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20242026
most citedPAS: Data-Efficient Plug-and-Play Prompt Augmentation System

2 citations · 2 across the 12 of their papers we have counts for

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5 papers · 1 filter

cs.LG2024

Baichuan Alignment Technical Report

Mingan Lin, Fan Yang, Yanjun Shen +21

We introduce Baichuan Alignment, a detailed analysis of the alignment techniques employed in the Baichuan series of models. This represents the industry's first comprehensive accou…

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★ 2 cited

PAS: Data-Efficient Plug-and-Play Prompt Augmentation System

Miao Zheng, Hao Liang, Fan Yang +16

In recent years, the rise of Large Language Models (LLMs) has spurred a growing demand for plug-and-play AI systems. Among the various AI techniques, prompt engineering stands out…

cs.CV2024

MathScape: Benchmarking Multimodal Large Language Models in Real-World Mathematical Contexts

Hao Liang, Linzhuang Sun, Minxuan Zhou +7

With the rapid progress of Multimodal LLMs, evaluating their mathematical reasoning capabilities has become an increasingly important research direction. In particular, visual-text…

cs.CL2024

CFBench: A Comprehensive Constraints-Following Benchmark for LLMs

Tao Zhang, Chenglin Zhu, Yanjun Shen +10

The adeptness of Large Language Models (LLMs) in comprehending and following natural language instructions is critical for their deployment in sophisticated real-world applications…