arXiv:2504.09762v4 Announce Type: replace 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
The proliferation of advanced language models generating intermediate steps for reasoning tasks necessitates a more rigorous and less anthropomorphic interpretation of these internal mechanisms.
A clearer conceptual understanding of intermediate tokens is crucial for developing robust, auditable, and truly intelligent AI, rather than misleading ourselves about their 'thinking processes'.
This position paper challenges the prevalent anthropomorphic language used to describe AI's internal operations, pushing for more precise terminology that accurately reflects model behavior without implying human-like cognition.
- · AI ethicists
- · AI safety researchers
- · Developers of interpretable AI systems
- · AI evangelists promoting anthropomorphic views
- · Users relying on 'reasoning traces' for human-like understanding
Researchers will be encouraged to adopt more precise, mechanistic descriptions of AI's internal workings.
This could lead to a re-evaluation of interpretability methods, moving away from human-analogue explanations.
It might foster new research directions focused on understanding AI systems on their own terms, distinct from human cognitive models.
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Read at arXiv cs.AI