thought, can be used to improve performance in multi-step reasoning tasks. We reconsider rationale-augmented pr…

cs.CL2021

Measure and Improve Robustness in NLP Models: A Survey

Xuezhi Wang, Haohan Wang, Diyi Yang

As NLP models achieved state-of-the-art performances over benchmarks and gained wide applications, it has been increasingly important to ensure the safe deployment of these models…

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