Guided Profile Generation Improves Personalization with LLMs

• URL: http://arxiv.org/abs/2409.13093v1
• Date: Thu, 19 Sep 2024 21:29:56 GMT
• Title: Guided Profile Generation Improves Personalization with LLMs
• Authors: Jiarui Zhang,
• Abstract summary: In modern commercial systems, including Recommendation, Ranking, and E-Commerce platforms, there is a trend towards incorporating Personalization context as input into Large Language Models (LLMs) We propose Guided Profile Generation (GPG), a general method designed to generate personal profiles in natural language. Our experimental results show that GPG improves LLM's personalization ability across different tasks, for example, it increases 37% accuracy in predicting personal preference compared to directly feeding the LLMs with raw personal context.
• Score: 3.2685922749445617
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In modern commercial systems, including Recommendation, Ranking, and E-Commerce platforms, there is a trend towards improving customer experiences by incorporating Personalization context as input into Large Language Models (LLMs). However, LLMs often struggle to effectively parse and utilize sparse and complex personal context without additional processing or contextual enrichment, underscoring the need for more sophisticated context understanding mechanisms. In this work, we propose Guided Profile Generation (GPG), a general method designed to generate personal profiles in natural language. As is observed, intermediate guided profile generation enables LLMs to summarize, and extract the important, distinctive features from the personal context into concise, descriptive sentences, precisely tailoring their generation more closely to an individual's unique habits and preferences. Our experimental results show that GPG improves LLM's personalization ability across different tasks, for example, it increases 37% accuracy in predicting personal preference compared to directly feeding the LLMs with raw personal context.

Related papers

• Personalization of Large Language Models: A Survey [131.00650432814268]
  Personalization of Large Language Models (LLMs) has recently become increasingly important with a wide range of applications. Most existing works on personalized LLMs have focused either entirely on (a) personalized text generation or (b) leveraging LLMs for personalization-related downstream applications, such as recommendation systems. We introduce a taxonomy for personalized LLM usage and summarizing the key differences and challenges.
  arXiv  Detail & Related papers  (2024-10-29T04:01:11Z)
• MetaAlign: Align Large Language Models with Diverse Preferences during Inference Time [50.41806216615488]
  Large Language Models (LLMs) acquire extensive knowledge and remarkable abilities from extensive text corpora. To make LLMs more usable, aligning them with human preferences is essential. We propose an effective method, textbf MetaAlign, which aims to help LLMs dynamically align with various explicit or implicit preferences specified at inference time.
  arXiv  Detail & Related papers  (2024-10-18T05:31:13Z)
• Aligning LLMs with Individual Preferences via Interaction [51.72200436159636]
  We train large language models (LLMs) that can ''interact to align'' We develop a multi-turn preference dataset containing 3K+ multi-turn conversations in tree structures. For evaluation, we establish the ALOE benchmark, consisting of 100 carefully selected examples and well-designed metrics to measure the customized alignment performance during conversations.
  arXiv  Detail & Related papers  (2024-10-04T17:48:29Z)
• PersonalLLM: Tailoring LLMs to Individual Preferences [11.717169516971856]
  We present a public benchmark, PersonalLLM, focusing on adapting LLMs to provide maximal benefits for a particular user. We curate open-ended prompts paired with many high-quality answers over which users would be expected to display heterogeneous latent preferences. Our dataset and generated personalities offer an innovative testbed for developing personalization algorithms.
  arXiv  Detail & Related papers  (2024-09-30T13:55:42Z)
• Orchestrating LLMs with Different Personalizations [28.344891363780576]
  This paper presents a novel approach to aligning large language models (LLMs) with individual human preferences. Given stated preferences along multiple dimensions, such as helpfulness, conciseness, or humor, the goal is to create an LLM without re-training that best adheres to this specification. Starting from specialized expert LLMs, each trained for one particular preference dimension, we propose a black-box method that merges their outputs on a per-token level.
  arXiv  Detail & Related papers  (2024-07-04T22:55:02Z)
• Few-shot Personalization of LLMs with Mis-aligned Responses [40.0349773257245]
  This paper proposes a new approach for a few-shot personalization of large language models (LLMs) Our key idea is to learn a set of personalized prompts for each user by progressively improving the prompts using LLMs. During an iterative process of prompt improvement, we incorporate the contexts of mis-aligned responses by LLMs.
  arXiv  Detail & Related papers  (2024-06-26T18:29:12Z)
• Step-Back Profiling: Distilling User History for Personalized Scientific Writing [50.481041470669766]
  Large language models (LLM) excel at a variety of natural language processing tasks, yet they struggle to generate personalized content for individuals. We introduce STEP-BACK PROFILING to personalize LLMs by distilling user history into concise profiles. Our approach outperforms the baselines by up to 3.6 points on the general personalization benchmark.
  arXiv  Detail & Related papers  (2024-06-20T12:58:26Z)
• Aligning Large Language Models with Self-generated Preference Data [72.99676237703099]
  We propose a new framework that boosts the alignment of large language models (LLMs) with human preferences. Our key idea is leveraging the human prior knowledge within the small (seed) data. We introduce a noise-aware preference learning algorithm to mitigate the risk of low quality within generated preference data.
  arXiv  Detail & Related papers  (2024-06-06T18:01:02Z)
• PMG : Personalized Multimodal Generation with Large Language Models [20.778869086174137]
  This paper proposes the first method for personalized multimodal generation using large language models (LLMs) It showcases its applications and validates its performance via an extensive experimental study on two datasets. PMG has a significant improvement on personalization for up to 8% in terms of LPIPS while retaining the accuracy of generation.
  arXiv  Detail & Related papers  (2024-04-07T03:05:57Z)
• LMPriors: Pre-Trained Language Models as Task-Specific Priors [78.97143833642971]
  We develop principled techniques for augmenting our models with suitable priors. This is to encourage them to learn in ways that are compatible with our understanding of the world. We draw inspiration from the recent successes of large-scale language models (LMs) to construct task-specific priors distilled from the rich knowledge of LMs.
  arXiv  Detail & Related papers  (2022-10-22T19:09:18Z)

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.