No Side Effects: MyGPT's Approach to Safe Generative AI
LLMs and Hallucinations Go Hand in Hand
Here’s the translation with a professional tone:
With the rise of Large Language Models (LLMs) like ChatGPT, the potential risk of model hallucinations has come to the forefront. Numerous reports on this phenomenon foster skepticism, even among those convinced of the technology’s possibilities.
The convincing tone in which incorrect or misleading information is presented exacerbates the issue. There are examples where companies have suffered financial losses due to AI misinformation. Additionally, there are concerns that hallucinations may undermine trust in AI and even lead to legal problems.
- 17. October. 2024
Patrick Ratheiser
CEO & Founder
Karin Schnedlitz
Content Managerin
Why Do Hallucinations Occur?
LLMs operate purely statistically, determining the next word based on probability. The most plausible response is generated from the extensive knowledge base with which an LLM has been trained. If there are conflicting pieces of information or insufficient data in the training set, the result can be a hallucination, as the internal knowledge cannot be coherently aligned. Importantly, the LLM does not experience self-doubt; it conveys both true and false information with the same level of conviction.
MyGPT as a Shield Against Hallucinations
An insurance company has already identified use cases for LLMs with great potential and is seeking a way to address hallucinations. The MyGPT solution from the Austrian AI startup Leftshift One promises to be a flexible and secure LLM tailored to internal company data, designed to prevent potential hallucinations through its functionalities. To understand the benefits of this solution, it is helpful to look at the techniques that prevent hallucinations in LLMs.
How Hallucinations Can Be Prevented
There are fundamentally three approaches to prevent hallucinations in LLMs:
- Fine-Tuning:
LLMs are further trained to refine their predictions. Fine-tuning allows generative AI to be tailored to specific applications or industries. Through additional training, more recent data or industry-specific information can also be incorporated.
- Prompt-Engineering
By optimizing input prompts for LLMs, more accurate and reliable results are achieved. These prompts are characterized by precise and specific formulations. Feedback loops can also be used to verify responses.
- Combination of Fine-Tuning and Prompt-Engineering
By combining both fine-tuning and prompt engineering, higher accuracy and reliability in responses can be achieved. This combination ensures that the generated response is both current and relevant to the input prompt.
MyGPT Flexibly Utilizes the Best Methods to Combat Hallucinations
MyGPT is a flexible and independent solution that enables the internal use of various LLMs within organizations. The Strict Mode ensures that the model’s responses are based solely on the available information previously collected and integrated by the company. Leftshift One primarily utilizes prompt engineering, automatically embedding customer queries into prompts in the background to guarantee maximum accuracy and reliability of the responses.
For specific industries where jargon is particularly important, MyGPT employs a combination of fine-tuning and prompt engineering. This approach is especially relevant for the insurance sector, where complex vocabulary and precise formulations are of great significance.
MyGPT Enhances Trust in AI
The insurance company is convinced that the use of MyGPT, with the functionality of the Strict Mode, has significantly reduced skepticism and the resulting avoidance of LLM deployment in the corporate context. Consequently, for a planned innovation initiative, they have prioritized the trial introduction of MyGPT for the developed use cases. The time for the productive use of generative AI has come, and with Leftshift One, they have a strong and trustworthy partner.
What Does the Future Look Like?
An insight into the MyGPT roadmap reveals additional advantages: With the technique of introspection, the trustworthiness of MyGPT can be further enhanced in the future. In Strict Mode, the LLM reflects on its own response and combines it with a second, alternative LLM in a competitive scenario. This iterative response generation more effectively prevents contradictions and deviations from factual correctness.
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