Using search-enhanced generation technology to solve the artificial intelligence hallucination problem

Author| Rahul Pradhan

##Source| https: //www.infoworld.com/article/3708254/addressing-ai-hallucinations-with-retrieval-augmented-generation.html

Artificial intelligence is expected to become the most influential technology of our time . Recent advances in transformer technology and generative artificial intelligence have demonstrated their potential to unleash innovation and ingenuity at scale.

However, generative AI is not without its challenges—challenges that may even seriously hinder the adoption and value creation of this transformative technology. As generative AI models continue to increase in complexity and capability, they also present unique challenges, including generating output that is not based on the input data. "Illusion" means that the output results produced by the model, although coherent, may be divorced from the facts or input context. This article will briefly introduce the transformative impact of generative artificial intelligence, examine the shortcomings and challenges of the technology, and discuss techniques that can be used to mitigate hallucinations.

The transformative effect of generative artificial intelligence

Re-stated as: Generative artificial intelligence models leverage the power of deep learning A complex computational process to identify patterns in large data sets and use this information to create new and compelling output. These models use neural networks in machine learning technology, which are inspired by the way the human brain processes and interprets information, and continuously learns and improves over time

OpenAI's GPT Generative AI models such as -4 and Google’s PaLM 2 promise to bring innovations in automation, data analysis, and user experience. These models can write code, summarize articles, and even help diagnose diseases. However, the feasibility and ultimate value of these models depends on their accuracy and reliability. In critical areas such as healthcare, financial or legal services, reliability of accuracy is critical. But for all users to realize the full potential of generative AI, these challenges must be addressed

Drawbacks of Large Language Models

LLM is fundamentally probabilistic and non-deterministic. They generate text based on the likelihood that a specific word sequence will occur next. LLM has no notion of knowledge and relies entirely on navigation through a corpus of trained data as a recommendation engine. The text they generate generally follows grammatical and semantic rules, but is entirely based on statistical consistency with the prompt.

This probabilistic nature of LLM is both an advantage and a disadvantage. If the goal is to arrive at the correct answer or to make a critical decision based on the answer, then hallucination is bad and can even cause damage. However, if the goal is a creative endeavor, the LLM can be used to foster artistic creativity, resulting in the creation of artwork, storylines, and screenplays relatively quickly.

However, regardless of the goal, failure to trust the output of an LLM model can have serious consequences. Not only would this erode trust in the capabilities of these systems, it would also significantly reduce the impact of AI in accelerating human productivity and innovation.

Ultimately, artificial intelligence is only as good as the data it is trained on.

The illusion of LLM is mainly caused by defects in the data set and training, including the following aspects:

Overfitting: Overfitting occurs when a model learns too well on training data (including noise and outliers). Model complexity, noisy training data, or insufficient training data can all lead to overfitting. This results in low-quality pattern recognition where the model does not generalize well to new data, leading to classification and prediction errors, factually incorrect outputs, outputs with low signal-to-noise ratio, or outright hallucinations.

• Data Quality: Mislabeling and misclassification of data used for training may play a role in hallucinations effect. Biased data or a lack of relevant data can actually result in model output that appears to be accurate but could prove to be harmful, depending on the scope of decisions the model recommends.
• Data Scarcity: Data scarcity or the need for fresh or relevant data is what creates illusions and hinders enterprise adoption One of the important issues in generative artificial intelligence. Refreshing data with the latest content and contextual data helps reduce illusions and bias.
• Addressing hallucinations in large language models

There are several ways to resolve them

Illusion problems in LLM, including techniques such as fine-tuning, cue engineering, and retrieval-augmented generation (RAG).

• Fine-tuning refers to retraining a model using a domain-specific dataset to more accurately generate content relevant to that domain. However, retraining or fine-tuning the model takes a long time, and in addition, the data quickly becomes outdated without continuous training. In addition, retraining the model also brings a huge cost burden.
• The Hint Project aims to help ## by providing more descriptive and illustrative features in the input as hints # LLM produces high quality results. Providing the model with additional context and grounding it in facts reduces the likelihood that the model is hallucinating.
• Retrieval Enhanced Generation (RAG) is a method that focuses on using the most accurate and up-to-date information for LLM Provide a basic framework. The responsiveness of the LLM can be improved by feeding the model with facts from external knowledge bases in real time.

Retrieval-augmented generation and real-time data

Retrieval-augmented generation is one of the most promising techniques for improving the accuracy of large language models one. It turns out that RAG combined with real-time data can significantly reduce hallucinations.

RAG enables enterprises to leverage LLM by leveraging the latest proprietary and contextual data. In addition, RAG can also enrich the input content with specific contextual information, thereby helping the language model generate more accurate and contextually relevant responses. In an enterprise environment, fine-tuning is often impractical, but RAG offers a low-cost, high-yield alternative for delivering a personalized, informed user experience

In order to improve the efficiency of RAG models, it is necessary to combine RAG with an operational data store capable of storing data in the native language of LLMs, i.e. high-dimensional mathematical vectors called embeddings, using on the meaning of the encoded text. When a user asks a query, the database converts it into a numeric vector. In this way, related texts can be queried through the vector database regardless of whether they contain the same terms or not.

Highly available, high-performance databases capable of storing and querying massive amounts of unstructured data using semantic search are key components of the RAG process.

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