3 ms·

Explicit <eot> is only used in training. At inference time, the paper says: > A challenge lies in determining when to switch between latent and language modes

Explicit <eot> is only used in training. At inference time, the paper says: > A challenge lies in determining when to switch between latent and language modes. As we focus on the problem-solving setting, we insert a <bot> token immediately following the question tokens. For <eot>, we consider two potential strategies: a) train a binary classifier on latent thoughts to enable the model to autonomously decide when to terminate the latent reasoning, or b) always pad the latent thoughts to a constant length. We found that both approaches work comparably well. Therefore, we use the second option in our experiment for simplicity, unless specified otherwise. (the bottom of the page 4 in the paper pdf, which can be downloaded from https://arxiv.org/abs/2412.06769 https://arxiv.org/abs/2412.06769) Why this point in you summary caught my eye, because the article specifically emphasises non-verbal nature or aspect of reasoning. Internal representaions used by a thinking human are largely not words, and the COCONUT approach tries to model that. Also note, that a whole reasoning step in training data - easily a sentence or more of natural language - can be replaced by a single "Thought" element. (How many Thought elements replace a reasonong step is controlled by a hyperparameter ‘c’; the illustrations are made for ‘c=1’). BTW, one observation: the aipapersacademy.com article in the subject calls the Thought elements "thought tokens", but the original paper never calls them "tokens", just "Thoughts" or "latent thoughts". I suppose the paper carefully avoids that to prevent confusion, as "token" mainly means a linguistic unit in LLMs.

ttul 2y ago
Thanks for your extensive explanation!
- avodonosov 2y ago
  I do it for myself - the desire to post a comment motives to read a little more. A little correction: > Explicit <eot> is only used in training. Of course an explicit <eot> is present in the context at inference time, because the LLM was trained to produce verbal tokens after <eot>. It's just that the <eot> is placed into the context in a one of the two ways above. BTW, I do not understand why the <eot> is not produced by LLM itself, as you describe. It seems reasonable and natural. Is that to save computational performance on unembedding while in the latent thought mode? But unembedding takes a small fraction of computations, should not be an issue. Something prevents reliable learning of how and when to produce the <eot>? But they managed to train a binary classifier. But why separate classifier, why not rely on LLM learning? Another though is that maybe better names for special tokens would be not "begin of thought" (<bot>), "end of thought" (<eot>), but rather something like "pause speak", "begin of speak". Because neither human nor LLM stop thinking when speaking.

ttul 2y ago

Thanks for your extensive explanation!

avodonosov 2y ago
I do it for myself - the desire to post a comment motives to read a little more. A little correction: > Explicit <eot> is only used in training. Of course an explicit <eot> is present in the context at inference time, because the LLM was trained to produce verbal tokens after <eot>. It's just that the <eot> is placed into the context in a one of the two ways above. BTW, I do not understand why the <eot> is not produced by LLM itself, as you describe. It seems reasonable and natural. Is that to save computational performance on unembedding while in the latent thought mode? But unembedding takes a small fraction of computations, should not be an issue. Something prevents reliable learning of how and when to produce the <eot>? But they managed to train a binary classifier. But why separate classifier, why not rely on LLM learning? Another though is that maybe better names for special tokens would be not "begin of thought" (<bot>), "end of thought" (<eot>), but rather something like "pause speak", "begin of speak". Because neither human nor LLM stop thinking when speaking.