4 ms·
>what are the known downsides that people in academia already know about? I work in biology with some interface to wildlife managers/'end users' of our biologi
by a_bonobo 4y ago
>what are the known downsides that people in academia already know about?
I work in biology with some interface to wildlife managers/'end users' of our biological insights. I was hoping we could use these models as advanced chatbots so that our end users could ask biological questions from our data.
However, even the replies to the most basic biological questions in LLAMA/ChatGPT/OpenChatKit are hallucinated, even when setting temperature to 0. We simply can't trust any of these answers; the bot needs to be as truthful as humans in its replies, but none of them are so far. So what good are these models then?
- int_19h 4y agoThis is the wrong way to go about it. Instead of trying to force that informaiton into the model via training, you need to set it up such that it "thinks out loud" as it answers the question, generating intermediate queries against your data sources in the process. Take a look at this chat log from Bing. Note that the user only sees the stuff that is marked #message and #suggestions. https://www.make-safe-ai.com/is-bing-chat-safe/Prompts_Conversations.txt https://www.make-safe-ai.com/is-bing-chat-safe/Prompts_Conve... Then take a look at https://langchain.readthedocs.io/en/latest/ https://langchain.readthedocs.io/en/latest/, specifically the Document Loaders and the Indexes sections to begin with.
- a_bonobo 4y agoI've been playing a lot with the langchain Document Loaders - their examples make the results look more impressive than they are, or perhaps that's because our documents are 'special' (heavy in taxonomy/biology). I've been priming it with 'if you don't know, say that you don't know, don't invent anything' and it usually ends up saying 'I don't know' to questions it can clearly answer. But yeah, working on that part!