Large Language Models Fail to Capture the Richness of Human Language: A Call for Integrating Dynamic Human Context

Core Concepts

To truly understand human language, language models must directly integrate the rich and dynamic human context that shapes how people express themselves, rather than relying on linguistic signals alone.

Abstract

The content discusses the need for large language models (LLMs) to better incorporate human context, which encompasses the various personal, social, and situational factors that influence how people use language. It makes three key arguments:

LM training should include the human context: Current LLMs treat text sequences as independent, missing the opportunity to capture the dependence between an individual's language use and their unique human context. Integrating the human context directly into LM training can lead to better language understanding.

LHLMs should recognize that people are more than their group(s): Human context is not limited to discrete group memberships, but rather a rich mixture of continuous individual traits and characteristics. LHLMs should model this diversity and intersectionality of human factors, rather than relying on narrow group-based representations.

LHLMs should account for the dynamic and temporally-dependent nature of human context: A person's language expresses their changing states of being over time, influenced by factors like mood, personality, and temporal rhythms. LHLMs should capture these dynamic and temporal aspects of the human context to better model human language.

The content reviews relevant past work, discusses key challenges, and proposes potential solutions for realizing this vision of large human language models (LHLMs). It emphasizes the need for representative datasets, scalable modeling approaches, and responsible development strategies to address privacy and ethical concerns.

Stats

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Quotes

"Serious errors can result when an investigator makes the seemingly natural assumption that the inference from an ecological analysis must pertain either to individuals within the group or to individuals across groups."
"[P]eople from the collectivist culture produc[e] significantly more group and fewer idiocentric self-descriptions than ... people from the individualist cultures"
"[P]eople are embedded within time, ... time is fundamentally important to life as it is lived, and ... personality processes take place over time."

Key Insights Distilled From

Large Human Language Models

by Niki... at arxiv.org 04-03-2024

https://arxiv.org/pdf/2312.07751.pdf

Deeper Inquiries

How can we ensure the responsible development and deployment of large human language models to prevent misuse and protect individual privacy?

Responsible development and deployment of large human language models are crucial to prevent misuse and protect individual privacy. Several strategies can be implemented to ensure ethical practices:

Data Privacy and Consent: Obtain explicit consent from users before using their data for training models. Implement robust data anonymization techniques to protect sensitive information.

Transparency and Accountability: Maintain transparency in model development, including disclosing data sources, model architecture, and potential biases. Establish accountability mechanisms for any misuse of the models.

Fairness and Bias Mitigation: Regularly audit models for biases and ensure fairness in predictions. Implement debiasing techniques to mitigate any unfair outcomes.

User Empowerment: Provide users with control over their data and the ability to opt-out of data collection or model usage. Educate users about how their data is being used.

Regulatory Compliance: Adhere to data protection regulations such as GDPR and ensure compliance with ethical guidelines and standards set by regulatory bodies.

Continuous Monitoring and Evaluation: Regularly monitor model performance and behavior to detect any potential misuse or privacy breaches. Conduct thorough evaluations to assess the impact of the models on individuals and society.

By incorporating these measures, developers can ensure that large human language models are developed and deployed responsibly, safeguarding individual privacy and preventing misuse.

How might the integration of multimodal human context, beyond just language, further enhance our understanding of human expression and communication?

Integrating multimodal human context, including aspects beyond language such as gestures, speech, and body language, can significantly enhance our understanding of human expression and communication in several ways:

Holistic Understanding: By considering multiple modalities, we can capture a more comprehensive view of human expression, incorporating non-verbal cues that play a crucial role in communication.

Emotional Intelligence: Non-verbal cues like facial expressions and tone of voice convey emotions that are integral to communication. Integrating these cues with language can provide deeper insights into the emotional states of individuals.

Contextual Enrichment: Multimodal data can enrich the context of language, providing additional layers of information that enhance the interpretation of verbal communication.

Cultural Sensitivity: Non-verbal cues often vary across cultures and can influence communication. Integrating multimodal context can help in understanding and respecting cultural differences in expression.

Personalization and Adaptation: Multimodal data can enable personalized communication models that adapt to individual preferences and communication styles, leading to more effective interactions.

Overall, the integration of multimodal human context can offer a more nuanced and nuanced understanding of human expression and communication, leading to more accurate and contextually rich language models.

What insights from other disciplines, such as psychology, sociology, and anthropology, could inspire novel approaches to modeling the richness and dynamism of human context in language models?

Insights from psychology, sociology, and anthropology can inspire novel approaches to modeling the richness and dynamism of human context in language models:

Psychology: Psychological theories on personality traits, emotions, and behavior can inform the modeling of individual characteristics and dynamic states in language models. Concepts like the Big Five personality traits can be integrated to capture nuanced human attributes.

Sociology: Sociological perspectives on social structures, group dynamics, and cultural influences can guide the modeling of social context in language models. Understanding societal norms and values can help in contextualizing language use.

Anthropology: Anthropological studies on cultural practices, rituals, and traditions can provide insights into the diversity of human expression. By incorporating anthropological perspectives, language models can better capture the cultural nuances in communication.

Behavioral Sciences: Insights from behavioral sciences, such as behavioral economics and cognitive psychology, can offer valuable frameworks for understanding decision-making processes and cognitive biases reflected in language use. These insights can enhance the contextual understanding of human language.

By drawing from these interdisciplinary fields, language models can be enriched with a deeper understanding of human context, encompassing a wide range of individual, social, and cultural factors that shape human expression and communication.