Open LLM Wave (2023) › Limitations
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Despite their significant contributions, the open models of 2023 had important limitations that shaped their adoption and development. Understanding these limitations helps contextualize both the achievements of the open model wave and the challenges that remained.
Performance gaps remained between open models and the largest proprietary systems. While open models achieved competitive performance on many benchmarks, the most capable proprietary systems like GPT-4 continued to outperform open alternatives on complex reasoning tasks, creative generation, and specialized capabilities. These performance gaps limited the applicability of open models for use cases requiring the highest levels of capability.
Computational requirements still posed barriers for many potential users. Even the smaller open models like Mistral 7B required significant computational resources for training and inference. While optimizations like quantization and efficient architectures helped, deploying and using open models effectively still required more technical expertise and infrastructure than using proprietary APIs. This complexity limited adoption among organizations without strong technical teams.
The open model ecosystem also faced challenges with model alignment and safety. While open models enabled independent evaluation and improvement of safety measures, they also made it easier for bad actors to deploy models without safety guardrails. The balance between openness and safety remained an ongoing challenge, with some arguing that overly permissive licensing or insufficient safety measures could enable harmful applications.
The openness that made these models valuable for research and legitimate applications also created risks. Unlike proprietary systems with built-in safety guardrails, open models could be modified or deployed without safety measures. This tension between accessibility and safety would become a central challenge for the open model ecosystem, requiring careful consideration of licensing, model release practices, and community governance.
Licensing and legal considerations created complexity for commercial users. Different open models used different licenses with varying restrictions on commercial use, modification, and redistribution. Meeting these licensing requirements could be challenging for organizations wanting to use multiple models or build commercial applications. Some licenses also created uncertainty about acceptable use cases or requirements for derivative works.
The pace of innovation in the open ecosystem, while generally positive, also created challenges. With new models and updates being released frequently, organizations faced decisions about which models to adopt and when to upgrade. The rapid pace made it difficult to establish stable tooling and infrastructure, as the ecosystem evolved quickly.
Data quality and transparency, while improved in open models, still had limitations. Even when developers released information about training data, complete transparency was often impractical due to dataset size or licensing constraints. Understanding the biases, limitations, and failure modes of models remained challenging despite increased transparency compared to proprietary systems.