# LLM In-Context Recall is Prompt Dependent

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This new [paper by Machlab and Battle (2024)](https://arxiv.org/abs/2404.08865) analyzes the in-context recall performance of different LLMs using several needle-in-a-haystack tests.

It shows that various LLMs recall facts at different lengths and placement depths. It finds that a model's recall performance is significantly affected by small changes in the prompt. 

!["Needle In the HayStack Performance"](../../img/research/haystack-performance.png)
*Source: [Machlab and Battle (2024)](https://arxiv.org/abs/2404.08865)*


In addition, the interplay between prompt content and training data can degrade the response quality.

The recall ability of a model can be improved with increasing size, enhancing the attention mechanism, trying different training strategies, and applying fine-tuning.

Important practical tip from the paper: "Continued evaluation will further inform the selection of LLMs for individual use cases, maximizing their impact and efficiency in real-world applications as the technology continues to evolve."

The takeaways from this paper are the importance of careful prompt design, establishing a continuous evaluation protocol, and testing different model enhancement strategies to improve recall and utility.
