Exploring Prompt Compression with LLMLingua: Balancing Efficiency and Quality
Tags: llm, ai, promptengineering, llmlingua
Published: 2024-04-05 01:56:32

For the last year  I have working on integrating  Large Language Models (LLMs) into different applications.  I've been fascinated by how powerful they are but they have also had their limitations. One of the challenges I've encountered, is the trade-off between prompt length and performance. Longer prompts often yield better results but come at the cost of increased latency and computational resources. This is where LLMLingua comes in - a tool designed to compress prompts while maintaining the quality of the generated responses.


Having worked with various LLMs, I was eager to explore how LLMLingua could be used to solve issues with large prompts and limited token size with LLMs. My main concern was whether the compressed prompts would still produce high-quality results. To address this, I designed an experiment to evaluate the impact of different prompt compression techniques and ratios on the similarity between the original and compressed prompts, as well as the similarity between the responses generated by the LLM.

For this experiment, I used an L4 GPU with 24 GB and configured LLMLingua to use the LLAMA 7B LLM. I prepared a set of prompts with varying lengths (2k, 4k, and 8k tokens) and applied different compression techniques: Context Filtering, Sentence Filtering, Token Level Filtering, and a combination of all three. I also tested different compression ratios: 90%, 55%, and 30% of the original prompt length.

To measure the similarity between the original and compressed prompts, I used OpenAI Vector Embeddings and calculated the cosine similarity. I then sent both the compressed and original prompts to Claude Opus, and compared the generated responses using the same embedding and similarity metrics.

Results:
The results of the experiment are summarized in the following tables:

## Prompt Similarity:

| Ratio	| All	| Content Filtering | Sentence Filtering |	Token Filtering|
|-----------|--------|----------------|-----------------|-------------------
| 30.0%	| 0.89	| 0.90	| 0.94	| 0.86 |
| 55.0%	| 0.93	| 0.95	| 0.97	| 0.90 |
| 90.0%	| 0.96	| 0.97	| 0.90	| 0.95 |


## Response Similarity:

| Ratio	| All	| Content Filtering | Sentence Filtering |	Token Filtering|
|-----------|--------|----------------|-----------------|-------------------
|30.0%	| 0.87	| 0.86	| 0.94	| 0.90 |
| 55.0%	| 0.89	| 0.93	| 0.96	| 0.96 |
| 90.0%	| 0.95	| 0.96	| 0.96	| 0.98 |


The experiment results demonstrate that LLMLingua is an effective tool for prompt compression, capable of maintaining high similarity between the original and compressed prompts, as well as the generated responses. Even at aggressive compression ratios of 30%, the compressed prompts retained a high degree of similarity to the original prompts, with the best-performing technique being Sentence Filtering.

Interestingly, the similarity between the responses generated from the compressed and original prompts was also high, suggesting that the compressed prompts maintained the essential information needed for the LLM to produce similar outputs. These findings highlight the potential of LLMLingua to optimize prompt efficiency without compromising the quality of the generated responses, making it a valuable addition to any LLM developer's toolkit.