Building with AI and LLMs is now a must-know for every developer. Every application is trying to integrate AI models. But hallucinations -- the phenomenon of AI models generating incorrect or unverified information -- are still an unsolved problem.
"Ughh ChatGPT - I told you to NOT make stuff up!"
Andrej Karpathy shared recently his take on hallucinations on Twitter:
"The LLM has no "hallucination problem". Hallucination is not a bug, it is LLM's greatest feature. The LLM Assistant has a hallucination problem, and we should fix it."
So how do we fix it?
Chain-of-Verification (CoVe), a technique introduced by researchers at Meta, is one way. Let's dive into the high-level process of CoVe and then explore how we implemented a CoVe prompt template using AIConfig that you can use to reduce hallucinations in your LLM-powered apps.
The Chain-of-Verification (CoVe) Process 🔗
As documented in the white paper, the process involves 4 crucial steps:
1️⃣ Generate Baseline: Given a query, the Large Language Model (LLM) generates a response.
2️⃣ Plan Verification(s): With the query and baseline response, the system formulates a list of verification questions. These would aid in analyzing potential inaccuracies within the original response.
3️⃣ Execute Verification(s): Each verification question is answered, and then cross-checked against the original response to discern inconsistencies or flaws.
4️⃣ Generate Final Response: If inconsistencies are found, a revised response is generated, factoring in the results from the verification process.
Integrate CoVe into your App with AIConfig💡
We've brought the CoVe technique to life using AIConfig, streamlining the process to help reduce hallucinations in your LLM applications.
Using AIConfig, we can separate the core application logic from the model components (prompts, model routing parameters, etc.). Here's what the prompt template looks like:
1️⃣ GPT4 + Baseline Generation prompt: This sets the foundation by generating the initial response using GPT4.
2️⃣ GPT4 + Verification prompt: This prompt creates a series of verification questions based on the initial response.
3️⃣ GPT4 + Final Response Generation prompt: Leveraging the findings from the verification stage, this prompt generates a final, more reliable response.
🔗 AIConfig CoVE: https://github.com/lastmile-ai/aiconfig/tree/main/cookbooks/Chain-of-Verification
Want to see it action? 👀 Try out our demo in Streamlit!!
🎁 Streamlit App: https://chain-of-verification.streamlit.app/
Are you already using AIConfig or CoVe in your projects? Feel free to share your experiences in the comments below.
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Top comments (1)
Checked out the open source library @tanyarai! Looks pretty good!
We know how powerful are these techniques to improve the quality of LLM generated outputs.
We've built Athina AI - An open source platform for monitoring and evaluating LLM responses.
I belive we can collaborate on Development X Evaluation workflows. Let me know what do you think about it.
More info - Launch Post