Exploring the Value of Personalized Word Embeddings

Welch, Charles; Kummerfeld, Jonathan K.; Pérez-Rosas, Verónica; Mihalcea, Rada

doi:10.18653/v1/2020.coling-main.604

Cited by 10 publications

(6 citation statements)

References 25 publications

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“…The diagram by Welch et al (2022) illustrates the utilization of data, models, and metrics (Figure 3). The diagram comprises two primary sections: a left rectangle demonstrating the computation of three similarity metrics and a right rectangle depicting two methods for leveraging similar user data to create personalized models.…”

Section: Transformer-based Personalizationmentioning

confidence: 99%

LLM- Based Personalized Recommendations in Health

Galitsky

2024

Preprint

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Today, LLMs are not good at personalization providing recommendation. They advise physicians and financial advisors to ask professionals in respective fields for help, even having user information available. Answering questions of software professionals, LLM needs to deliver in-depth answers with codes or algorithms, whereas for professionals in other fields would need definitions and main concepts. The intent of this chapter is to make LLM answer tailored to the needs of users, taking into account available information about them. To do that, we need to generalize available information about a person like her health record, maintaining the privacy of this person. We rely on meta-learning techniques to design a LLM prompt to produce a personalization prompt to obtain a suitable relevant information. Such “meta-prompt” is produced by generalization operation applied to available documents for the user. These documents need to be de-identified so that they are sufficient for personalization on one hand and will maintain user privacy on the other hand. The second neuro-symbolic technique to support personalization is abductive reasoning, acting in parallel to LLM fine-tuning. Traditional recommendation and personalization techniques as well as modern, deep learning – based are presented, and the comparison is drawn to the proposed approach. We also share the evaluation and comparative analyses of these approaches. We consider an example for how to build personalization LLM systems coming from Langchain platform. We will explore how to construct chains to form a personalization profile for a user and apply it to user search and recommendation requests.

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Section: Transformer-based Personalizationmentioning

confidence: 99%

LLM- Based Personalized Recommendations in Health

Galitsky

2024

Preprint

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“…In addition to addressing to social and human factors, recent work has also focused on personalized language models (King and Cook, 2020;Jaech and Ostendorf, 2018) learning author representations (Delasalles et al, 2019) and personalized word embeddings (Lin et al, 2017) pointing out the importance of personalized semantics in understanding language. Welch et al (2020b) explore personalized versus generic word representations showing the benefits of both combined. While these models are trained for singular user, Mireshghallah et al (2021) trains a single shared model for all users for personalized sentiment analysis.…”

Section: Related Workmentioning

confidence: 99%

Human Language Modeling

Soni¹,

Matero²,

Balasubramanian³

et al. 2022

Findings of the Association for Computational Linguistics: ACL 2022

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Natural language is generated by people, yet traditional language modeling views words or documents as if generated independently. Here, we propose human language modeling (HuLM), a hierarchical extension to the language modeling problem whereby a humanlevel exists to connect sequences of documents (e.g. social media messages) and capture the notion that human language is moderated by changing human states. We introduce, HaRT, a large-scale transformer model for the HULM task, pre-trained on approximately 100,000 social media users, and demonstrate its effectiveness in terms of both language modeling (perplexity) for social media and fine-tuning for 4 downstream tasks spanning document-and user-levels: stance detection, sentiment classification, age estimation, and personality assessment.1 Results on all tasks meet or surpass the current state-of-the-art.

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“…These may encode, say, that 'funny' is more subjective than 'violent' if organic text shows more disagreement in the usage of the former. Welch et al (2020) show that personalized word embeddings can be built using text from different users. Subjectivity could be extended to richer phrases with more expressive power (e.g., 'reminds me of my childhood').…”

Section: Research Challengesmentioning

confidence: 99%

Subjective Attributes in Conversational Recommendation Systems: Challenges and Opportunities

Radlinski

Boutilier

Ramachandran

et al. 2022

AAAI

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The ubiquity of recommender systems has increased the need for higher-bandwidth, natural and efficient communication with users. This need is increasingly filled by recommenders that support natural language interaction, often conversationally. Given the inherent semantic subjectivity present in natural language, we argue that modeling subjective attributes in recommenders is a critical, yet understudied, avenue of AI research. We propose a novel framework for understanding different forms of subjectivity, examine various recommender tasks that will benefit from a systematic treatment of subjective attributes, and outline a number of research challenges.

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Exploring the Value of Personalized Word Embeddings

Cited by 10 publications

References 25 publications

LLM- Based Personalized Recommendations in Health

LLM- Based Personalized Recommendations in Health

Human Language Modeling

Subjective Attributes in Conversational Recommendation Systems: Challenges and Opportunities

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