1 citations · 1 across the 9 of their papers we have counts for
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Position: Vector Prompt Interfaces Should Be Exposed to Enable Customization of Large Language Models
Liangwei Yang, Shiyu Wang, Haolin Chen +12
As large language models (LLMs) transition from research prototypes to real-world systems, customization has emerged as a central bottleneck. While text prompts can already customi…
Prompt Optimization Via Diffusion Language Models
Shiyu Wang, Haolin Chen, Liangwei Yang +8
We propose a diffusion-based framework for prompt optimization that leverages Diffusion Language Models (DLMs) to iteratively refine system prompts through masked denoising. By con…
ToolLibGen: Scalable Automatic Tool Creation and Aggregation for LLM Reasoning
Murong Yue, Zhiwei Liu, Liangwei Yang +8
Large Language Models (LLMs) equipped with external tools have demonstrated enhanced performance on complex reasoning tasks. The widespread adoption of this tool-augmented reasonin…
Promptomatix: An Automatic Prompt Optimization Framework for Large Language Models
Rithesh Murthy, Ming Zhu, Liangwei Yang +6
Large Language Models (LLMs) perform best with well-crafted prompts, yet prompt engineering remains manual, inconsistent, and inaccessible to non-experts. We introduce Promptomatix…
Entropy-Based Block Pruning for Efficient Large Language Models
Liangwei Yang, Yuhui Xu, Juntao Tan +5
As large language models continue to scale, their growing computational and storage demands pose significant challenges for real-world deployment. In this work, we investigate redu…