Large Human Language Models: A Need and the Challenges (2024)

Abstract: As research in human-centered NLP advances, there is a growing recognition of the importance of incorporating human and social factors into NLP models. At the same time, our NLP systems have become heavily reliant on LLMs, most of which do not model authors. To build NLP systems that can truly understand human language, we must better integrate human contexts into LLMs. This brings to the fore a range of design considerations and challenges in terms of what human aspects to capture, how to represent them, and what modeling strategies to pursue. To address these, we advocate for three positions toward creating large human language models (LHLMs) using concepts from psychological and behavioral sciences: First, LM training should include the human context. Second, LHLMs should recognize that people are more than their group(s). Third, LHLMs should be able to account for the dynamic and temporally-dependent nature of the human context. We refer to relevant advances and present open challenges that need to be addressed and their possible solutions in realizing these goals.

## introduction language is a fundamental form of human expression and communication of thoughts, emotions, and experiences. learning the meaning of words extends beyond syntax, semantics, and the neighboring words. to truly understand human language, we must look at words in the context of the human generating the language. figure 1 depicts a view of how our language is moderated by our somewhat stable and changing human states of being over time @xcite @xcite . progress in human-centered nlp research has established the importance of modeling human and social factors, presenting a compelling argument that learning language from linguistic signals alone is not adequate @xcite @xcite figure 1 : language expresses the changing human states of being over time. to truly understand human language, language models should have the advantage of the dynamic human context along with the context of its neighboring words. 2020), and noting that feelings, knowledge and mental states of the speaker and listener referred to as the "theory of mind" @xcite , along with other social context variables are vital to language understanding @xcite . this need is backed by a wealth of empirical evidence demonstrating the benefits of modeling human and social factors @xcite @xcite @xcite , and personalized models @xcite @xcite . in parallel, with the advent of transformers @xcite , there have been many advances in language modeling @xcite @xcite yielding transformer-based large language figure 2: language is moderated by multiple factors like who is speaking to whom, where, when, and other factors like demographics, personality, occupation, modes of communication, etc. the author's language is highly dependent on their context, which is referred to as their human context. models (llms) as the base of most current nlp systems. llms train on a pre-training task and are capable of being applied to a broad set of nlp tasks producing state-of-the-art results. however, these language models create word representation without explicitly accounting for the context of the authors. moreover, a person's language can be considered in the rich and complex human context that spans a multitude of aspects. llms can benefit immensely from integrating the human context to truly understand human language but this entails multiple challenges. llms can be seen as containing a multitude of personas, and when prompted or primed appropriately can assume a specific one @xcite . recently, such user-centric prompting has been employed for personalized recommender systems @xcite , dialog systems @xcite , and measuring political biases and fairness @xcite . models such as gpt-3 and chatgpt demonstrate potential for simulating some forms of human context, especially in generative tasks @xcite . continued scaling -building bigger models trained on larger amounts of data -will continue to improve these abilities. however, there are two fundamental limitations to this paradigm. first, models do not explicitly handle the multilevel structure (documents connected to people) necessary for modeling the richness of human context. second, newer paradigms of in-context learning and user-centric prompting can benefit specific settings such as personalization but are still limiting llms from making full use of the human context more broadly. recent evaluations and benchmarks reveal that prompting is insufficient to capture the richness of the human and social context @xcite . instead, in this work we call for a more direct and explicit integration of the human contexts when building language models. in particular, we advocate for including the human context directly in language model training, building rich human contexts that account for the fact that people are more than their groups, and the dynamic and temporaldependent changes to their states of being. in short, we call for building large human language models as a step towards better understanding the human language. we motivate our positions using insights from a large body of past work and discuss shortcomings throughout the text. furthermore, we discuss open challenges in realizing this vision and their possible solutions. social context @xcite encompasses the human context but is not limited to it. in this work, we focus our vision of lhlms in the scope of human context. human context goes far beyond what can be captured from text modality alone. for example, other modalities such as gestures, speech, and body language are also a significant part of humans and their thought processes which gives us a more holistic picture of understanding human expression. however, in this work, we limit ourselves to the human context derived from language. we discuss limitations in detail in section 6. furthermore, lhlms by their very nature are associated with sensitive user information and have the potential to be misused. thus, it becomes essential to adopt a responsible release strategy for such models. we discuss a range of ethical considerations and privacy concerns and implications in section 7. current llms exhibit a form of this ecological fallacy, whereby text sequences written by an author are treated as if independent and miss the opportunity to capture dependence @xcite . conversely, in the absence of a notion of authors, the llms can be seen as modeling the documents from many different people as if they were generated by a single universal author. motivated by this need for interpreting language in its human context and inducing inter-dependence between different text sequences from an individual, we posit the need to train our base large language models with the human context. one broad way to frame human context-aware language modeling is as follows: this human language modeling problem generalizes the regular language modeling problem of predicting the next word conditioned on the previous words in a text sequence x to also condition on a human context h foot_0 . to train llms for this human language modeling problem, we need methods to both represent the human context and to include it in our training objective. ## conclusion building upon the success of two parallels of nlp research: large language models and humancentered nlp, we envision large human language models (lhlms) as the base of future nlp systems. previous positions taken in human-centered nlp advocate for modeling human and social factors @xcite @xcite @xcite . we go further and call for modeling a richer and dynamic human context in our future large language models. a rich human context captures the personal, social, and situational attributes of the person, and represents both static traits and dynamic human states of being. we put forward three specific positions as steps toward integrating this rich human context in language models to realize the vision of large human language models. our roadmap draws on motivations from multiple disciplines, prior advances in human-centered nlp, and organizes the range of challenges to be met in realizing this vision. we call for our nlp research community to take on the challenge of bringing humans, the originators of language, into our large language models.