4 posts tagged with "machine learning"

Introduction​

As someone who works with vector databases daily, I've become accustomed to the conventional applications of Retrieval-Augmented Generation (RAG) in scenarios such as extracting information from dense user manuals, navigating complex code bases, or conducting in-depth legal research. These "talk to your documents" use cases, while impressive, often revolve around similar challenges across different datasets, which can become somewhat monotonous.

So, it was particularly refreshing when I came across the paper "Approaching Human-Level Forecasting with Language Models" by researchers Danny Halawi, Fred Zhang, Chen Yueh-Han, and Jacob Steinhardt from UC Berkeley. They propose a novel (at least to me) use of RAG: forecasting events!

Introduction​

Imagine a tool that can dive into real GitHub repositories to debug and fix issues automatically. That's SWE-agent for you, a project born at Princeton University, where language models like GPT-4 are turned into software engineering agents. These aren't just toys either; they've shown to resolve over 12% of software issues tested in the SWE-bench dataset. While 12% might not initially seem high, it represents a significant leap from the previous benchmark of just 3.79%. This achievement marks a considerable advancement in the field, underscoring the growing potential of AI to transform software development and maintenance.

My journey into SWE-agent began with curiosity and a bit of a stumble. I wanted to set up a local dev environment to study the model's inference step but the project doesn't say how to set up such an environment! It's a familiar story in open-source projects, especially those with roots in academia. I encountered a mix of excitement and frustration, reading through the setup instructions in the README and realizing the commitment needed to even start. And I wasn't the only one feeling this way; a community issue highlighted similar struggles.

Deciding to lean into the challenge, I saw an opportunity to simplify this for everyone. While the official setup process is being refined, I've put together an alternative guide to get you up and running with SWE-agent in a local dev environment using dev containers.

All you need is Docker and VS Code!

Introduction​

Retrieval Augmented Generation (RAG) typically finds its place in enhancing document-based question answering (QnA), effectively leveraging extensive databases to provide contextually relevant information for Large Language Models (LLMs) to formulate precise answers. Traditionally, when looking to boost the reasoning capabilities of LLMs, the go-to strategy has been fine-tuning these models with additional data. However, fine-tuning is not only resource-intensive but also presents scalability challenges.

Interestingly, RAG could potentially offer a more efficient pathway to enhance LLMs' reasoning skills without the hefty costs of fine-tuning. This intriguing premise is explored in depth in Enhancing LLM Intelligence with ARM-RAG: Auxiliary Rationale Memory for Retrieval Augmented Generation by Eric Melz, which proposes a novel use of RAG beyond its conventional application, aiming to refine and expand the problem-solving prowess of LLMs efficiently.

Introduction​

When it comes to bestowing Large Language Models (LLMs) with long-term memory, the prevalent approach often involves a Retrieval Augmented Generation (RAG) solution, with vector databases acting as the storage mechanism for the long-term memory. This begs the question: Can we achieve the same results without vector databases?

Enter "RecallM: An Adaptable Memory Mechanism with Temporal Understanding for Large Language Models" by Brandon Kynoch, Hugo Latapie, and Dwane van der Sluis. This paper proposes the use of an automatically constructed knowledge graph as the backbone of long-term memory for LLMs.