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Show HN: Open-source model and scorecard for measuring hallucinations in LLMs

vectara.com

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Re: Show HN: Open-source model and scorecard for measuring hallucinations in LLMs

#11
post #6

I worked on the model with our research team. Recently featured in this NYT ( https://www.nytimes.com/2023/11/06/technology/chatbots-hallu... . Post here to AMA. We are also looking for collaborators to help us maintain this model and make it the best it can be. Let us know if you want to help

interesting work, thanks! Not enough people studying this. Do you have a whitepaper describing how you trained this hallucination detection model? Is each row of the leaderboard the mean of the Vectara model's judgment of the 831 (article,summary) pairs, or was there any human rating involved? With so few pairs, it seems feasible that human ratings should be able to quantify how much hallucination is actually occurri…

We may write a research paper at some point. For now, see here: https://vectara.com/cut-the-bull-detecting-hallucinations-in...

Given the number of models involved, we have over 9k rows currently. Judging for this task is quite time consuming as you need to read a whole document and check it against a several sentence summary and some of the docs are a 1-3 min read. We wanted to automate this process and also make it as objective as possible (even humans can miss hallucinations or disagree on an annotation). Plus we also wanted people to be able to replicate the work, non of which is possible with a human rater, plus others have attempted that but on a much smaller scale, e.g. see AnyScales - https://www.anyscale.com/blog/llama-2-is-about-as-factually-... (but note that is under 1k examples).

We did some human validation and the model is well in alignment with humans but not in perfect agreement, as it is a model after all. And again human's don't agree 100% of the time on this task either.

Re: Show HN: Open-source model and scorecard for measuring hallucinations in LLMs

#12
post #8

This link gives a lot more context for the less informed: https://vectara.com/cut-the-bull-detecting-hallucinations-in... I really want to know more about the hallucinations produced! Were some sources more likely to produce errors! I would be curious if other document sets were explored for use with this test and if different source material would change the results in a meaningful way.

The original data we used was not annotated with sources, only where the overall data came from. Most was news articles. The length doesn't seem to matter too much as we see a lot of errors even when summarizing a single sentence (sometimes the model felt compelled to elaborate more info). Usually the hallucinations were common sense inferences, such as assuming the plant was a cannabis plan in the example listed in the NYT article. Other times the LLM would invert things. Eg. if you ask any of the google LLMs to summarize an article about a famous boxer, where the article stated that Wahlberg was a fan of said boxer, the Palm models would flip it to say the boxer was a fan of Wahlbergs. Even the latest Bard model still does that, I tested it this weekend. It's a subtle and small error. But it's still factually incorrect.

Re: Show HN: Open-source model and scorecard for measuring hallucinations in LLMs

#13
post #8

This link gives a lot more context for the less informed: https://vectara.com/cut-the-bull-detecting-hallucinations-in... I really want to know more about the hallucinations produced! Were some sources more likely to produce errors! I would be curious if other document sets were explored for use with this test and if different source material would change the results in a meaningful way.

You can view the responses here in the linked csv file: https://github.com/vectara/hallucination-leaderboard
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