June 2024 Summaries

The RAFT (Retrieval Augmentation Fine-Tuning) paper presents a method that improves retrieval augmented language models by fine-tuning them on domain-specific data. This approach allows the model to better utilize context from retrieved documents, leading to more accurate and relevant responses. RAFT is particularly useful in specialized domains where traditional document sources may not be effective. The authors demonstrate the effectiveness of RAFT through experiments on various question answering datasets, showing that it outperforms other methods, including GPT-3.5, in most cases.

This article discusses how to manage and monitor open source Language Learning Models (LLMs) applications using UbiOps and Arize. It highlights the benefits of open-source LLMs, such as Llama 3, Mistral, or Falcon, which can be customized easily compared to closed-source models like GPT-4. The article provides a step-by-step guide on deploying an open source LLM (llama-3-8b-instruct) to the cloud with UbiOps and logging prompt and response embeddings together with some metadata to Arize for monitoring purposes. It also explains how to set up a connection with Arize API client, calculate the embeddings using HuggingFace's embedding model, and log the embeddings to Arize. The article concludes by demonstrating how to inspect the results in Arize's platform.

In this paper, the authors propose a method to identify and interpret features in large language models (LLMs) using sparse autoencoders (SAEs). They demonstrate that these features can be used for various applications such as model editing, feature ablation, searching for specific features, and ensuring safety. The main takeaway from this paper is the potential of SAEs to provide a better understanding of LLMs' inner workings, which could lead to more robust and safer models in the future.