most citedMed-R: Crafting Trustworthy LLM Physicians via Retrieval and Reasoning of Evidence-Based Medicine

4 citations · 5 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CL2025★ 4 cited

Med-R: Crafting Trustworthy LLM Physicians via Retrieval and Reasoning of Evidence-Based Medicine

Keer Lu, Zheng Liang, Da Pan +6

Large Language Models (LLMs) have exhibited remarkable capabilities in clinical scenarios. Despite their potential, existing works face challenges when applying LLMs to medical set…

cs.CL2024

VersaTune: An Efficient Data Composition Framework for Training Multi-Capability LLMs

Keer Lu, Keshi Zhao, Zhuoran Zhang +8

As demonstrated by the proprietary Large Language Models (LLMs) such as GPT and Claude series, LLMs have the potential to achieve remarkable proficiency across a wide range of doma…

cs.CL2024

DataSculpt: Crafting Data Landscapes for Long-Context LLMs through Multi-Objective Partitioning

Keer Lu, Xiaonan Nie, Zheng Liang +8

In recent years, Large Language Models (LLMs) have demonstrated significant improvements across a variety of tasks, one of which is the long-context capability. The key to improvin…

cs.CL2024

Facilitating Multi-turn Function Calling for LLMs via Compositional Instruction Tuning

Mingyang Chen, Haoze Sun, Tianpeng Li +7

Large Language Models (LLMs) have exhibited significant potential in performing diverse tasks, including the ability to call functions or use external tools to enhance their perfor…

cs.CL2024

FB-Bench: A Fine-Grained Multi-Task Benchmark for Evaluating LLMs' Responsiveness to Human Feedback

Youquan Li, Miao Zheng, Fan Yang +5

Human feedback is crucial in the interactions between humans and Large Language Models (LLMs). However, existing research primarily focuses on benchmarking LLMs in single-turn dial…

cs.CL2024

BaichuanSEED: Sharing the Potential of ExtensivE Data Collection and Deduplication by Introducing a Competitive Large Language Model Baseline

Guosheng Dong, Da Pan, Yiding Sun +17

The general capabilities of Large Language Models (LLM) highly rely on the composition and selection on extensive pretraining datasets, treated as commercial secrets by several ins…

cs.CL2024★ 1 cited

SysBench: Can Large Language Models Follow System Messages?

Yanzhao Qin, Tao Zhang, Yanjun Shen +8

Large Language Models (LLMs) have become instrumental across various applications, with the customization of these models to specific scenarios becoming increasingly critical. Syst…