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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Thinking Tokens for Language Modeling · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2405.08644/</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/2405.08644/feed.xml" rel="self" type="application/rss+xml" /><item><title>Thinking Tokens for Language Modeling — mentioned by refulgentis</title><link>https://news.ycombinator.com/item?id=43450238</link><guid isPermaLink="false">urn:hackerpapers:mention:2405.08644:43450238</guid><pubDate>Sun, 23 Mar 2025 01:31:18 GMT</pubDate><description>&lt;p&gt;Latent space has a certain shape, which may mean I&amp;#x27;m missing a technical distinction.* There&amp;#x27;s been publications with a pause token (https://arxiv.org/abs/2310.02226), backspace token (https://arxiv.org/abs/2306.05426), or a think token (https://arxiv.org/html/2405.08644v1#Ch0.S4), all of them based on the theory that a generic token can sort of act a placeholder for manipulating attention further without further meaningful output. However, in practice, those approaches haven&amp;#x27;t been used in the training of a large scale model, i.e. I haven&amp;#x27;t seen it at all, the most adventurous people have…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2405.08644/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CL</category></item><item><title>Thinking Tokens for Language Modeling — mentioned by wongarsu</title><link>https://news.ycombinator.com/item?id=42584882</link><guid isPermaLink="false">urn:hackerpapers:mention:2405.08644:42584882</guid><pubDate>Fri, 03 Jan 2025 12:01:06 GMT</pubDate><description>&lt;p&gt;You should get decent results by asking it to do that in the prompt. Just add &amp;quot;if you are uncertain, answer I don&amp;#x27;t know&amp;quot; or &amp;quot;give the answer or say I don&amp;#x27;t know&amp;quot; or something along those lines LLM are far from perfect at knowing their limits, but they are better at it than most people give them credit for. They just never do it unless prompted for it. Fine tuning can improve that ability. For example the thinking tokens paper [1] is at some level training the model to output a special token when it doesn&amp;#x27;t reach a good answer (and then try again, thus &amp;quot;thinking&amp;quot;) 1…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2405.08644/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CL</category></item><item><title>Thinking Tokens for Language Modeling — HN submission</title><link>https://news.ycombinator.com/item?id=40385302</link><guid isPermaLink="false">urn:hackerpapers:submission:2405.08644:40385302</guid><pubDate>Fri, 17 May 2024 01:11:04 GMT</pubDate><description>&lt;p&gt;Thinking Tokens for Language Modeling&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;6 points; 1 discussion reply on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/2405.08644/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.CL</category></item></channel></rss>
