https://hackerpaper.kaipfister.de/p/2101.06573/Collected HN comments and submissions linking this paper.enMon, 02 Nov 2026 11:16:22 GMThttps://news.ycombinator.com/item?id=34750312urn:hackerpapers:mention:2101.06573:34750312Sat, 11 Feb 2023 07:20:08 GMT<p>The author says: “Large Language Models don’t understand anything — and they can’t get better at something they can’t do.” Yet, LLM’s can understand (unless one claims that understanding requires consciousness): https://arxiv.org/abs/2101.06573 And, strangely enough, LLM’s can get better at something they can’t do. https://news.mit.edu/2023/large-language-models-in-context-l... https://arxiv.org/pdf/2211.15661.pdf</p><p><a href="https://hackerpaper.kaipfister.de/p/2101.06573/">Paper and discussion history</a>. A mention is not an endorsement.</p>cs.AIhttps://news.ycombinator.com/item?id=34750180urn:hackerpapers:mention:2101.06573:34750180Sat, 11 Feb 2023 06:50:30 GMT<p>I do! So long as we don’t appeal to consciousness as the basis for understanding but measure it with benchmarks, LLMs understand many things. Maetschke, S., Iraola, D. M., Barnard, P., Shafiei Bavani, E., Zhong, P., Xu, Y., &amp; Yepes, A. J. (2021). Understanding in artificial intelligence. arXiv preprint arXiv:2101.06573.</p><p><a href="https://hackerpaper.kaipfister.de/p/2101.06573/">Paper and discussion history</a>. A mention is not an endorsement.</p>cs.AI