arXiv:2504.09762cs.AI
Position: Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!
Abstract
Intermediate token generation (ITG), where a model produces output before the solution, has become a standard method to improve the performance of language models on reasoning tasks. These intermediate tokens have been called \say{reasoning traces} or even \say{thinking traces} -- implicitly anthropomorphizing the traces, and implying that these traces resemble steps a human might take when solving a challenging problem, and as such can provide an interpretable window into the operation of the model's thinking process to the end user. In this position paper, we present evidence that this anthropomorphization isn't a harmless metaphor, and instead is quite dangerous -- it confuses the nature of these models and how to use them effectively, and leads to questionable research. We call on the community to avoid such anthropomorphization of intermediate tokens.
Still not meaningful -- https://arxiv.org/pdf/2504.09762; even for local models, the reasoning traces are often filtered and summarized to sound sensible to humans. And even if not, they don't necessarily represent what the model is thinking.
a2ff6eeb0View on HNParent context