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researcher

Zhuoran Li

7 papers hereh-index 4130 citations8 works total

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

author position
  • first author2
  • middle author3
  • last author1

Across the 6 of 7 papers where every author was matched, so the position is known.

fields
  • cs.CL4
  • cs.LG2
  • cs.CV1
same name
  • Zhuoran Li — 8 papers, h 5
  • Zhuoran Li — 7 papers, h 2
  • Zhuoran Li — 5 papers, h 3
  • Zhuoran Li — 4 papers, h 6
  • Zhuoran Li — 2 papers, h 3
  • Zhuoran Li — 2 papers, h 1

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
20242026
most citedHarnessing Multiple Large Language Models: A Survey on LLM Ensemble

5 citations · 5 across the 7 of their papers we have counts for

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2026

Same Evidence, Different Target: Decoding How Diagnostic Evidence Bears on Causal Questions from Language-Model States

Weiyi Kong, Zhuoran Li

The same diagnostic result can support or challenge one causal claim yet fail to address another when the claims concern different populations, outcomes, estimands, pathways, or id…

cs.CL2026

Enhancing Multilingual Reasoning via Steerable Model Merging

Zhuoran Li, Rui Xu, Jian Yang +8

Model merging is an effective technique for composing the capabilities of a multilingual model and a reasoning model. It has achieved promising generalization in multilingual reaso…

cs.CL2025

Scoring, Reasoning, and Selecting the Best! Ensembling Large Language Models via a Peer-Review Process

Zhijun Chen, Zeyu Ji, Qianren Mao +13

We propose LLM-PeerReview, an unsupervised LLM Ensemble method that selects the most ideal response from multiple LLM-generated candidates for each query, harnessing the collective…

cs.CL2025★ 5 cited

Harnessing Multiple Large Language Models: A Survey on LLM Ensemble

Zhijun Chen, Xiaodong Lu, Jingzheng Li +12

LLM Ensemble -- which involves the comprehensive use of multiple large language models (LLMs), each aimed at handling user queries during downstream inference, to benefit from thei…

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