activity
20202026
most citedRe-thinking Knowledge Graph Completion Evaluation from an Information Retrieval Perspective

13 citations · 23 across the 16 of their papers we have counts for

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Showing 2025Show all

7 papers · 1 filter

cs.CL2025

AI-Salesman: Towards Reliable Large Language Model Driven Telemarketing

Qingyu Zhang, Chunlei Xin, Xuanang Chen +7

Goal-driven persuasive dialogue, exemplified by applications like telemarketing, requires sophisticated multi-turn planning and strict factual faithfulness, which remains a signifi…

cs.CL2025

CoCoNUTS: Concentrating on Content while Neglecting Uninformative Textual Styles for AI-Generated Peer Review Detection

Yihan Chen, Jiawei Chen, Guozhao Mo +4

The growing integration of large language models (LLMs) into the peer review process presents potential risks to the fairness and reliability of scholarly evaluation. While LLMs of…

cs.IR2025

PaperRegister: Boosting Flexible-grained Paper Search via Hierarchical Register Indexing

Zhuoqun Li, Xuanang Chen, Hongyu Lin +5

As researchers delve more deeply into their work, paper search requirements may become more flexible, sometimes involving specific details such as module configuration rather than…

cs.AI2025

LiveMCPBench: Can Agents Navigate an Ocean of MCP Tools?

Guozhao Mo, Wenliang Zhong, Jiawei Chen +7

Model Context Protocol (MCP) has become a key infrastructure for connecting LLMs with external tools, scaling to 10,000+ MCP servers with diverse tools. Unfortunately, there is sti…

cs.CL2025

Beyond Isolated Dots: Benchmarking Structured Table Construction as Deep Knowledge Extraction

Tianyun Zhong, Guozhao Mo, Yanjiang Liu +9

With the emergence of large language models (LLMs), there is an expectation that LLMs can effectively extract explicit information from complex real-world documents (e.g., papers,…

cs.SE2025

Across Programming Language Silos: A Study on Cross-Lingual Retrieval-augmented Code Generation

Qiming Zhu, Jialun Cao, Xuanang Chen +6

Current research on large language models (LLMs) with retrieval-augmented code generation (RACG) has largely focused on single-language settings, leaving their cross-lingual effect…

cs.AI2025★ 1 cited

DeepSolution: Boosting Complex Engineering Solution Design via Tree-based Exploration and Bi-point Thinking

Zhuoqun Li, Haiyang Yu, Xuanang Chen +6

Designing solutions for complex engineering challenges is crucial in human production activities. However, previous research in the retrieval-augmented generation (RAG) field has n…