title: Large Content And Behavior Models To Understand, Simulate, And Optimize
    Content And Behavior
summary: Shannon, in his seminal paper introducing information theory, divided the
  communication into three levels: technical, semantic, and effectivenss. While
  the technical level is concerned with accurate reconstruction of transmitted
  symbols, the semantic and effectiveness levels deal with the inferred meaning
  and its effect on the receiver. Thanks to telecommunications, the first level
  problem has produced great advances like the internet. Large Language Models
  (LLMs) make some progress towards the second goal, but the third level still
  remains largely untouched. The third problem deals with predicting and
  optimizing communication for desired receiver behavior. LLMs, while showing
  wide generalization capabilities across a wide range of tasks, are unable to
  solve for this. One reason for the underperformance could be a lack of
  "behavior tokens" in LLMs' training corpora. Behavior tokens define receiver
  behavior over a communication, such as shares, likes, clicks, purchases,
  retweets, etc. While preprocessing data for LLM training, behavior tokens are
  often removed from the corpora as noise. Therefore, in this paper, we make some
  initial progress towards reintroducing behavior tokens in LLM training. The
  trained models, other than showing similar performance to LLMs on content
  understanding 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 these
  capabilities. We call these models Large Content and Behavior Models (LCBMs).
  Further, to spur more research on LCBMs, we release our new Content Behavior
  Corpus (CBC), a repository containing communicator, message, and corresponding
  receiver behavior.
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paper_authors.name: Ashmit Khandelwal
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paper_authors.name: Yaman K Singla
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paper_authors.fullname: Yaman
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paper_authors.name: Somesh Singh
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paper_authors.fullname: Somesh Singh
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paper_authors.user: ssingh22
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paper_authors.name: Uttaran Bhattacharya
paper_authors.status: admin_assigned
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paper_authors.fullname: Uttaran Bhattacharya
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paper_authors.name: Ishita Dasgupta
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paper_authors.fullname: Ishita Dasgupta
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paper_authors.name: Stefano Petrangeli
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paper_authors.name: Rajiv Ratn Shah
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paper_authors.fullname: Rajiv Ratn Shah
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paper_authors.name: Changyou Chen
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paper_authors.fullname: Changyou Chen
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paper_authors.name: Balaji Krishnamurthy
paper_title: Large Content And Behavior Models To Understand, Simulate, And Optimize
    Content And Behavior
paper_summary: Shannon, in his seminal paper introducing information theory, divided the
  communication into three levels: technical, semantic, and effectivenss. While
  the technical level is concerned with accurate reconstruction of transmitted
  symbols, the semantic and effectiveness levels deal with the inferred meaning
  and its effect on the receiver. Thanks to telecommunications, the first level
  problem has produced great advances like the internet. Large Language Models
  (LLMs) make some progress towards the second goal, but the third level still
  remains largely untouched. The third problem deals with predicting and
  optimizing communication for desired receiver behavior. LLMs, while showing
  wide generalization capabilities across a wide range of tasks, are unable to
  solve for this. One reason for the underperformance could be a lack of
  "behavior tokens" in LLMs' training corpora. Behavior tokens define receiver
  behavior over a communication, such as shares, likes, clicks, purchases,
  retweets, etc. While preprocessing data for LLM training, behavior tokens are
  often removed from the corpora as noise. Therefore, in this paper, we make some
  initial progress towards reintroducing behavior tokens in LLM training. The
  trained models, other than showing similar performance to LLMs on content
  understanding 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 these
  capabilities. We call these models Large Content and Behavior Models (LCBMs).
  Further, to spur more research on LCBMs, we release our new Content Behavior
  Corpus (CBC), a repository containing communicator, message, and corresponding
  receiver behavior.
paper_ai_summary: Large Content and Behavior Models (LCBMs) are introduced to address the third level of communication by incorporating behavior tokens into LLM training, demonstrating improved generalization on behavior simulation and domain adaptation.
paper_ai_keywords: Large Language Models (LLMs) behavior tokens communication receiver behavior behavior simulation content simulation behavior understanding behavior domain adaptation Large Content and Behavior Models (LCBMs) Content Behavior Corpus (CBC)