Communication is defined as ``Who says what to whom with what effect.'' A
message from a communicator generates downstream receiver effects, also known
as behavior. Receiver behavior, being a downstream effect of the message,
carries rich signals about it. Even after carrying signals about the message,
the behavior data is often ignored while training large language models. We
show that training LLMs on receiver behavior can actually help improve their
content-understanding abilities. Specifically, we show that training LLMs to
predict the receiver behavior of likes and comments improves the LLM's
performance on a wide variety of downstream content understanding tasks. We
show this performance increase over 40 video and image understanding tasks over
23 benchmark datasets across both 0-shot and fine-tuning settings,
outperforming many supervised baselines. Moreover, since receiver behavior,
such as likes and comments, is collected by default on the internet and does
not need any human annotations to be useful, the performance improvement we get
after training on this data is essentially free-lunch. We release the receiver
behavior cleaned comments and likes of 750k images and videos collected from
multiple platforms along with our instruction-tuning data.

训练语言模型以预测接收者行为，可以改善其对内容的理解能力。通过训练语言模型预测接收者的喜欢和评论行为，可以在多种内容理解任务上显著提高性能。