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researcher

Junlin Wang

Duke University

26 papers hereh-index 11891 citations30 works total

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

author position
  • first author10
  • middle author14

Across the 24 of 26 papers where every author was matched, so the position is known.

fields
  • cs.CL12
  • cs.AI5
  • cs.LG2
  • q-fin.ST2
  • cs.CR1
  • cs.CV1
affiliations
  • Duke University
same name
  • Junlin Wang — 12 papers, h 10
  • Junlin Wang — 4 papers, h 2
  • Junlin Wang — 3 papers, h 2
  • Junlin Wang — 3 papers, h 2
  • Junlin Wang — 2 papers, h 1
  • Junlin Wang — 1 paper, 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

activity
20192026
most citedMixture-of-Agents Enhances Large Language Model Capabilities

25 citations · 56 across the 25 of their papers we have counts for

collaborators
Showing 2024 · cs.CLShow all

4 papers · 2 filters

cs.CL2024★ 1 cited

How Well Do LLMs Identify Cultural Unity in Diversity?

Jialin Li, Junli Wang, Junjie Hu +1

Much work on the cultural awareness of large language models (LLMs) focuses on the models' sensitivity to geo-cultural diversity. However, in addition to cross-cultural differences…

cs.CL2024★ 1 cited

Reasoning in Token Economies: Budget-Aware Evaluation of LLM Reasoning Strategies

Junlin Wang, Siddhartha Jain, Dejiao Zhang +3

A diverse array of reasoning strategies has been proposed to elicit the capabilities of large language models. However, in this paper, we point out that traditional evaluations whi…

cs.CL2024★ 25 cited

Mixture-of-Agents Enhances Large Language Model Capabilities

Junlin Wang, Jue Wang, Ben Athiwaratkun +2

Recent advances in large language models (LLMs) demonstrate substantial capabilities in natural language understanding and generation tasks. With the growing number of LLMs, how to…

cs.CL2024

ReCaLL: Membership Inference via Relative Conditional Log-Likelihoods

Roy Xie, Junlin Wang, Ruomin Huang +5

The rapid scaling of large language models (LLMs) has raised concerns about the transparency and fair use of the data used in their pretraining. Detecting such content is challengi…

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