No Single LLM Can Be Trusted in Isolation

I started building Glama after a simple observation: no single LLM can be trusted in isolation.

From Awe to Skepticism

Like many others, my first exposure to LLMs was through OpenAI's GPT-2 model. At first, I would compose a prompt, share it with the model, and usually accept its response as "most likely correct." However, as many others at the time, I still viewed the technology as a promise of what will be possible in the future, rather than a trustworthy peer to consult with.

Later, in June 2020, as GPT-3 came out, wowed by many incredible demos, I began exploring what it is like to rely on LLMs for helping with everyday tasks in my domain of expertise. This is where my trust in LLMs began to diminish...

Trust, but Verify

There is a phenomenon known as the Gell-Mann Amnesia effect. The effect describes how an expert can spot numerous errors in an article about their field but then accept information on other subjects as accurate, forgetting the flaws they just identified. Being aware of the phenomenon, and observing the frequency of errors in the information I was receiving, I stopped trusting LLMs without validating their responses.

Over time, more models started to appear, each one making more grandiose statements than the others. I started to experiment with all of them. No matter what the prompt was, I developed a habit of copy-pasting my prompts across multiple models like OpenAI, Claude, and Gemini. This change in behavior led me to a further insight:

A single LLM might be unreliable, but when multiple models independently reach the same conclusion, it boosts confidence in the accuracy of the information.

As a result, my trust in LLMs became proportional to the level of consensus achieved by consulting multiple models.

Limitations of LLMs

We've established that relying on any single LLM is dangerous. Based on my understanding of the technology, I believe this limitation to be inherent to LLMs (rather than a question of model quality). It's because of the following reasons:

1. Dataset Bias: Each LLM is trained on a specific dataset, inheriting its biases and limitations.
2. Knowledge Cutoff: LLMs have a fixed knowledge cutoff date, lacking information on recent events.
3. Hallucination: LLMs can generate plausible-sounding but incorrect information.
4. Domain Specificity: Models excel in certain areas but underperform in others.
5. Ethical Inconsistency: Alignment techniques vary, leading to inconsistent handling of ethical queries.
6. Overconfidence: LLMs may present incorrect information with high confidence.

By leveraging multiple LLMs, we can mitigate these limitations. Different models can complement each other's strengths, allow user to cross-verify information, and provide a more balanced perspective. This approach, while not perfect, significantly improves the trustworthiness of LLMs.

[!NOTE]
In addition to what's being discussed in this article, I also want to draw attention to the emergence of 'AI services' (it's no longer accurate to call them just LLM models) that are capable of reasoning. These services combine techniques such as Dynamic Chain-of-Thought (CoT), Reflection, and Verbal Reinforcement Learning to provide responses that aim to offer a higher degree of trust. There is a great article that goes into detail about what these techniques are and how they work. We are actively working on bringing these capabilities to Glama.

Glama: Streamlining Multi-Model Interactions

Recognizing the limitations of single-model reliance, I developed Glama as a solution to streamline the process of gaining perspectives from multiple LLMs. Glama provides a unified platform where users can interact with various AI models simultaneously, effectively creating a panel of AI advisors.

Key features of Glama include:

1. Multi-Model Querying: Simultaneously consult multiple LLMs, including the latest from Google, OpenAI, and Anthropic.

2. Enterprise-Grade Security:

   • Your data remains under your control, never used for model training.
   • End-to-end encryption (AES 256, TLS 1.2+) for data in transit and at rest.
   • SOC 2 compliance, meeting stringent security standards.

3. Seamless Integration:

   • Admin console for easy team management, including SSO and domain verification.
   • Collaborative features like shared chat templates for streamlined workflows.

4. Comparative Analysis: Easily compare responses side-by-side to identify consistencies and discrepancies across models.

5. Customizable Model Selection: Choose which LLMs to consult based on your specific needs and security requirements.

By facilitating secure, efficient access to diverse AI perspectives, Glama empowers users to make more informed decisions, leveraging the strengths of multiple models while mitigating individual weaknesses – all within a robust, enterprise-ready environment.

Conclusion

In today's AI landscape, relying on a single LLM is akin to seeking advice from just one expert – potentially valuable, but inherently limited. Glama embodies the principle that diversity in AI perspectives leads to more robust and reliable outcomes. By streamlining access to multiple LLMs, Glama not only saves time but also enhances the quality of AI-assisted decision-making.

As we continue to navigate the evolving world of AI, tools like Glama will play a crucial role in helping users harness the collective intelligence of multiple models...

There's no one AI to rule them all – but with Glama, you can leverage the power of many.