From 45c6406e8bb42e502d0394a9f1d217e5494ba4a2 Mon Sep 17 00:00:00 2001 From: Christine Belzie <105683440+CBID2@users.noreply.github.com> Date: Fri, 25 Aug 2023 16:08:49 -0400 Subject: [PATCH] [revise] small edits and fixed typos (#510) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * [revise] made small edits and fixed typos * fix: revert back to original descriptions * small language updates --------- Co-authored-by: Simón Fishman --- text_comparison_examples.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/text_comparison_examples.md b/text_comparison_examples.md index 6f1d112..70da87e 100644 --- a/text_comparison_examples.md +++ b/text_comparison_examples.md @@ -34,7 +34,7 @@ In more advanced search systems, the cosine similarity of embeddings can be used ## Question answering -The best way to get reliably honest answers from GPT-3 is to give it source documents in which it can locate correct answers. Using the semantic search procedure above, you can cheaply search a corpus of documents for relevant information and then give that information to GPT-3, via the prompt, to answer a question. We demonstrate in [Question_answering_using_embeddings.ipynb](examples/Question_answering_using_embeddings.ipynb). +The best way to get reliably honest answers from GPT-3 is to give it source documents in which it can locate correct answers. Using the semantic search procedure above, you can cheaply search through a corpus of documents for relevant information and then give that information to GPT-3 via the prompt to answer a question. We demonstrate this in [Question_answering_using_embeddings.ipynb](examples/Question_answering_using_embeddings.ipynb). ## Recommendations