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Lingpeng Kong

13 papers hereh-index 7351 citations13 works total

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

author position
  • middle author4
  • last author8

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

fields
  • cs.AI8
  • cs.CL4
  • cs.LG1
same name
  • Lingpeng Kong — 73 papers, h 41
  • Lingpeng Kong — 20 papers, h 11
  • Lingpeng Kong — 18 papers, h 11
  • Lingpeng Kong — 11 papers, h 7
  • Lingpeng Kong — 11 papers, h 5
  • Lingpeng Kong — 8 papers, h 5

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 citedAgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agents

8 citations · 10 across the 12 of their papers we have counts for

collaborators
Showing 2024Show all

4 papers · 1 filter

cs.CL2024★ 1 cited

A Survey on Large Language Model-Based Social Agents in Game-Theoretic Scenarios

Xiachong Feng, Longxu Dou, Ella Li +5

Game-theoretic scenarios have become pivotal in evaluating the social intelligence of Large Language Model (LLM)-based social agents. While numerous studies have explored these age…

cs.AI2024

Non-myopic Generation of Language Models for Reasoning and Planning

Chang Ma, Haiteng Zhao, Junlei Zhang +2

Large Language Models have demonstrated remarkable abilities in reasoning and planning by breaking down complex problems into sequential steps. Despite their success in various dom…

cs.AI2024

Empowering Large Language Model Agents through Action Learning

Haiteng Zhao, Chang Ma, Guoyin Wang +5

Large Language Model (LLM) Agents have recently garnered increasing interest yet they are limited in their ability to learn from trial and error, a key element of intelligent behav…

cs.CL2024★ 8 cited

AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agents

Chang Ma, Junlei Zhang, Zhihao Zhu +6

Evaluating Large Language Models (LLMs) as general-purpose agents is essential for understanding their capabilities and facilitating their integration into practical applications.…

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