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From the 1 of 33 linked papers with an AI index.

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20242026
most citedA Survey of Data Agents: Emerging Paradigm or Overstated Hype?

1 citations · 1 across the 12 of their papers we have counts for

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cs.CL2026

Mitigating Judgment Preference Bias in Large Language Models through Group-Based Polling

Shuliang Liu, Zhipeng Xu, Zhenghao Liu +6

Large Language Models (LLMs) as automatic evaluators, commonly referred to as LLM-as-a-Judge, have also attracted growing attention. This approach plays a vital role in aligning LL…

cs.CL2026

HIPPO: Enhancing the Table Understanding Capability of LLMs through Hybrid-Modal Preference Optimization

Haolan Wang, Zhenghao Liu, Xinze Li +7

Tabular data contains rich structural semantics and plays a crucial role in organizing and manipulating information. Recent methods employ Multi-modal Large Language Models (MLLMs)…

cs.CL2026

PilotRL: Training Language Model Agents via Global Planning-Guided Progressive Reinforcement Learning

Keer Lu, Chong Chen, Xili Wang +3

Large Language Models (LLMs) have shown remarkable advancements in tackling agent-oriented tasks. Despite their potential, existing work faces challenges when deploying LLMs in age…

cs.CL2025

TableRAG: A Retrieval Augmented Generation Framework for Heterogeneous Document Reasoning

Xiaohan Yu, Pu Jian, Chong Chen

Retrieval-Augmented Generation (RAG) has demonstrated considerable effectiveness in open-domain question answering. However, when applied to heterogeneous documents, comprising bot…

cs.CL2025

Can LLMs be Good Graph Judge for Knowledge Graph Construction?

Haoyu Huang, Chong Chen, Zeang Sheng +2

In real-world scenarios, most of the data obtained from the information retrieval (IR) system is unstructured. Converting natural language sentences into structured Knowledge Graph…

cs.CL2025

PanguIR Technical Report for NTCIR-18 AEOLLM Task

Lang Mei, Chong Chen, Jiaxin Mao

As large language models (LLMs) gain widespread attention in both academia and industry, it becomes increasingly critical and challenging to effectively evaluate their capabilities…