LLM: Decoding Pre-Filling, Decoding, and KV Cache

The Crucial Role of Pre-filling in LLMs

In the functioning of language models (LLMs), pre-filling plays a fundamental role. When a model receives a prompt, it uses an attention mechanism to identify the most relevant parts of the input. This process allows the model to focus on key words or phrases that will influence the final outcome.

Decoding: The Response Generation Phase

After pre-filling, the model enters the decoding phase. It is at this moment that the output is generated, based on the pre-filled information. Several factors influence decoding, including the length of the sequence and generation parameters such as temperature and top-k sampling. These elements determine the creativity and diversity of the produced responses.

Optimizing Decoding with KV Cache

The KV (key-value) cache is a technique that enhances the efficiency of decoding in LLMs. By storing the keys and values from previous steps, the model avoids recalculating this information at each iteration. This reduces processing time and optimizes overall performance, especially when generating long sequences.

Take the example of the prompt: "Today's weather is so...". Thanks to pre-filling, decoding, and the use of the KV cache, the model can quickly and efficiently produce a relevant and contextual response.