Compositional Demographic Word Embeddings (2010.02986v2)

Abstract: Word embeddings are usually derived from corpora containing text from many individuals, thus leading to general purpose representations rather than individually personalized representations. While personalized embeddings can be useful to improve LLM performance and other language processing tasks, they can only be computed for people with a large amount of longitudinal data, which is not the case for new users. We propose a new form of personalized word embeddings that use demographic-specific word representations derived compositionally from full or partial demographic information for a user (i.e., gender, age, location, religion). We show that the resulting demographic-aware word representations outperform generic word representations on two tasks for English: LLMing and word associations. We further explore the trade-off between the number of available attributes and their relative effectiveness and discuss the ethical implications of using them.

• The paper proposes two methods to integrate demographic data into word embeddings, reducing perplexity in language modeling.
• The paper demonstrates that demographic-sensitive embeddings achieve superior performance in word association tasks by mirroring human linguistic nuances.
• The paper addresses ethical implications, urging careful use of demographic data to prevent biases and privacy violations in NLP applications.

Compositional Demographic Word Embeddings: Enhancing NLP with Demographic Awareness

Recent strides in NLP have underscored the importance of word embeddings, such as word2vec and GloVe, which encapsulate the syntactic and semantic properties of text. However, these embeddings typically represent a broad spectrum of language rather than catering to individual linguistic variations. Charles Welch, Jonathan K. Kummerfeld, Verónica Pérez-Rosas, and Rada Mihalcea address this limitation with their proposal of compositional demographic word embeddings. This innovative approach focuses on incorporating demographic-specific information into word embeddings, specifically engineered from partial or complete demographic data, including gender, age, location, and religion, to yield personalized word representations.

Methodology

The authors propose two methodologies for constructing personalized word embeddings using demographic data. The first strategy involves demographic attribute vectors, which integrate a generic word matrix with vectors representing each demographic attribute. This allows the model to individually adjust both the word representation and the influence of demographic elements. The second method involves demographic word matrices, wherein each demographic group is characterized by a unique word matrix in addition to a generic word matrix, capturing demographic-specific linguistic nuances.

The team developed their model using a massive corpus of Reddit posts from 61,981 users, where they identified self-reported demographic attributes. This data choice provided a substantial foundation for exploring the nuances in word usage across demographic lines, facilitating a robust evaluation of the approach on two English NLP tasks: LLMing and word associations.

Empirical Results

In LLMing, the demographic matrices demonstrated a notable performance edge over generic embeddings, evidenced by a reduction in perplexity. This suggests the model’s improved capability in predicting the next word in a sequence for demographics-specific scenarios. Even in settings with limited user data, demographic compositions yielded better results, emphasizing their utility in applications such as personalized LLMs, auto-captioning, and voice assistants.

For the word association task, which assesses relatedness between words, the demographic-sensitive embeddings significantly outperformed traditional embeddings. By refining associations through demographic data, these embeddings more accurately mirrored human associations, bolstering their application in nuanced NLP tasks requiring cultural or demographic context.

Theoretical and Practical Implications

Theoretically, this work broadens the conceptual framework of word embeddings to move beyond universal representations, integrating demographic-specific layers that capture variability in language use at a group level. This challenges existing paradigms in NLP by highlighting the benefits of demographic-aware computations over generic models. The distinct insights gained reflect a methodological shift toward more customized NLP applications.

Practically, the enhanced performance of these embeddings underscores their potential in diverse applications where personalized user experiences are crucial. For instance, predictive text inputs, sentiment analysis, and dialog systems could leverage these embeddings to enhance accuracy and user satisfaction by accommodating individual linguistic preferences.

Ethical and Future Considerations

The authors candidly discuss the ethical implications of this approach, acknowledging potential biases and the risk of amplifying demographic stereotypes if mishandled. They caution against using demographic information in ways that could reinforce societal divisions or lead to privacy violations. Future work could expand on these foundations by exploring less common demographics, refining attribution methods, and applying these embeddings in more diverse contexts, such as cross-linguistic NLP tasks.

Overall, compositional demographic word embeddings present a compelling advancement in the personalization of NLP, offering refined interpretations of text that account for demographic nuances. The implementation of such models promises enhanced interaction across various platforms while advocating for ethical and judicious utilization of personalized data in AI systems. Future research can further illuminate the complexities of demographic influences on language, driving both theoretical and practical enhancements in tailored LLMs.