Prior work has shown that large language models (LLMs) often converge to accurate input embedding for numbers, based on sinusoidal representations.In this work, we demonstrate that these representations are in fact strikingly systematic, to the point of being almost perfectly universal: different LLM families develop equivalent sinusoidal structures, and number representations are broadly interchangeable in a large swathe of experimental setups.We show that properly factoring in this characteristic is crucial when it comes to assessing how accurately LLMs encode numeric and other ordinal information, and that mechanistically enhancing this sinusoidality can also lead to reductions of LLMs’ arithmetic errors.

Despite the widespread use of ‘artificial intelligence’ (AI) framing in Natural Language Processing (NLP) research, it is not clear what researchers mean by ”intelligence”. To that end, we present the results of a survey on the notion of ”intelligence” among researchers and its role in the research agenda. The survey elicited complete responses from 303 researchers from a variety of fields including NLP, Machine Learning (ML), Cognitive Science, Linguistics, and Neuroscience.We identify 3 criteria of intelligence that the community agrees on the most: generalization, adaptability, & reasoning.Our results suggests that the perception of the current NLP systems as ”intelligent” is a minority position (29%).Furthermore, only 16.2% of the respondents see developing intelligent systems as a research goal, and these respondents are more likely to consider the current systems intelligent.