AI Concepts Made Easy: A Beginner's Manual (Part 4 of 5)
Reducing Hallucinations in Large Language Models
In the previous article, we discussed how Large Language Models (LLMs) can hallucinate1, generating inaccurate responses despite their advanced capabilities. In this article, we dive deeper into why these hallucinations occur and methods to detect and minimize fabricated content.
Why Hallucinations Occur in Large Language Models
Large language models (LLMs) like ChatGPT can sometimes provide incorrect or nonsensical responses, a phenomenon known as hallucination. But why does this happen?
Hallucinations2 occur because LLMs are trained using unsupervised learning on vast amounts of text data. They learn patterns and associations in the data, but don't have a strong sense of objective truth. Without anyone to correct them or fact check, they can develop an impression of the world that mixes truth with fiction, leading to hallucinations.
To understand this, imagine you spent years reading millions of novels. You would learn about how conversations flow and how words relate. But you could also develop a distorted model of reality without external feedback. That's how hallucinations emerge in LLMs.
Reducing Hallucinations Through Reinforcement Learning
One way to address hallucinations is to use reinforcement learning. Reinforcement learning is like training a dog with treats. The system gets "rewards" for generating accurate information and "penalties" for false information. Over many training rounds, it learns to produce more consistently truthful output.
For example: ChatGPT could be trained to simplify scientific concepts to a virtual student. When ChatGPT provides explanations consistent with established facts, it can receive positive reinforcement through mechanisms like a thumbs-up and down button for user feedback, facilitating reinforcement learning. This approach enables ChatGPT to enhance its Large Language Model (LLM) by learning from user interactions. On the other hand, if ChatGPT generates fabricated or incorrect responses, it faces penalties.
Through many iterations of this human-AI collaboration, where users give feedback on the accuracy of ChatGPT's responses, the system learns to prioritize factually correct explanations that earn rewards. This reinforcement learning approach reduces the occurrence of hallucinations by optimizing the model to favor truth over fabrication.
Reinforcement learning reduces hallucinations because the model is optimized for objectively accurate information. Like a dog motivated by treats, it learns to avoid behaviors that lead to penalties. This makes the model's world knowledge more grounded in reality.
How RAG Combines Retrieval with Generation
One reinforcement learning-based system is RAG, short for "Retrieval-Augmented Generation." RAG combines two strategies: retrieving texts and generating text.
Imagine RAG is an elementary school student answering science questions. When asked a question, it first quickly "retrieves" relevant passages from textbooks and study guides. But it doesn't just copy those passages word-for-word. It "generates" a new explanation summarizing the key information in its own words.
This retrieval step anchors RAG in objective facts. The generation step then expresses those facts in a fluid, conversational way. Used together, retrieval and generation allow RAG to stay accurate while being engaging and easy to understand.
Additional techniques could be combined with retrieval-augmented generation (RAG) systems to further reduce hallucinations in LLMs like ChatGPT from OpenAI and Gemini from Google:
Formulating Prompts: Carefully crafted prompts are crucial to minimizing hallucinations and fabricated or biased content from large language models3.
Uncertainty Estimation: Having the model quantify its confidence in potential responses allows filtering out or flagging unsure answers more prone to inaccuracy.
Improving Data Quality: Enhancing the quality of input data can help reduce the likelihood of fabricated responses by LLMs4.
Grounding in External Knowledge: Anchoring responses in verified external knowledge sources, like medical databases for health information, constrains the model to factual data.
Employing Better Prompt Engineering: Utilizing effective prompt engineering techniques can guide the model's responses and decrease the occurrence of fabricated answers5.
Applying AI Advancements to Improve Customer Experience
While hallucinations remain an issue, research into reinforcement learning and retrieval generation systems (RAG) shows promise for mitigating fabricated content. But how can we apply AI advancements to improve customer experiences beyond just accuracy?
Legal Document Summarization
Legal documents like terms of service and privacy policies for SaaS products often contain complex jargon. RAG systems could analyze these documents and generate simplified summaries highlighting the most important points in plain language. This makes it easier for SaaS customers to understand key policies and make informed decisions.
Personalized Onboarding
RAG chatbots could engage new users in a conversational onboarding process. By retrieving customer usage data and goals, the chatbot can generate walkthroughs and training tailored to getting each user up and running with the features most relevant to them. This personalized approach improves onboarding efficiency.
Contextual Troubleshooting
When customers need support troubleshooting SaaS product issues, RAG can combine analyzing recent usage data with retrieving relevant help articles to generate customized troubleshooting suggestions for that specific situation. This provides more contextual assistance compared to generic FAQs.
Productivity-Boosting AI Tools
Compose AI- a chrome extension to enable anyone with advanced writing features like generating full sentences and contextually aware suggestions, setting it apart from Gmail's autocomplete. Shortwave, on the other hand, integrates AI directly into email workflows with a focus on creativity and user trust through collaboration with Open AI.
Pace AI - an AI tool designed to simplify technical language and enhance communication. Pace AI is beneficial for product managers and business stakeholders seeking to better understand technical audiences.
By applying AI to improve customer experience and productivity, businesses can deliver greater value - as long as they remain vigilant about minimizing potential downsides like hallucinations.
Sources:
https://www.reddit.com/r/MachineLearning/comments/14slf2p/d_list_of_prior_works_on_llm_hallucination/
https://www.eisneramper.com/insights/blogs/digital-blog/artificial-intelligence-chatbot-hallucinations-di-blog-1023/