Persona-DB: Efficient Large Language Model Personalization for Response Prediction with Collaborative Data Refinement

• URL: http://arxiv.org/abs/2402.11060v1
• Date: Fri, 16 Feb 2024 20:20:43 GMT
• Title: Persona-DB: Efficient Large Language Model Personalization for Response Prediction with Collaborative Data Refinement
• Authors: Chenkai Sun, Ke Yang, Revanth Gangi Reddy, Yi R. Fung, Hou Pong Chan, ChengXiang Zhai, Heng Ji
• Abstract summary: We introduce Persona-DB, a simple framework consisting of a hierarchical construction process to improve generalization across task contexts. In the task of response forecasting, Persona-DB demonstrates superior efficiency in maintaining accuracy with a significantly reduced retrieval size. Our experiments also indicate a marked improvement of over 15% under cold-start scenarios, when users have extremely sparse data.
• Score: 82.56964750522161
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The increasing demand for personalized interactions with large language models (LLMs) calls for the development of methodologies capable of accurately and efficiently identifying user opinions and preferences. Retrieval augmentation emerges as an effective strategy, as it can accommodate a vast number of users without the costs from fine-tuning. Existing research, however, has largely focused on enhancing the retrieval stage and devoted limited exploration toward optimizing the representation of the database, a crucial aspect for tasks such as personalization. In this work, we examine the problem from a novel angle, focusing on how data can be better represented for more efficient retrieval in the context of LLM customization. To tackle this challenge, we introduce Persona-DB, a simple yet effective framework consisting of a hierarchical construction process to improve generalization across task contexts and collaborative refinement to effectively bridge knowledge gaps among users. In the task of response forecasting, Persona-DB demonstrates superior efficiency in maintaining accuracy with a significantly reduced retrieval size, a critical advantage in scenarios with extensive histories or limited context windows. Our experiments also indicate a marked improvement of over 15% under cold-start scenarios, when users have extremely sparse data. Furthermore, our analysis reveals the increasing importance of collaborative knowledge as the retrieval capacity expands.

Related papers

• Robust Utility-Preserving Text Anonymization Based on Large Language Models [80.5266278002083]
  Text anonymization is crucial for sharing sensitive data while maintaining privacy. Existing techniques face the emerging challenges of re-identification attack ability of Large Language Models. This paper proposes a framework composed of three LLM-based components -- a privacy evaluator, a utility evaluator, and an optimization component.
  arXiv  Detail & Related papers  (2024-07-16T14:28:56Z)
• LESS: Selecting Influential Data for Targeted Instruction Tuning [64.78894228923619]
  We propose LESS, an efficient algorithm to estimate data influences and perform Low-rank gradiEnt Similarity Search for instruction data selection. We show that training on a LESS-selected 5% of the data can often outperform training on the full dataset across diverse downstream tasks. Our method goes beyond surface form cues to identify data that the necessary reasoning skills for the intended downstream application.
  arXiv  Detail & Related papers  (2024-02-06T19:18:04Z)
• Knowledge-Augmented Large Language Models for Personalized Contextual Query Suggestion [16.563311988191636]
  We construct an entity-centric knowledge store for each user based on their search and browsing activities on the web. This knowledge store is light-weight, since it only produces user-specific aggregate projections of interests and knowledge onto public knowledge graphs.
  arXiv  Detail & Related papers  (2023-11-10T01:18:47Z)
• Integrating Summarization and Retrieval for Enhanced Personalization via Large Language Models [11.950478880423733]
  Personalization is an essential factor in user experience with natural language processing (NLP) systems. With the emergence of Large Language Models (LLMs), a key question is how to leverage these models to better personalize user experiences. We propose a novel summary-augmented personalization with task-aware user summaries generated by LLMs.
  arXiv  Detail & Related papers  (2023-10-30T23:40:41Z)
• Which Prompts Make The Difference? Data Prioritization For Efficient Human LLM Evaluation [9.452326973655445]
  We find that metric-based methods enhance the efficiency of human evaluations by minimizing the number of required annotations. We show that our method is effective across widely used model families, reducing instances of indecisive (or "tie") outcomes by up to 54%. This potential reduction in required human effort positions our approach as a valuable strategy in future large language model evaluations.
  arXiv  Detail & Related papers  (2023-10-22T21:48:51Z)
• Unlocking the Potential of User Feedback: Leveraging Large Language Model as User Simulator to Enhance Dialogue System [65.93577256431125]
  We propose an alternative approach called User-Guided Response Optimization (UGRO) to combine it with a smaller task-oriented dialogue model. This approach uses LLM as annotation-free user simulator to assess dialogue responses, combining them with smaller fine-tuned end-to-end TOD models. Our approach outperforms previous state-of-the-art (SOTA) results.
  arXiv  Detail & Related papers  (2023-06-16T13:04:56Z)
• Improving Classifier Training Efficiency for Automatic Cyberbullying Detection with Feature Density [58.64907136562178]
  We study the effectiveness of Feature Density (FD) using different linguistically-backed feature preprocessing methods. We hypothesise that estimating dataset complexity allows for the reduction of the number of required experiments. The difference in linguistic complexity of datasets allows us to additionally discuss the efficacy of linguistically-backed word preprocessing.
  arXiv  Detail & Related papers  (2021-11-02T15:48:28Z)
• Self-Supervised Contrastive Learning for Efficient User Satisfaction Prediction in Conversational Agents [35.2098736872247]
  We propose a self-supervised contrastive learning approach to learn user-agent interactions. We show that the pre-trained models using the self-supervised objective are transferable to the user satisfaction prediction. We also propose a novel few-shot transfer learning approach that ensures better transferability for very small sample sizes.
  arXiv  Detail & Related papers  (2020-10-21T18:10:58Z)
• Mining Implicit Entity Preference from User-Item Interaction Data for Knowledge Graph Completion via Adversarial Learning [82.46332224556257]
  We propose a novel adversarial learning approach by leveraging user interaction data for the Knowledge Graph Completion task. Our generator is isolated from user interaction data, and serves to improve the performance of the discriminator. To discover implicit entity preference of users, we design an elaborate collaborative learning algorithms based on graph neural networks.
  arXiv  Detail & Related papers  (2020-03-28T05:47:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.