A Wikipedia-Fueled Question-Answering Bot, Synced with GPT-3 Technology
In the realm of artificial intelligence, a new approach is being taken to improve the accuracy of responses from GPT-3 bots. This method involves integrating retrieval-augmented generation (RAG) or similar retrieval-based techniques to access up-to-date, authoritative Wikipedia content dynamically.
The bot in question uses the JavaScript function to call GPT-3 for few-shot learning. To begin, it removes stopwords from the user's question using a function developed by http://geeklad.com, and then queries Wikipedia with the cleaned-up words. The full text of a Wikipedia article is referred to as an "extract." For each article retrieved, the bot makes a new call to obtain its full text using one command for each.
Once the Wikipedia text is at hand, the bot appends the original question to each article and processes it with GPT-3 to compute a plausible answer based on the Wikipedia article. GPT-3 sometimes provides incorrect or misleading answers, even with the right settings, but fine-tuning or few-shot learning can improve factual accuracy. Deciding what information to use and how to apply it is challenging, but it's a crucial step in ensuring the bot's responses are as accurate as possible.
One-shot work that the author intends to work on next is pooling all the partial answers into a single, definitive answer. Another desirable yet feasible feature is assigning reliability scores to the GPT-3 generations. This would help users understand the confidence level of the bot's responses.
The bot was put to the test by asking it to find the evolutionary origin of chloroplasts. It provided a list of answers from various Wikipedia articles, each with a link to the article. Quillbot's summarizing tool was used to summarize the bot's answers about the evolutionary origin of chloroplasts.
The bot's answer is not a single one but actually one per Wikipedia article, and each answer includes a link to the article. To improve the bot, it could be extended to pool all the partial answers and then use that as a new piece of text to produce a single output. The pooled answers can be summarized to create a concise but informative paragraph.
An extra cool thing about the bot is that it listens and reads answers out loud. GPT-3 is a powerful tool used in various projects, including web-based applications for natural conversation with computers. The source code of the web app can be seen by doing CTRL+U. You can test the bot at https://lucianoabriata.altervista.org/tests/gpt-3/wikipedia-gpt3-2.html.
The bot does not reply when it hasn't found the answer to the question as posed. To enhance a GPT-3 bot with direct information from Wikipedia for more accurate answers, it's essential to implement a retrieval-augmented framework where GPT-3 uses real-time Wikipedia content inserted into its prompt or as a retrieval context, ensuring that answers are both current and accurate instead of relying solely on GPT-3's pretrained knowledge base.
This hybrid architecture leverages Wikipedia’s trusted, structured knowledge base as a grounding source, reducing hallucinations and enhancing answer accuracy beyond GPT-3’s static training data alone. This approach aligns with how several modern AI systems tend to favor Wikipedia for structured explanations and rely on retrieval for updated facts.
- The incorporation of retrieval-augmented generation (RAG) or similar retrieval-based techniques into GPT-3 bots, such as the one described, can leverage Wikipedia's structured knowledge base for more accurate responses.
- In addition to pooling all the partial answers into a single, definitive answer, a desirable feature for this GPT-3 bot could be assigning reliability scores to the generated answers, helping users understand the confidence level of the bot's responses.
- As artificial intelligence continues to evolve, integrating technology like GPT-3 with resources like Wikipedia for education and self-development, such as online learning, can provide users with more accurate and up-to-date information.
