activity
20242026
most citedCOIG-CQIA: Quality is All You Need for Chinese Instruction Fine-tuning

5 citations · 23 across the 14 of their papers we have counts for

collaborators
Showing cs.CLShow all

11 papers · 1 filter

cs.CL2026

PLOT: Enhancing Preference Learning via Optimal Transport

Liang Zhu, Yuelin Bai, Xiankun Ren +6

Preference learning in Large Language Models (LLMs) has advanced significantly, yet existing methods remain limited by modest performance gains, high computational costs, hyperpara…

cs.CL2026

DEFT: Distribution-guided Efficient Fine-Tuning for Human Alignment

Liang Zhu, Feiteng Fang, Yuelin Bai +4

Reinforcement Learning from Human Feedback (RLHF), using algorithms like Proximal Policy Optimization (PPO), aligns Large Language Models (LLMs) with human values but is costly and…

cs.CL2025★ 4 cited

SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines

P Team, Xinrun Du, Yifan Yao +94

Large language models (LLMs) have demonstrated remarkable proficiency in mainstream academic disciplines such as mathematics, physics, and computer science. However, human knowledg…

cs.CL2024

MAmmoTH-VL: Eliciting Multimodal Reasoning with Instruction Tuning at Scale

Jarvis Guo, Tuney Zheng, Yuelin Bai +7

Open-source multimodal large language models (MLLMs) have shown significant potential in a broad range of multimodal tasks. However, their reasoning capabilities remain constrained…

cs.CL2024

Can MLLMs Understand the Deep Implication Behind Chinese Images?

Chenhao Zhang, Xi Feng, Yuelin Bai +18

As the capabilities of Multimodal Large Language Models (MLLMs) continue to improve, the need for higher-order capability evaluation of MLLMs is increasing. However, there is a lac…

cs.CL2024

Ruler: A Model-Agnostic Method to Control Generated Length for Large Language Models

Jiaming Li, Lei Zhang, Yunshui Li +5

The instruction-following ability of large language models enables humans to interact with AI agents in a natural way. However, when required to generate responses of a specific le…

cs.CL2024★ 4 cited

DeliLaw: A Chinese Legal Counselling System Based on a Large Language Model

Nan Xie, Yuelin Bai, Hengyuan Gao +7

Traditional legal retrieval systems designed to retrieve legal documents, statutes, precedents, and other legal information are unable to give satisfactory answers due to lack of s…

cs.CL2024

II-Bench: An Image Implication Understanding Benchmark for Multimodal Large Language Models

Ziqiang Liu, Feiteng Fang, Xi Feng +23

The rapid advancements in the development of multimodal large language models (MLLMs) have consistently led to new breakthroughs on various benchmarks. In response, numerous challe…

cs.CL2024★ 2 cited

MAP-Neo: Highly Capable and Transparent Bilingual Large Language Model Series

Ge Zhang, Scott Qu, Jiaheng Liu +42

Large Language Models (LLMs) have made great strides in recent years to achieve unprecedented performance across different tasks. However, due to commercial interest, the most comp…

cs.CL2024★ 5 cited

COIG-CQIA: Quality is All You Need for Chinese Instruction Fine-tuning

Yuelin Bai, Xinrun Du, Yiming Liang +19

Remarkable progress on English instruction tuning has facilitated the efficacy and reliability of large language models (LLMs). However, there remains a noticeable gap in instructi…

cs.CL2024★ 3 cited

MoZIP: A Multilingual Benchmark to Evaluate Large Language Models in Intellectual Property

Shiwen Ni, Minghuan Tan, Yuelin Bai +9

Large language models (LLMs) have demonstrated impressive performance in various natural language processing (NLP) tasks. However, there is limited understanding of how well LLMs p…