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Understanding transformer-based language models is becoming increasinglycrucial, particularly as they play pivotal roles in advancing towardsartificial general intelligence. However, language model research facessignificant challenges, especially for academic research groups withconstrained resources. These challenges include complex data structures,unknown target functions, high computational costs and memory requirements, anda lack of interpretability in the inference process, etc. Drawing a parallel tothe use of simple models in scientific research, we propose the concept of ananchor function. This is a type of benchmark function designed for studyinglanguage models in learning tasks that follow an "anchor-key" pattern. Byutilizing the concept of an anchor function, we can construct a series offunctions to simulate various language tasks. The anchor function plays a roleanalogous to that of mice in diabetes research, particularly suitable foracademic research. We demonstrate the utility of the anchor function with anexample, revealing two basic operations by attention structures in languagemodels: shifting tokens and broadcasting one token from one position to manypositions. These operations are also commonly observed in large languagemodels. The anchor function framework, therefore, opens up a series of valuableand accessible research questions for further exploration, especially fortheoretical study.

Anchor function: a type of benchmark functions for studying language models