RAG s to Style: Personalizing LLM s with Style Embeddings (2024)

Abstract: This paper studies the use of style embeddings to enhance author profiling for the goal of personalization of Large Language Models (LLMs). Using a style-based Retrieval-Augmented Generation (RAG) approach, we meticulously study the efficacy of style embeddings in capturing distinctive authorial nuances. The proposed method leverages this acquired knowledge to enhance the personalization capabilities of LLMs. In the assessment of this approach, we have employed the LaMP benchmark, specifically tailored for evaluating language models across diverse dimensions of personalization. The empirical observations from our investigation reveal that, in comparison to term matching or context matching, style proves to be marginally superior in the development of personalized LLMs.

## introduction in the dynamic realm of large language models (llms), achieving personalization has evolved from a mere aspiration to a vital goal @xcite . the continuous growth of llms emphasizes the need to customize their outputs based on individual user preferences. however, the challenge lies in bridging the gap between the inherent universal capabilities of these models and the increasing demand for personalized interactions. a relevant example from science fiction illustrates this phenomenon: in marvel comics, jarvis, the personal ai system in iron man, is crucial to tony stark but requires adaptation to peter parker's unique style for optimal service. addressing this challenge requires a deep understanding of individual writing styles. consequently, our research employs style embeddings for author profiling, driven by the question: can style embeddings capture the author profile for user-personalized retrieval, enhancing the overall personalization of llms? this inquiry explores whether the nuances in an author's writing style can be strategically used to improve llm adaptability to individual user preferences. to empirically assess this question, our study adopts the lamp (large language models meet personalization) benchmark @xcite . this benchmark rigorously compares the effectiveness of style embeddings with traditional bm25 retrieval methods. the style embeddings used come from the model introduced by @xcite , complemented by google's flan-t5 @xcite small models for generation. additionally, the flan-t5 base model is used for evaluation, benchmarked against the performance outlined in the original lamp paper. the quantitative insights from this study aim to contribute significantly to the personalized language models landscape. by highlighting the relative importance of a user's stylistic nuances in identifying user patterns compared to the content itself, our research provides a nuanced understanding useful for refining user-specific interactions with llms. the systematic exploration of style embeddings and their impact on author profiling in user-personalized retrieval not only offers empirical clarity but also sets the stage for future advancements in optimizing the personalization capabilities of advanced llms. in an era where personalization is trending, our findings aim to inform and influence the ongoing development of language models, fostering a more sophisticated and effective era of human-computer interactions. ## related work significant strides have been taken in the realms of authorship verification and style embeddings within the existing body of literature. in the pioneering work by @xcite , a distinctive approach to authorship verification was undertaken. their study focused on a sentence embedding model designed explicitly to encapsulate linguistic style, distinguishing itself from conventional sentence transformer models (reimers and gurevych, 2019) that primarily captured textual content and semantics. further exploration in the domain of authorship attribution (aa) and authorship verification (av) was conducted by @xcite . their noteworthy findings revealed a compelling outcome: a conventional n-gram-based model demonstrated superior performance in five out of seven aa tasks, while bert-based models excelled in the remaining two aa tasks and all av tasks. in the pursuit of effective methodologies, coates and bollegala (2018) demonstrated the simplicity and efficacy of employing the average of word embeddings as a "meta" embedding. additionally, the utilization of average word embeddings in author profiling was investigated by @xcite , concluding that this approach outperformed tf-idf(jones, 2021) in the context of author profiling tasks. synthesizing these advancements, we introduce a novel approach that employs the average style embedding as the author's identity. by identifying documents closely aligned with the average embedding, our methodology aims to intricately capture the essence of the author, thereby contributing a distinct perspective to the evolving discourse in this domain. ## dataset we evaluate our methodology using the lamp-7u, lamp-7t, and lamp-4u datasets 1 , each carefully selected to scrutinize distinct facets of our approach. lamp-7 encompasses a tweet paraphrasing dataset, while lamp-4 focuses on a dataset for generating news article titles. the designations "u" and "t" 1 https://lamp-benchmark.github.io/download signify whether the profiles are segregated among different users in the train, validation, and test sets or distributed across distinct timestamps for the same user. the deliberate choice of lamp-7 @xcite stems from its unique composition, housing a tweet paraphrasing dataset that inherently facilitates the effective capture of user style. tweets, characterized by less filtered content compared to articles or abstracts, serve as an ideal substrate for discerning nuanced stylistic elements. extending this rationale, lamp-4 @xcite also captures user style through the titles generated from news articles specific to each user. this meticulous selection of datasets not only ensures a comprehensive evaluation but also underscores the versatility of our approach in accommodating diverse textual genres and user-specific linguistic nuances. ## retrieval model this study specifically directs its attention to one constituent of the lamp architecture-the retrieval model, as depicted in figure 1 . our investigation answers a fundamental query: can style embeddings effectively capture the author profile to facilitate user-personalized retrieval? to accomplish this, we employ the model proposed by @xcite to extract the style embeddings from the input. subsequently, by computing the average of these embeddings to encapsulate the overall stylistic tendencies of the author, we arrange the inputs in descending order based on their cosine similarity to the average embedding. the top k results are then retrieved and employed as input for our llm. our architecture can be seen in figure 2 . the use of the average embedding as a representation of user style proves to be a reasonable choice, as the dimensions within the style vector inherently signify distinct aspects of the user's linguistic style. as studied in coates and bollegala (2018), the simple linear average of the sentence embeddings captures the "meta" information, which in our case, captures the author's writing profile. for instance, considering our focus on lamp-7, a user's tweeting style may encompass frequent usage of abbreviations such as "ur" or "brb." a few dimensions within the style vector quantifies the extent to which the user incorporates abbreviations, thereby contributing to an effective representation of the user's writing style. formally, let t i (1), t i (2), ..., t i (n ) be tweets from user i. let ⃗ s i (t i (1)), ⃗ s i (t i (2)), ..., ⃗ s i (t i (n )) be the the vector embeddings obtained from the style embedding model for each of the tweets. then, we can represent the style of the user as ⃗ s i , where: ## conclusion this study shows the effectiveness of style embeddings in user personalized retrieval and personalization of llms. there is a significant performance increase when using average style embeddings to capture the identity of an author over both term matching retrieval like bm25 and semantic similarity retrieval like contriever @xcite . the author's style, therefore, can be more impor-tant than just the words the author uses. this is a very interesting finding. further research has to be done on this to better represent style as a vector along with using appropriate non-linear functions for combining the vectors and utilize these embeddings for author profiling. there is a concern for privacy of author data when it comes to the task of author profiling. but using the average embedding from a pretrained style embedding model does not require the author to share any data with the owner of the llm. the retrieval and rag can be performed on the user's machine without the need for powerful machinery. hence, the scope for data leaks is very limited. in conclusion, the integration of style embeddings for author profiling within a personalized retrieval framework, as demonstrated through our lamp benchmark evaluations, not only showcases promising advancements in tailoring language models but also underscores the significance of considering individual writing styles for the future development of personalized, context-aware linguistic technologies.