what is caveman-steer fr?

This project is a research experiment about making AI coding assistants give shorter answers without losing accuracy. It builds on a separate popular tool called caveman, which tells an AI model, through its prompt instructions, to drop small words like the and a, and to answer in short fragments rather than full sentences, cutting the length of its answers by roughly two thirds while keeping the technical content correct. The author wanted to know whether a technique called activation steering could shrink answers even further. Activation steering works by nudging the internal numerical state of the model while it is generating text, rather than only changing the text of the instructions given to it. The steering direction used here was built by comparing the model's internal activity when it produces short, caveman style answers against its activity when producing normal, longer answers, and then using the difference between the two as a push in the desired direction. The experiments compare four setups: the model with no special instructions at all, the model given only the caveman style prompt, the model given only the steering nudge with no special prompt, and the model given both the prompt and the steering nudge together. The tests use a language model called Qwen2.5-Coder-7B-Instruct and ask it to explain what a piece of code does in plain language, using a standard benchmark dataset built for that exact task. The results show that using the caveman prompt by itself already shortens answers a great deal, and adding steering on top of the prompt shortens them further still, by about another 13 percent, while a separate check found that the combined approach used far fewer of the discouraged small words like the and a than the prompt alone. Steering by itself, without the prompt, made little difference. Correctness stayed roughly the same, around 90 to 95 percent, across all four setups, based on scoring done by a separate AI model. The repository is written in Python and includes the code used to prepare the data, run the experiments, and generate the charts and example outputs shown in the results.