Prompt Compression in Diffusion Large Language Models: Evaluating LLMLingua-2 on LLaDA
📰 ArXiv cs.AI
Learn how to apply prompt compression in diffusion large language models to reduce inference cost and context length, and evaluate its effectiveness on LLaDA using LLMLingua-2
Action Steps
- Apply prompt compression to diffusion large language models using LLMLingua-2
- Evaluate compression performance on benchmarks like GSM8K, DUC2004, and ShareGPT
- Compare the results of compressed and uncompressed prompts on LLaDA
- Analyze the trade-off between compression ratio and model performance
- Optimize prompt compression for specific use cases and datasets
Who Needs to Know This
NLP engineers and researchers can benefit from this study to optimize their language models, while AI engineers can apply the findings to improve the efficiency of their models
Key Insight
💡 Prompt compression can be effectively applied to diffusion large language models, reducing inference cost and context length while maintaining model performance
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📚 Reduce inference cost & context length in large language models with prompt compression! 🤖 Evaluate LLMLingua-2 on LLaDA and improve model efficiency 🚀
Full Article
Title: Prompt Compression in Diffusion Large Language Models: Evaluating LLMLingua-2 on LLaDA
Abstract:
arXiv:2605.17932v1 Announce Type: cross Abstract: Prompt compression reduces inference cost and context length in large language models, but prior evaluations focus primarily on autoregressive architectures. This study investigates whether prompt compression transfers effectively to diffusion large language models (DLLMs) using LLMLingua-2, specifically the 8B-parameter DLLM LLaDA. We evaluate compression performance on GSM8K, DUC2004, and ShareGPT using 250 prompts per dataset at an approximate
Abstract:
arXiv:2605.17932v1 Announce Type: cross Abstract: Prompt compression reduces inference cost and context length in large language models, but prior evaluations focus primarily on autoregressive architectures. This study investigates whether prompt compression transfers effectively to diffusion large language models (DLLMs) using LLMLingua-2, specifically the 8B-parameter DLLM LLaDA. We evaluate compression performance on GSM8K, DUC2004, and ShareGPT using 250 prompts per dataset at an approximate
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