Authors: Henri Lemoine, Thomas Lemoine, and Fraser Lee
We are excited to introduce AlignmentSearch, an attempt to create a conversational agent that can answer questions about AI alignment. We built this site in response to ArthurB’s $5k bounty for a LessWrong conversational agent calling for the creation of a chatbot capable of discussing bad AI Alignment takes, and guiding people new to the field through common misunderstandings and early points of confusion.
Tl;dr
AlignmentSearch uses a dataset about AI alignment to construct a prompt for ChatGPT to answer AI alignment-related questions, while citing established sources. We qualitatively observe a massive boost in the quality of answers over ChatGPT without any specialized prompt, with results either on par or better than those given by much stronger LLMs (GPT-4 and Bing Search).
Two answers backed by ChatGPT
Overview
AlignmentSearch indexes the Alignment Research Dataset, generating vector embeddings to enable nearest-semantic-neighbor search. We take a user query to find the top-k most semantically close “paragraphs” (text blocks of around 220 tokens, plus some padding). The size of the dataset means we generally have access to a few paragraphs that are very semantically similar to the user’s question, and are likely to contain information that is useful in producing an answer. We form a prompt that gives these paragraphs to ChatGPT along with the user’s question and instructions on citation, and retrieve an answer somewhere between summary and synthesis, all with accurate inline citations linking to the source material. We’ve created a website that lets the user interact easily with this process: ask questions, dive into the sources used in an answer, and ask for further clarifications. We also have an alternative mode that exposes the raw results of the semantic search.
Alignment Research Dataset
The alignment research dataset was announced on LessWrong in June 2022, with a related paper accessible he