Artificial Intelligence (AI) has made big changes in many areas, but it’s not perfect. A major problem with AI language models is that they sometimes produce wrong and confusing information. This has made some people doubt AI and even led some organizations to stop using AI completely. But there’s a company called AI21 Labs that wants to fix this. They made a new tool called Contextual Answers. This tool tries to solve these problems and make AI better at talking sensibly. Let’s learn why Contextual Answers could be a good solution for these issues in AI that create text and how contextual answers mitigate LLM hallucinations In AI-Language models.
Reviewing How Hallucinations Pose Big Problems for AI-Language Model
AI language models, such as the large model Jurassic II of AI21, predict the next word in a sentence. They excel at structurally and grammatically correct sentences – but no factuality understanding is involved. This means that they might generate responses that are not grounded in truth or accuracy. For example, you’re looking at a website’s return policy. The AI21 Contextual Answers tool might not give you an exact answer from that policy. Instead, it could give you a more general response. This tool is designed to be smart about the context.
It works by training the AI to understand information from just one document or several documents. They use sets of three things (triplets): the documents, the questions, and the answers. This helps the AI give accurate responses that fit the situation and the information it has learned.
Contextual Answers’ Specific Approach
Contextual Answers operates on AI21’s extensive language model, Jurassic II. This model is specialized in various business areas like finance, medicine, insurance, pharmaceuticals, and retail. Unlike models that learn from the internet and can forget old information with new data, Contextual Answers ensures that even when trained with new information for specific business topics, it doesn’t forget what it already knows.
Some organizations have tried similar approaches with open-source projects. However, these projects demand a lot of human effort from AI experts, language processing specialists, and engineers. Because of its modular architecture and plug-and-play nature, Contextual Answers offers a quick-bake solution with minimal engineering work required.
A major other advantage is flexibility in document length handling. While most models suffer from context window size, they can scale documents over any number of documents with any length document. This would be appropriate for organizations that have massive amounts of paper and extremely complex information requirements.
See Also: AI development life cycle
How Contextual Answers Mitigate LLM Hallucinations In AI-Language Models
Contextual Answers deals with hallucination by amalgamating training methods and built-in filters. It is first trained on triplets comprising documents, questions, and answers, ensuring it learns to retrieve information properly from provided. This training technique helps avoid hallucinations due to its grounding in the model’s responses in specifically unique context-like materials put forward by the documents under question.
Additionally, Contextual Answers incorporate filters and rails to detect hallucinations and remove them or prompt the model to generate alternative outputs. These filters act as safeguards, preventing the model from going off the rails and generating inaccurate information. While it is still possible to trigger hallucinations, the safeguards implemented by Contextual Answers make it significantly more reliable and truthful than other models.
Implementing Contextual Answers
An organization could run Contextual Answers in two ways. One is by running as a Software-as-a-Service (SaaS). This is through cloud platforms such as AWS, which provides convenience and scalability where organizations could exploit the power of Contextual Answers without giving a thought to infrastructure management. Alternatively, an organization could opt to roll it out from its own virtual private cloud so as to have its data remain within its secured environment.
An organization would use AI21’s website or API so as to train the model from its document library, but this time, customizing it across for special domain requirements even further increases the accuracy and relevancy of answers generated.
The Future of Contextual Answers
Right now, Contextual Answers tries to reduce mistakes in AI language models. AI21 Labs wants to make it even better by adding more features. They’re thinking about helping with coding. The current version doesn’t work perfectly for coding, but they plan to teach the AI to assist with coding tasks in the future. This would be useful for developers and would make Contextual Answers more serious than just a fun experiment.
Contextual Answers is an AI21 solution that limits the risk associated with LLM hallucinations in AI-language models. It trains a model to retrieve information accurately from specific documents and robustness filters, ensuring at least very little occurrence of responses with LLM Hallucinations In AI-language models. Contextual Answers is flexible regarding document length or implementation options; it gives organizations a reliable yet customizable tool generating grounded but accurate answers. As AI21 Labs keeps on fine-tuning and expanding functionality-wise, contextual answers can revolutionize the organization.
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