A surprisingly effective way to predict token importance in LLM prompts
We explored a novel method to gauge the significance of tokens in prompts given to large language models, without needing direct model access. Essentially, we just did an ablation study on the prompt using cosine similarity of the embeddings as the measure. We got surprisingly promising results when comparing this really simple approach to integrated gradients. Curious to hear thoughts from the community!
In plain words
This project presents a method for identifying which tokens matter most in prompts sent to large language models, without requiring access to the model itself. It uses ablation testing combined with embedding cosine similarity to measure token importance, and compares favorably against integrated gradients, a more complex alternative approach. The tool is designed for researchers and developers working with LLMs who need to understand prompt behavior and optimize input effectiveness.
written from the facts on this page · September 2026
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