Ais Dark Secret: Can Large Language Models Trust Their Own Decisions

The limitations of Large Language Models (LLMs) are becoming increasingly apparent as businesses scale their reliance on AI. LLMs are incredibly powerful yet can sometimes “lose the plot” or offer outputs that veer off course due to their generalist training and massive data sets.

In regulated industries such as healthcare, financial services, and legal, inaccurate answers can have huge financial consequences and even life-or-death repercussions. Regulatory bodies are already taking notice and will likely begin to demand explainable AI solutions.

To address these limitations, companies can supplement their LLMs with Specialized Language Models (SLMs), which offer greater explainability, precision, and control over data privacy and security.

LLMs are essentially “black boxes” that don’t reveal how they arrive at an answer. A trust issue arises as businesses often deploy a “human-in-the-loop” approach to mitigate these issues, but relying solely on this may lead to a false sense of security. Over time, complacency can set in and mistakes can slip through undetected.

SLMs are better suited to address many of the limitations of LLMs. Rather than being designed for general-purpose tasks, SLMs are developed with a narrower focus and trained on domain-specific data. This specificity allows them to handle nuanced language requirements in areas where precision is paramount.

SLMs offer several advantages: they provide greater explainability, enabling easier understanding of the source and rationale behind their outputs. They can also perform faster than LLMs, which is crucial for real-time applications. Furthermore, SLMs offer businesses more control over data privacy and security, especially if deployed internally or built specifically for the enterprise.

In practice, SLMs can augment LLMs, creating hybrid solutions where LLMs provide broader context and SLMs ensure precise execution. However, it’s essential to understand that LLMs and SLMs are not mutually exclusive, and companies should explore the possibilities and benefits of both.

Leaders should invest time and attention in building distinct skills required for training, fine-tuning, and testing SLMs. It’s crucial to vet partners carefully and implement controlled proof-of-concepts or live deployments before investing significant time and money.

In high-stakes fields that demand accuracy and explainability, supplementing with SLMs is a path forward. By investing in SLMs, companies can future-proof their AI strategies, ensuring that their tools drive innovation while meeting the demands of trust, reliability, and control.