Personalized Large Language Models
- URL: http://arxiv.org/abs/2402.09269v1
- Date: Wed, 14 Feb 2024 15:55:30 GMT
- Title: Personalized Large Language Models
- Authors: Stanis{\l}aw Wo\'zniak, Bart{\l}omiej Koptyra, Arkadiusz Janz,
Przemys{\l}aw Kazienko, Jan Koco\'n
- Abstract summary: This paper investigates methods to personalize large language models (LLMs)
Results demonstrate that personalized fine-tuning improves model reasoning compared to non-personalized models.
Experiments on datasets for emotion recognition and hate speech detection show consistent performance gains with personalized methods.
- Score: 8.714932744665958
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Large language models (LLMs) have significantly advanced Natural Language
Processing (NLP) tasks in recent years. However, their universal nature poses
limitations in scenarios requiring personalized responses, such as
recommendation systems and chatbots. This paper investigates methods to
personalize LLMs, comparing fine-tuning and zero-shot reasoning approaches on
subjective tasks. Results demonstrate that personalized fine-tuning improves
model reasoning compared to non-personalized models. Experiments on datasets
for emotion recognition and hate speech detection show consistent performance
gains with personalized methods across different LLM architectures. These
findings underscore the importance of personalization for enhancing LLM
capabilities in subjective text perception tasks.
Related papers
- Evaluating Linguistic Capabilities of Multimodal LLMs in the Lens of Few-Shot Learning [15.919493497867567]
This study aims to evaluate the performance of Multimodal Large Language Models (MLLMs) on the VALSE benchmark.
We conducted a comprehensive assessment of state-of-the-art MLLMs, varying in model size and pretraining datasets.
arXiv Detail & Related papers (2024-07-17T11:26:47Z) - SELF-GUIDE: Better Task-Specific Instruction Following via Self-Synthetic Finetuning [70.21358720599821]
Large language models (LLMs) hold the promise of solving diverse tasks when provided with appropriate natural language prompts.
We propose SELF-GUIDE, a multi-stage mechanism in which we synthesize task-specific input-output pairs from the student LLM.
We report an absolute improvement of approximately 15% for classification tasks and 18% for generation tasks in the benchmark's metrics.
arXiv Detail & Related papers (2024-07-16T04:41:58Z) - 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) - An LLM Feature-based Framework for Dialogue Constructiveness Assessment [8.87747076871578]
Research on dialogue constructiveness focuses on (i) analysing conversational factors that influence individuals to take specific actions, win debates, change their perspectives or broaden their open-mindedness and (ii) predicting constructive outcomes following dialogues for such use cases.
We propose a novel LLM feature-based framework that combines the strengths of feature-based and neural approaches while mitigating their downsides, in assessing dialogue constructiveness.
We apply this framework to three datasets of dialogue constructiveness and find that our LLM feature-based models significantly outperform standard feature-based models and neural models, and tend to learn more robust prediction rules instead of relying on superficial shortcuts.
arXiv Detail & Related papers (2024-06-20T22:10:52Z) - Optimization Methods for Personalizing Large Language Models through Retrieval Augmentation [23.174810143027234]
This paper studies retrieval-augmented approaches for personalizing large language models (LLMs)
We propose the first attempt to optimize the retrieval models that deliver a limited number of personal documents to large language models for the purpose of personalized generation.
arXiv Detail & Related papers (2024-04-09T02:58:05Z) - The Strong Pull of Prior Knowledge in Large Language Models and Its Impact on Emotion Recognition [74.04775677110179]
In-context Learning (ICL) has emerged as a powerful paradigm for performing natural language tasks with Large Language Models (LLM)
We show that LLMs have strong yet inconsistent priors in emotion recognition that ossify their predictions.
Our results suggest that caution is needed when using ICL with larger LLMs for affect-centered tasks outside their pre-training domain.
arXiv Detail & Related papers (2024-03-25T19:07:32Z) - Unveiling the Generalization Power of Fine-Tuned Large Language Models [81.70754292058258]
We investigate whether fine-tuning affects the intrinsic generalization ability intrinsic to Large Language Models (LLMs)
Our main findings reveal that models fine-tuned on generation and classification tasks exhibit dissimilar behaviors in generalizing to different domains and tasks.
We observe that integrating the in-context learning strategy during fine-tuning on generation tasks can enhance the model's generalization ability.
arXiv Detail & Related papers (2024-03-14T08:18:59Z) - LLMvsSmall Model? Large Language Model Based Text Augmentation Enhanced
Personality Detection Model [58.887561071010985]
Personality detection aims to detect one's personality traits underlying in social media posts.
Most existing methods learn post features directly by fine-tuning the pre-trained language models.
We propose a large language model (LLM) based text augmentation enhanced personality detection model.
arXiv Detail & Related papers (2024-03-12T12:10:18Z) - Characterizing Truthfulness in Large Language Model Generations with
Local Intrinsic Dimension [63.330262740414646]
We study how to characterize and predict the truthfulness of texts generated from large language models (LLMs)
We suggest investigating internal activations and quantifying LLM's truthfulness using the local intrinsic dimension (LID) of model activations.
arXiv Detail & Related papers (2024-02-28T04:56:21Z) - Evaluating the Deductive Competence of Large Language Models [0.2218292673050528]
We investigate whether several large language models (LLMs) can solve a classic type of deductive reasoning problem.
We do find performance differences between conditions; however, they do not improve overall performance.
We find that performance interacts with presentation format and content in unexpected ways that differ from human performance.
arXiv Detail & Related papers (2023-09-11T13:47:07Z) - 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)
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.