“Paper close reading: “Why Language Models Hallucinate”” by LawrenceC

People often talk about paper reading as a skill, but there aren’t that many examples of people walking through how they do it. Part of this is a problem of supply: it's expensive to document one's thought process for any significant length of time, and there's the additional cost of probably looking quite foolish when doing so. Part of this is simply a question of demand: far more people will read a short paragraph or tweet thread summarizing a paper and offering some pithy comments, than a thousand-word post of someone's train of thought as they look through a paper. Thankfully, I’m willing to risk looking a bit foolish, and I’m pretty unresponsive to demand at this present moment, so I’ll try and write down my thought processes as I read through as much of a a paper I can in 1-2 hours. Standard disclaimers apply: this is unlikely to be fully faithful for numerous reasons, including the fact that I read and think substantially faster than I can type or talk.[1] Specifically, I tried to do this for a paper from last year: “Why Language Models Hallucinate”, by Kalai et al at OpenAI.[2] Due to time [...] ---Outline:(01:25) The Abstract(08:37) The Introduction(08:49) A quick sanity check of examples in the introduction(12:45) A digression on computational learning theory(14:38) Key result #1: relating generation error to binary classification error(15:29) Key result #2: The original text contained 6 footnotes which were omitted from this narration. --- First published: April 5th, 2026 Source: https://www.lesswrong.com/posts/rAjtnXx5qLgubsrGQ/paper-close-reading-why-language-models-hallucinate --- Narrated by TYPE III AUDIO. ---Images from the article:Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.