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20242026
most citedLarge Language Model Sourcing: A Survey

1 citations · 1 across the 5 of their papers we have counts for

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cs.CL2026

MatchTIR: Fine-Grained Supervision for Tool-Integrated Reasoning via Bipartite Matching

Changle Qu, Sunhao Dai, Hengyi Cai +3

Tool-Integrated Reasoning (TIR) empowers large language models (LLMs) to tackle complex tasks by interleaving reasoning steps with external tool interactions. However, existing rei…

cs.CL20251 cited

Large Language Model Sourcing: A Survey

Liang Pang, Jia Gu, Sunhao Dai +7

Due to the black-box nature of large language models (LLMs) and the realism of their generated content, issues such as hallucinations, bias, unfairness, and copyright infringement…

cs.CL2025

Length-Induced Embedding Collapse in PLM-based Models

Yuqi Zhou, Sunhao Dai, Zhanshuo Cao +2

Text embeddings from PLM-based models enable a wide range of applications, yet their performance often degrades on longer texts. In this paper, we introduce a phenomenon we call Le…

cs.CL2025

GUI-G1: Understanding R1-Zero-Like Training for Visual Grounding in GUI Agents

Yuqi Zhou, Sunhao Dai, Shuai Wang +3

Recent Graphical User Interface (GUI) agents replicate the R1-Zero paradigm, coupling online Reinforcement Learning (RL) with explicit chain-of-thought reasoning prior to object gr…

cs.CL2025

Perplexity Trap: PLM-Based Retrievers Overrate Low Perplexity Documents

Haoyu Wang, Sunhao Dai, Haiyuan Zhao +6

Previous studies have found that PLM-based retrieval models exhibit a preference for LLM-generated content, assigning higher relevance scores to these documents even when their sem…

cs.CL2025

From Exploration to Mastery: Enabling LLMs to Master Tools via Self-Driven Interactions

Changle Qu, Sunhao Dai, Xiaochi Wei +5

Tool learning enables Large Language Models (LLMs) to interact with external environments by invoking tools, serving as an effective strategy to mitigate the limitations inherent i…