https://hackerpaper.kaipfister.de/p/1903.10676/Collected HN comments and submissions linking this paper.enMon, 02 Nov 2026 11:16:22 GMThttps://news.ycombinator.com/item?id=33614608urn:hackerpapers:mention:1903.10676:33614608Tue, 15 Nov 2022 20:31:59 GMT<p>I use scientific language models professionally. I skimmed the paper and was immediately disappointed. - They benchmarked against general models like GPT-3 but not well-established specific models that have been trained for specific tasks like SPECTER[0] or SciBert[1]. Specter outperformed GPT-3 on tasks like citation prediction two years ago. Nobody seriously uses general LLMs on science tasks, so nobody who actually wants to use this cares about your benchmarks. I want to see task-specific models compared to your general model, otherwise whats going to happen is I either need to run my own…</p><p><a href="https://hackerpaper.kaipfister.de/p/1903.10676/">Paper and discussion history</a>. A mention is not an endorsement.</p>cs.CLhttps://news.ycombinator.com/item?id=31644446urn:hackerpapers:mention:1903.10676:31644446Mon, 06 Jun 2022 19:04:05 GMT<p>How is this BS? &quot;How AI technology can tame the scientific literature&quot; https://www.nature.com/articles/d41586-018-06617-5 &quot;SCIBERT: A Pretrained Language Model for Scientific Text&quot; https://arxiv.org/pdf/1903.10676.pdf &quot;Domain-specific language model pretraining for biomedical natural language processing&quot; https://dl.acm.org/doi/pdf/10.1145/3458754 These are just examples I pulled up in a couple minutes of Googling. Yeah, the wording here may be a little too grandiose, but nevertheless the fact that AI can read through vast sets of papers and data quickly and help humans sort through it is…</p><p><a href="https://hackerpaper.kaipfister.de/p/1903.10676/">Paper and discussion history</a>. A mention is not an endorsement.</p>cs.CL