When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunities [PDF]
, 2023 The advent of large language models marks a revolutionary breakthrough in artificial intelligence. With the unprecedented scale of training and model parameters, the capability of large language models has been dramatically improved, leading to human-like performances in understanding, language synthesizing, and common-sense reasoning, etc.
arxiv +1 more source
CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AI [PDF]
arXiv, 2022 Human language expression is based on the subjective construal of the situation instead of the objective truth conditions, which means that speakers' personalities and emotions after cognitive processing have an important influence on conversation. However, most existing datasets for conversational AI ignore human personalities and emotions, or only ...
arxiv
HRTF Estimation in the Wild [PDF]
arXiv, 2023 Head Related Transfer Functions (HRTFs) play a crucial role in creating immersive spatial audio experiences. However, HRTFs differ significantly from person to person, and traditional methods for estimating personalized HRTFs are expensive, time-consuming, and require specialized equipment.
arxiv
Personalized Automatic Sleep Staging with Single-Night Data: a Pilot Study with KL-Divergence Regularization [PDF]
, 2020 Brain waves vary between people. An obvious way to improve automatic sleep staging for longitudinal sleep monitoring is personalization of algorithms based on individual characteristics extracted from the first night of data. As a single night is a very small amount of data to train a sleep staging model, we propose a Kullback-Leibler (KL) divergence ...
arxiv +1 more source
PersA-FL: Personalized Asynchronous Federated Learning [PDF]
arXiv, 2022 We study the personalized federated learning problem under asynchronous updates. In this problem, each client seeks to obtain a personalized model that simultaneously outperforms local and global models. We consider two optimization-based frameworks for personalization: (i) Model-Agnostic Meta-Learning (MAML) and (ii) Moreau Envelope (ME).
arxiv
Personalized Path Recourse for Reinforcement Learning Agents [PDF]
arXiv, 2023 This paper introduces Personalized Path Recourse, a novel method that generates recourse paths for a reinforcement learning agent. The goal is to edit a given path of actions to achieve desired goals (e.g., better outcomes compared to the agent's original path) while ensuring a high similarity to the agent's original paths and being personalized to the
arxiv
Learning Graph Representation of Person-specific Cognitive Processes from Audio-visual Behaviours for Automatic Personality Recognition [PDF]
arXiv, 2021 This approach builds on two following findings in cognitive science: (i) human cognition partially determines expressed behaviour and is directly linked to true personality traits; and (ii) in dyadic interactions individuals' nonverbal behaviours are influenced by their conversational partner behaviours.
arxiv
Statistical Feature-based Personal Information Detection in Mobile Network Traffic [PDF]
arXiv, 2021 With the popularity of smartphones, mobile applications (apps) have penetrated the daily life of people. Although apps provide rich functionalities, they also access a large amount of personal information simultaneously. As a result, privacy concerns are raised.
arxiv
Controlling Personality Style in Dialogue with Zero-Shot Prompt-Based Learning [PDF]
arXiv, 2023 Prompt-based or in-context learning has achieved high zero-shot performance on many natural language generation (NLG) tasks. Here we explore the performance of prompt-based learning for simultaneously controlling the personality and the semantic accuracy of an NLG for task-oriented dialogue.
arxiv
MIRACLE: Towards Personalized Dialogue Generation with Latent-Space Multiple Personal Attribute Control [PDF]
arXiv, 2023 Personalized dialogue systems aim to endow the chatbot agent with more anthropomorphic traits for human-like interactions. Previous approaches have explored explicitly user profile modeling using text descriptions, implicit derivation of user embeddings, or utilizing handicraft prompts for ChatGPT-like models.
arxiv