From: redpointai
The future of Large Language Models (LLMs) involves several key areas of development, particularly focusing on efficiency, architecture, and application. Mistral, a leading developer of open-source LLMs, views AI as an infrastructure technology that should be modifiable and owned by customers, believing this will lead to the prevalence of open-source solutions [00:03:31]. The company’s mission is to be the most relevant platform for developers [00:05:29].
Key Frontiers for LLMs
Arthur Mensch, CEO and co-founder of Mistral, highlights several frontiers for the development of LLMs:
- Efficiency Frontier: There is still significant potential to push the efficiency frontier [00:10:25]. Mistral 7B, for instance, demonstrated a compressed model, and further improvements are expected [00:10:29]. The goal is to make models more efficient, enabling them to be deployed on smaller devices and improving latency [00:11:27].
- Scaling Laws: The industry is not yet at the end of scaling loads, meaning even better models can be created [00:10:35]. This involves continuously scaling model training and making models more efficient [00:11:23].
- Controllability: A significant challenge remains in making models truly controllable [00:10:44]. Research is needed to develop methods for tweaking models to follow specific instructions more reliably [00:11:00].
- Architectural Innovation: While Transformers have been dominant for seven years, Mensch believes they are not the optimal architecture [00:14:42]. More efficient designs than a “plain Transformer” that spends the same compute on every token are likely possible [00:11:09]. However, the co-adaptation of training algorithms, debugging processes, and hardware to Transformers makes non-incremental architectural changes very challenging [00:15:07]. Mistral has focused on improvements like sparse attention for memory efficiency [00:15:37].
- Latency Improvement: Making models “think faster” is crucial, as it opens up a vast array of new applications that use LLMs as foundational components for complex tasks like planning and exploration [00:11:34].
Efficiency and Compute Strategy
Mistral’s approach to development emphasizes efficiency, even with fewer resources compared to larger players. Despite Meta having significantly more GPUs, Mistral focuses on maintaining a high concentration of GPUs per person, enabling creative and efficient training methods [00:12:01]. The company currently operates effectively with 1.5K H100s and plans to increase this to ship better models [00:12:22].
Cost efficiency and accessibility is critical, especially for startups, as large-scale compute is expensive [00:12:44]. A key challenge is ensuring that money spent on compute and training accrues to more than that in revenue, necessitating efficiency in training compute for a valid business model [00:12:48].
Role of Data Strategy and Language
The future of LLMs also depends on evolving data strategies. While large datasets are crucial, the focus is shifting towards “demonstration data”—traces of what users do—to enable more robust and reliable systems [00:29:31]. Many enterprises lack this type of data readily available, suggesting an “even field” where companies can start acquiring it faster [00:29:45]. Enterprises should rethink their data strategy in light of the copilot and assistant applications they aim to deploy [00:30:11].
Language capabilities are another vital area. Currently, models perform much better in English than in other languages [00:23:43]. Mistral is committed to developing models that are excellent in every language, starting with French where their models are among the best [00:23:46]. This focus on multilingualism and portability is central to their global approach, ensuring the technology is ubiquitous and usable worldwide [00:24:08]. The ability to excel in various languages largely resides in the pre-training phase, making it a core task for foundational model companies [00:24:45].
Regulation and Sovereignty
Regarding regulation, Mistral advocates for a product safety perspective, focusing on the application layer rather than directly regulating the underlying technology [00:17:17]. Regulating applications would force application makers to verify that their products are safe and perform as expected, which would, in turn, create competitive pressure on foundational model providers to offer more controllable models [00:21:17]. Direct technology regulation, such as that proposed in the EU AI Act, is seen as an “ill-directed burden” that does not solve the core product safety problem [00:18:32] and could favor larger players [00:22:45].
The challenge of making AI products safe is primarily a technological and product problem, requiring rethinking evaluation, continuous integration, and verification processes [00:19:45]. The emergence of AI safety and evaluation startups (middleware) is beneficial for developing necessary tools, which may eventually consolidate into core AI platforms [00:20:43].
Sovereignty concerns are addressed through portability, allowing countries and developers to deploy AI technology where they want [00:23:23]. Providing access to technology that can be modified and distributed in a decentralized way addresses sovereignty problems, unlike relying solely on SaaS services from a few dominant companies [00:25:28].
Applications and Future Niches
Beyond core model development, Mistral is exploring how to help enterprises adopt generative AI, initially through an internal assistant called “Entreprise” [00:27:28]. This serves as an entry point, providing immediate value by contextualizing the assistant with enterprise data, and also helps solidify internal APIs, exposing tools like moderation [00:27:45]. This strategy aims to get enterprises started before they fully realize the potential for their core business [00:28:16].
Arthur Mensch expresses excitement for hard science applications of AI, particularly in Material Science [00:32:36]. This field currently lacks a foundational model, and AI could significantly accelerate processes like the synthesis of ammonia, which is very carbon-intensive [00:32:49]. This trend of domain-specific foundational models for industries like biology, Material Sciences, and robotics is growing [00:38:40]. The main challenge in these areas is generating sufficient data, as the entire internet is not available for scraping [00:38:58].