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Kaijie Zhu

10 papers hereh-index 124.3k citations12 works total

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

author position
  • first author5
  • middle author5

Across the 10 of 10 papers where every author was matched, so the position is known.

fields
  • cs.CL5
  • cs.AI4
  • cs.LG1
same name
  • Kaijie Zhu — 7 papers, h 4
  • Kaijie Zhu — 4 papers, h 3
  • Kaijie Zhu — 3 papers, h 5
  • Kaijie Zhu — 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

most citedA Survey on Evaluation of Large Language Models

202 citations · 286 across the 10 of their papers we have counts for

collaborators
Showing cs.AIShow all

4 papers · 1 filter

cs.AI2023★ 7 cited

The Good, The Bad, and Why: Unveiling Emotions in Generative AI

Cheng Li, Jindong Wang, Yixuan Zhang +7

Emotion significantly impacts our daily behaviors and interactions. While recent generative AI models, such as large language models, have shown impressive performance in various t…

cs.AI2023★ 13 cited

PromptBench: A Unified Library for Evaluation of Large Language Models

Kaijie Zhu, Qinlin Zhao, Hao Chen +2

The evaluation of large language models (LLMs) is crucial to assess their performance and mitigate potential security risks. In this paper, we introduce PromptBench, a unified libr…

cs.AI2023★ 7 cited

CompeteAI: Understanding the Competition Dynamics in Large Language Model-based Agents

Qinlin Zhao, Jindong Wang, Yixuan Zhang +4

Large language models (LLMs) have been widely used as agents to complete different tasks, such as personal assistance or event planning. While most of the work has focused on coope…

cs.AI2023★ 1 cited

DyVal: Dynamic Evaluation of Large Language Models for Reasoning Tasks

Kaijie Zhu, Jiaao Chen, Jindong Wang +3

Large language models (LLMs) have achieved remarkable performance in various evaluation benchmarks. However, concerns are raised about potential data contamination in their conside…

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