https://hackerpaper.kaipfister.de/p/2305.14251/Collected HN comments and submissions linking this paper.enMon, 02 Nov 2026 11:16:22 GMThttps://news.ycombinator.com/item?id=39954017urn:hackerpapers:mention:2305.14251:39954017Sat, 06 Apr 2024 17:41:40 GMT<p>Looks like a slight modification of FActScore [1], but instead of using Wikipedia as a verification source, they use Google Search. They also claim to include a wider range of topics. That said, FActScore allows you to use whatever knowledge source and topics you want [2]. [1]: https://arxiv.org/abs/2305.14251 [2]: https://github.com/shmsw25/FActScore?tab=readme-ov-file#to-u...</p><p><a href="https://hackerpaper.kaipfister.de/p/2305.14251/">Paper and discussion history</a>. A mention is not an endorsement.</p>cs.CLhttps://news.ycombinator.com/item?id=39454961urn:hackerpapers:submission:2305.14251:39454961Wed, 21 Feb 2024 15:27:36 GMT<p>How does one detect hallucinations?</p><p>Hallucinations are an interesting artifact of LLMs where the model tends to make up facts or generate outputs that are not factually correct. There are two broad approaches for detecting hallucinations: 1. Verify the correctness of the response against world knowledge (via Google/Bing search) 2. Verify the groundedness of the response against the information present in the retrieved context The 2nd approach is more interesting and useful as the majority of LLM applications have an RAG component, and we ideally want the LLM only to utilize the retrieved knowledge to generate the response.…</p><p>5 points; 2 discussion replies on HN. <a href="https://hackerpaper.kaipfister.de/p/2305.14251/">Paper and source history</a>. A submission is not an endorsement.</p>cs.CL