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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Models In a Spelling Bee: Language Models Implicitly Learn the Character Composition of Tokens · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2108.11193/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2108.11193/feed.xml" rel="self" type="application/rss+xml" /><item><title>Models In a Spelling Bee: Language Models Implicitly Learn the Character Composition of Tokens — mentioned by gwern</title><link>https://news.ycombinator.com/item?id=39729059</link><guid isPermaLink="false">urn:hackerpapers:mention:2108.11193:39729059</guid><pubDate>Sat, 16 Mar 2024 20:08:39 GMT</pubDate><description>&lt;p&gt;Correct. Most image models to date have used very small text models, which are unable to spell many words (https://arxiv.org/abs/2108.11193) - if you cannot spell a word letter by letter because you don&amp;#x27;t know what the letters are, how are you going to generate a pixel image of each of those letters...? (It is not as if letters were intrinsically hard. GANs were generating letters easily like 7 years ago.) Their general inability is due to BPEs and non-character-based tokenization. This was noted in the DALL-E 2 paper (https://arxiv.org/pdf/2204.06125.pdf#page=16&amp;amp;org=openai), and it can be…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2108.11193/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CL</category></item></channel></rss>
