Category: LLM

In today’s AI-driven world, Retrieval-Augmented Generation (RAG) is becoming an increasingly significant approach that combines the capabilities of information retrieval with the generative abilities of large language models (LLMs). This overcomes a number of limitations faced by traditional LLMs and provides more accurate and fact-based answers.

What is RAG?

RAG is not a single technology, but an entire umbrella of different components, designs, and domain-specific adaptations. A typical RAG system includes:

1. A data ingestion component: where data is processed, embedded, and stored as context documents in a vector database
2. A retrieval component: where context documents are retrieved and ranked for relevance to the query
3. Query component: where the prompt with the query is combined with the search results and sent to the LLM

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RouteLLM reduces your costs for using LLMs by 3.6 times.

It chooses whether to use a strong or weak LLM model depending on the complexity of the user’s query. This optimizes the balance between cost and quality of the response.

The Python library allows you to use this approach directly.

import os
from routellm.controller import Controller

os.environ["OPENAI_API_KEY"] = "sk-XXXXXX"
# Replace with your model provider, we use Anyscale's Mixtral here.
os.environ["ANYSCALE_API_KEY"] = "esecret_XXXXXX"

client = Controller(
  routers=["mf"],
  strong_model="gpt-4-1106-preview",
  weak_model="anyscale/mistralai/Mixtral-8x7B-Instruct-v0.1",
)

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Google DeepMind has developed a multi-pass online approach using reinforcement learning to enhance the self-correction capabilities of large language models (LLMs).

It has been shown that supervised fine-tuning (SFT) is ineffective for learning self-correction and faces a mismatch between training data and the model’s responses. To address this issue, a two-stage approach is proposed, which first optimizes self-correction behavior and then uses an additional reward to reinforce self-correction during training. This method relies entirely on data generated by the model itself.

When applied to the Gemini 1.0 Pro and 1.5 Flash models, it achieved record-breaking self-correction performance, improving the baseline models by 15.6% and 9.1%, respectively, in MATH and HumanEval tests.

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Over the past month, there have been some very interesting and significant events in the world of Large Language Models (LLM).

Major companies have released fresh versions of their models. First, Google launched two new models, Gemini: Gemini-1.5-Pro-002 and Gemini-1.5-Flash-002.

Key Features:

• More than a 50% price reduction for the 1.5 Pro version
• Results are delivered twice as fast with three times lower latency

The main focus has been on improving performance and speed and reducing costs for models intended for industrial-grade systems.

Details here

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In these notes, I’d like to highlight the latest trends and research in reasoning and new prompting techniques that improve output.

Simply put, reasoning is the process of multi-step thinking, where several consecutive steps of reflection are performed, with each step depending on the previous one.

It may seem that Reasoning and Chain of Thought (CoT) are the same thing. They are related but represent different concepts.

Reasoning is a general concept of thinking and making inferences. It encompasses any forms of reflection and conclusions. Chain of Thought is a specific technique used to improve reasoning by adding intermediate steps to help the model clearly express its thoughts and reach more accurate solutions.

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