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Shannon, in his seminal paper introducing information theory, divided thecommunication into three levels: technical, semantic, and effectivenss. Whilethe technical level is concerned with accurate reconstruction of transmittedsymbols, the semantic and effectiveness levels deal with the inferred meaningand its effect on the receiver. Thanks to telecommunications, the first levelproblem has produced great advances like the internet. Large Language Models(LLMs) make some progress towards the second goal, but the third level stillremains largely untouched. The third problem deals with predicting andoptimizing communication for desired receiver behavior. LLMs, while showingwide generalization capabilities across a wide range of tasks, are unable tosolve for this. One reason for the underperformance could be a lack of``behavior tokens'' in LLMs' training corpora. Behavior tokens define receiverbehavior over a communication, such as shares, likes, clicks, purchases,retweets, etc. While preprocessing data for LLM training, behavior tokens areoften removed from the corpora as noise. Therefore, in this paper, we make someinitial progress towards reintroducing behavior tokens in LLM training. Thetrained models, other than showing similar performance to LLMs on contentunderstanding tasks, show generalization capabilities on behavior simulation,content simulation, behavior understanding, and behavior domain adaptation.Using a wide range of tasks on two corpora, we show results on all thesecapabilities. We call these models Large Content and Behavior Models (LCBMs).Further, to spur more research on LCBMs, we release our new Content BehaviorCorpus (CBC), a repository containing communicator, message, and correspondingreceiver behavior.

Large Content And Behavior Models To Understand, Simulate, And Optimize Content And Behavior