activity
20232026
most citedEvaluating Large Language Models: A Comprehensive Survey

63 citations · 63 across the 7 of their papers we have counts for

collaborators

9 papers

cs.CL2026

FDR: A Fine-Grained Full-Pipeline Reward Framework for DeepSearch Workflows

Bojian Xiong, Wentao Ding, Yujing Lu +11

With the widespread industrial deployment of Large Language Models (LLMs), DeepSearch has emerged as the dominant paradigm for resolving complex user queries. It typically operates…

cs.SE2026

CoRaCommit: A VS Code Extension for Commit Message Generation with Exemplar Retrieval

Chaoran Cai, Bo Xiong, Chong Wang +2

Commit messages are essential textual artifacts that describe the intent behind code changes, and play a critical role in version control, code review, and historical tracking. How…

cs.SE2026

Assessing Language Models for Salient Class Identification

Bo Xiong, Chaoran Cai, Zongen Ren +3

Code review requires reviewers to understand the core intent of code changes, which becomes difficult when a commit modifies multiple classes. In such commits, one or more primaril…

cs.AI2026

The Lattice Representation Hypothesis of Large Language Models

Bo Xiong

We propose the Lattice Representation Hypothesis of large language models: a symbolic backbone that grounds conceptual hierarchies and logical operations in embedding geometry. Our…

cs.CL2026

DEP: A Decentralized Large Language Model Evaluation Protocol

Jianxiang Peng, Junhao Li, Hongxiang Wang +15

With the rapid development of Large Language Models (LLMs), a large number of benchmarks have been proposed. However, most benchmarks lack unified evaluation standard and require t…

cs.CL2025

DecEx-RAG: Boosting Agentic Retrieval-Augmented Generation with Decision and Execution Optimization via Process Supervision

Yongqi Leng, Yikun Lei, Xikai Liu +7

Agentic Retrieval-Augmented Generation (Agentic RAG) enhances the processing capability for complex tasks through dynamic retrieval and adaptive workflows. Recent advances (e.g., S…

cs.CL2025

From Curated Data to Scalable Models: Continual Pre-training of Dense and MoE Large Language Models for Tibetan

Lei Yang, Leiyu Pan, Bojian Xiong +14

Large language models (LLMs) have achieved remarkable success across a wide range of natural language processing tasks, yet their performance remains heavily biased toward high-res…

cs.CL2024

FuxiTranyu: A Multilingual Large Language Model Trained with Balanced Data

Haoran Sun, Renren Jin, Shaoyang Xu +10

Large language models (LLMs) have demonstrated prowess in a wide range of tasks. However, many LLMs exhibit significant performance discrepancies between high- and low-resource lan…

cs.CL2023★ 63 cited

Evaluating Large Language Models: A Comprehensive Survey

Zishan Guo, Renren Jin, Chuang Liu +8

Large language models (LLMs) have demonstrated remarkable capabilities across a broad spectrum of tasks. They have attracted significant attention and been deployed in numerous dow…