Mistral Codestral is the Newest AI Model in the Code Generation Race

Plus updates from Elon Musk’s xAI , several major funding rounds and intriguing research publications.

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Next Week in The Sequence: Mistral Codestral is the New Model for Code Generation

Edge 401: We dive into reflection and refimenent planning for agents. Review the famous Reflextion paper and the AgentVerse framework for multi-agent task planning.
Edge 402: We review UC Berkeley’s research about models that can understand one hour long videos.

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📝 Editorial: Mistral Codestral is the Newest AI Model in the Code Generation Race

Code generation has become one of the most important frontiers in generative AI. For many, solving code generation is a stepping stone towards enabling reasoning capabilities in LLMs. This idea is highly debatable but certainly has many subscribers in the generative AI community. Additionally, coding is one of those use cases in which there is a clear and well-established customer base as well as distribution channels. More importantly, capturing the minds of developers is a tremendous stepping stone towards broader adoption.

Not surprisingly, all major LLM providers have released code generation versions of their models. Last week, Mistral entered the race with the open-weight release of Codestral, a code generation model trained in over 80 programming languages.

Like other Mistral releases, Codestral shows impressive performance across many coding benchmarks such as HumanEval and RepoBench. One of the most impressive capabilities of Codestral is its 32k context length in the 22B parameter model, which contrasts with the 8k context window in the Llama 3 70B parameter model.

Codestral is relevant for many reasons. First, it should become one of the most viable open-source alternatives to closed-source foundation models. Additionally, Mistral has already established strong enterprise distribution channels such as Databricks, Microsoft, Amazon, and Snowflake, which can catalyze Codestral’s adoption in enterprise workflows.

Being an integral part of the application programming lifecycle can unlock tremendous value for generative AI platforms. Codestral is certainly an impressive release and one that is pushing the boundaries of the space.”

🔎 ML Research

USER-LLM

Google Research published a paper outlining USER-LLM, a framework for contextualizing individual users interactions with LLMs. USER-LLM compresses user interactions into embedding representations that are then used in fine-tuning and inference —> Read more.

AGREE

Google Research published a paper introducing Adaptation for GRounding Enhancement(AGREE), a technique for grounding LLM responses. AGREE enables LLM to provide precise citations that back their responses —> Read more.

Linear Features and LLMs

Researchers from MIT published a paper proposing a framework to discover multi-dimensional features in LLMs. These features can be decomposed into lower dimensional features and can improve the computational ability of LLMs which are typically based on manipulating one-dimensional features —> Read more.

CoPE

Meta FAIR published a paper outlining contextual position encoding(CoPE), a new method that improves known counting challenges in attention mechanisms. CoPE allows positions to be based on context and addresses many challenges of traditional positional embedding methods —> Read more.

DP and Synthetic Data

Microsoft Research published a series of research papers exploring the potential of differential privacy(DP) and synthetic data generation. This is a fast growing are that allow companies to generate synthetic data and maintain privacy over the original datasets —> Read more.

LLMs and Theory of Mind

Researchers from Google DeepMind, Johns Hopkins University and several other research labs published a paper evaluating whether LLMs have developed a higher order theory of mind(ToM). By ToM we refers to the ability of human cognition to reason through multiple emotional and mental states in a recursive manner —> Read more.