3 ms·
There are two things to that answer; first, a summary: > Most of the article appears to be about what does not work. and then a segmentation, where they list
by PartiallyTyped 3y ago
There are two things to that answer; first, a summary:
> Most of the article appears to be about what does not work.
and then a segmentation, where they list the content.
This is very much in line with LLMs can do. Segmentation is part of a standard QA benchmark, and summaries are obviously something we have.
You can also ask a model to not only identify a summary, but segment only the relevant parts given the summary.
The "difficulty" is in pairing the two LLMs together.
- fouc 3y agoI asked chatgpt to pick the most important 2 or 3 lines of text from the article. It returned: 1. "Fortunately, there is advice out there on how to break the ice with strangers. Unfortunately, it’s abysmal." 2. "Making contacts on a site like LinkedIn is a lot less stressful." 3. "The real secret is to save your energy for the people who are most likely to be interesting to you." It got 2 out of 3 wrong basically. I'm not sure how to objectively measure/improve that in an automated way. I suppose some prompt might do better though? I guess a second pass of the results through another LLM could potentially help like you suggest?