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20232025
most citedIgniting Language Intelligence: The Hitchhiker's Guide From Chain-of-Thought Reasoning to Language Agents

11 citations · 15 across the 8 of their papers we have counts for

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6 papers · 1 filter

cs.CL2025★ 1 cited

The Hunger Game Debate: On the Emergence of Over-Competition in Multi-Agent Systems

Xinbei Ma, Ruotian Ma, Xingyu Chen +14

LLM-based multi-agent systems demonstrate great potential for tackling complex problems, but how competition shapes their behavior remains underexplored. This paper investigates th…

cs.CL2024

Mitigating Misleading Chain-of-Thought Reasoning with Selective Filtering

Yexin Wu, Zhuosheng Zhang, Hai Zhao

Large language models have manifested remarkable capabilities by leveraging chain-of-thought (CoT) reasoning techniques to solve intricate questions through step-by-step reasoning…

cs.CL2024

CoCo-Agent: A Comprehensive Cognitive MLLM Agent for Smartphone GUI Automation

Xinbei Ma, Zhuosheng Zhang, Hai Zhao

Multimodal large language models (MLLMs) have shown remarkable potential as human-like autonomous language agents to interact with real-world environments, especially for graphical…

cs.CL2024

GLaPE: Gold Label-agnostic Prompt Evaluation and Optimization for Large Language Model

Xuanchang Zhang, Zhuosheng Zhang, Hai Zhao

Despite the rapid progress of large language models (LLMs), their task performance remains sensitive to prompt design. Recent studies have explored leveraging the LLM itself as an…

cs.CL2023★ 11 cited

Igniting Language Intelligence: The Hitchhiker's Guide From Chain-of-Thought Reasoning to Language Agents

Zhuosheng Zhang, Yao Yao, Aston Zhang +8

Large language models (LLMs) have dramatically enhanced the field of language intelligence, as demonstrably evidenced by their formidable empirical performance across a spectrum of…

cs.CL2023★ 3 cited

Generalizable Chain-of-Thought Prompting in Mixed-task Scenarios with Large Language Models

Anni Zou, Zhuosheng Zhang, Hai Zhao +1

Large language models (LLMs) have unveiled remarkable reasoning capabilities by exploiting chain-of-thought (CoT) prompting, which generates intermediate reasoning chains to serve…