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20232026
most citedLLaMA Beyond English: An Empirical Study on Language Capability Transfer

6 citations · 9 across the 16 of their papers we have counts for

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9 papers · 1 filter

cs.CL2026

Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models

Hengyuan Zhang, Zhihao Zhang, Mingyang Wang +26

Mechanistic Interpretability (MI) has emerged as a vital approach to demystify the opaque decision-making of Large Language Models (LLMs). However, existing reviews primarily treat…

cs.CL2025

AgentPRM: Process Reward Models for LLM Agents via Step-Wise Promise and Progress

Zhiheng Xi, Chenyang Liao, Guanyu Li +12

Despite rapid development, large language models (LLMs) still encounter challenges in multi-turn decision-making tasks (i.e., agent tasks) like web shopping and browser navigation,…

cs.CL2025

LLMEval-Fair: A Large-Scale Longitudinal Study on Robust and Fair Evaluation of Large Language Models

Ming Zhang, Yujiong Shen, Jingyi Deng +19

Existing evaluation of Large Language Models (LLMs) on static benchmarks is vulnerable to data contamination and leaderboard overfitting, critical issues that obscure true model ca…

cs.CL2025

Why Reinforcement Fine-Tuning Enables MLLMs Preserve Prior Knowledge Better: A Data Perspective

Zhihao Zhang, Qiaole Dong, Qi Zhang +12

Post-training algorithms such as Supervised Fine-Tuning (SFT) and Reinforcement Fine-Tuning (RFT) are widely used to adapt (multimodal) large language models to downstream tasks. W…

cs.CL2025

What Makes a Good Speech Tokenizer for LLM-Centric Speech Generation? A Systematic Study

Xiaoran Fan, Zhichao Sun, Yangfan Gao +19

Speech-language models (SLMs) offer a promising path toward unifying speech and text understanding and generation. However, challenges remain in achieving effective cross-modal ali…

cs.CL2025

PFDial: A Structured Dialogue Instruction Fine-tuning Method Based on UML Flowcharts

Ming Zhang, Yuhui Wang, Yujiong Shen +16

Process-driven dialogue systems, which operate under strict predefined process constraints, are essential in customer service and equipment maintenance scenarios. Although Large La…

cs.CL2024★ 1 cited

Unveiling Linguistic Regions in Large Language Models

Zhihao Zhang, Jun Zhao, Qi Zhang +2

Large Language Models (LLMs) have demonstrated considerable cross-lingual alignment and generalization ability. Current research primarily focuses on improving LLMs' cross-lingual…

cs.CL2024★ 6 cited

LLaMA Beyond English: An Empirical Study on Language Capability Transfer

Jun Zhao, Zhihao Zhang, Luhui Gao +3

In recent times, substantial advancements have been witnessed in large language models (LLMs), exemplified by ChatGPT, showcasing remarkable proficiency across a range of complex t…

cs.CL2023★ 2 cited

Unveiling A Core Linguistic Region in Large Language Models

Jun Zhao, Zhihao Zhang, Yide Ma +4

Brain localization, which describes the association between specific regions of the brain and their corresponding functions, is widely accepted in the field of cognitive science as…