AI21 Labs launches anti-hallucination feature for GPT chatbots

Context responses are designed for business, but could have far-reaching implications for the generative AI industry.

AI21 Labs recently launched "Contextual Answers", a question-and-answer engine for large language models (LLM).

When connected to an LLM, the new engine allows users to upload their own data libraries to limit model outputs to specific information.

The launch of ChatGPT and similar artificial intelligence (AI) products has changed the paradigm for the AI ​​industry, but a lack of reliability makes adoption difficult for many companies.

According to research, employees spend almost half of their working day searching for information. This represents a huge opportunity for chatbots capable of performing search functions; however, most chatbots are not suitable for businesses.

AI21 has developed Contextual Answers to bridge the gap between chatbots designed for general use and enterprise-level question-and-answer services by giving users the ability to channel their own libraries of data and documents.

According to an AI21 blog post, contextual responses allow users to direct AI responses without retraining models, thereby mitigating some of the key barriers to adoption:

"Most organizations struggle to adopt [AI], citing the cost, complexity, and lack of specialization of models in their organizational data, resulting in incorrect, "hallucinated," or context-inappropriate responses."

One of the major challenges in developing useful LLMs, such as OpenAI's ChatGPT or Google's Bard, is teaching them to express distrust.

Usually, when a user queries a chatbot, it generates a response even though there is not enough information in its dataset to provide factual information. In these cases, rather than producing an unreliable response such as "I don't know", LLMs will often make up information without any...