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Junjie Chen

13 papers hereh-index 81.1k citations15 works total

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

author position
  • first author6
  • middle author6

Across the 12 of 13 papers where every author was matched, so the position is known.

fields
  • cs.CL9
  • cs.IR2
  • cs.CV1
  • cs.CY1
same name
  • Junjie Chen — 51 papers, h 43
  • Junjie Chen — 12 papers, h 7
  • Junjie Chen — 11 papers, h 18
  • Junjie Chen — 11 papers, h 5
  • Junjie Chen — 10 papers, h 11
  • Junjie Chen — 8 papers, h 4

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 citedLLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods

38 citations · 45 across the 13 of their papers we have counts for

collaborators
Showing 2024Show all

4 papers · 1 filter

cs.CL2024★ 1 cited

LegalAgentBench: Evaluating LLM Agents in Legal Domain

Haitao Li, Junjie Chen, Jingli Yang +10

With the increasing intelligence and autonomy of LLM agents, their potential applications in the legal domain are becoming increasingly apparent. However, existing general-domain b…

cs.CL2024★ 38 cited

LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods

Haitao Li, Qian Dong, Junjie Chen +5

The rapid advancement of Large Language Models (LLMs) has driven their expanding application across various fields. One of the most promising applications is their role as evaluato…

cs.CL2024

CalibraEval: Calibrating Prediction Distribution to Mitigate Selection Bias in LLMs-as-Judges

Haitao Li, Junjie Chen, Qingyao Ai +4

The use of large language models (LLMs) as automated evaluation tools to assess the quality of generated natural language, known as LLMs-as-Judges, has demonstrated promising capab…

cs.CL2024

Auto-PRE: An Automatic and Cost-Efficient Peer-Review Framework for Language Generation Evaluation

Junjie Chen, Weihang Su, Zhumin Chu +9

The rapid development of large language models (LLMs) has highlighted the need for efficient and reliable methods to evaluate their performance. Traditional evaluation methods ofte…

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