ZzzGPT: An Interactive GPT Approach to Enhance Sleep Quality

• URL: http://arxiv.org/abs/2310.16242v2
• Date: Tue, 7 May 2024 00:20:30 GMT
• Title: ZzzGPT: An Interactive GPT Approach to Enhance Sleep Quality
• Authors: Yonchanok Khaokaew, Kaixin Ji, Thuc Hanh Nguyen, Hiruni Kegalle, Marwah Alaofi, Hao Xue, Flora D. Salim,
• Abstract summary: This paper explores the intersection of technology and sleep pattern comprehension, presenting a cutting-edge framework that harnesses the power of Large Language Models (LLMs) The primary objective is to deliver precise sleep predictions paired with actionable feedback, addressing the limitations of existing solutions.
• Score: 9.249102003239663
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper explores the intersection of technology and sleep pattern comprehension, presenting a cutting-edge two-stage framework that harnesses the power of Large Language Models (LLMs). The primary objective is to deliver precise sleep predictions paired with actionable feedback, addressing the limitations of existing solutions. This innovative approach involves leveraging the GLOBEM dataset alongside synthetic data generated by LLMs. The results highlight significant improvements, underlining the efficacy of merging advanced machine-learning techniques with a user-centric design ethos. Through this exploration, we bridge the gap between technological sophistication and user-friendly design, ensuring that our framework yields accurate predictions and translates them into actionable insights.

Related papers

• Training Large Recommendation Models via Graph-Language Token Alignment [53.3142545812349]
  We propose a novel framework to train Large Recommendation models via Graph-Language Token Alignment. By aligning item and user nodes from the interaction graph with pretrained LLM tokens, GLTA effectively leverages the reasoning abilities of LLMs. Furthermore, we introduce Graph-Language Logits Matching (GLLM) to optimize token alignment for end-to-end item prediction.
  arXiv  Detail & Related papers  (2025-02-26T02:19:10Z)
• AlphaMaze: Enhancing Large Language Models' Spatial Intelligence via GRPO [0.0]
  Large Language Models (LLMs) have demonstrated impressive capabilities in language processing, yet they often struggle with tasks requiring visual spatial reasoning. We introduce a novel two-stage training framework designed to equip standard LLMs with visual reasoning abilities for maze navigation.
  arXiv  Detail & Related papers  (2025-02-20T16:05:18Z)
• A Survey on Inference Optimization Techniques for Mixture of Experts Models [50.40325411764262]
  Large-scale Mixture of Experts (MoE) models offer enhanced model capacity and computational efficiency through conditional computation. deploying and running inference on these models presents significant challenges in computational resources, latency, and energy efficiency. This survey analyzes optimization techniques for MoE models across the entire system stack.
  arXiv  Detail & Related papers  (2024-12-18T14:11:15Z)
• Closer Look at Efficient Inference Methods: A Survey of Speculative Decoding [1.3479499607624648]
  Speculative decoding addresses bottleneck by introducing a two-stage framework: drafting and verification. A smaller, efficient model generates a preliminary draft, which is then refined by a larger, more sophisticated model. This paper provides a comprehensive survey of speculative decoding methods, categorizing them into draft-centric and model-centric approaches.
  arXiv  Detail & Related papers  (2024-11-20T09:46:30Z)
• Fine-Grained Verifiers: Preference Modeling as Next-token Prediction in Vision-Language Alignment [57.0121616203175]
  We propose FiSAO, a novel self-alignment method that utilizes the model's own visual encoder as a fine-grained verifier to improve vision-language alignment. By leveraging token-level feedback from the vision encoder, FiSAO significantly improves vision-language alignment, even surpassing traditional preference tuning methods that require additional data.
  arXiv  Detail & Related papers  (2024-10-18T03:34:32Z)
• Seeing Eye to AI: Human Alignment via Gaze-Based Response Rewards for Large Language Models [46.09562860220433]
  We introduce GazeReward, a novel framework that integrates implicit feedback -- and specifically eye-tracking (ET) data -- into the Reward Model (RM) Our approach significantly improves the accuracy of the RM on established human preference datasets.
  arXiv  Detail & Related papers  (2024-10-02T13:24:56Z)
• Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application [54.984348122105516]
  Large Language Models (LLMs) pretrained on massive text corpus presents a promising avenue for enhancing recommender systems. We propose an Llm-driven knowlEdge Adaptive RecommeNdation (LEARN) framework that synergizes open-world knowledge with collaborative knowledge.
  arXiv  Detail & Related papers  (2024-05-07T04:00:30Z)
• Adapting LLMs for Efficient Context Processing through Soft Prompt Compression [1.1550486371582305]
  This article presents an innovative framework that strategically tailors Large Language Models for streamlined context processing. Our methodology, dubbed SoftPromptComp, amalgamates natural language prompts with dynamically generated soft prompts to forge a concise yet semantically robust depiction of protracted contexts. We substantiate that our framework markedly diminishes computational overhead and enhances LLMs' efficacy across various benchmarks.
  arXiv  Detail & Related papers  (2024-04-07T15:44:20Z)
• Entropy-Regularized Token-Level Policy Optimization for Language Agent Reinforcement [67.1393112206885]
  Large Language Models (LLMs) have shown promise as intelligent agents in interactive decision-making tasks. We introduce Entropy-Regularized Token-level Policy Optimization (ETPO), an entropy-augmented RL method tailored for optimizing LLMs at the token level. We assess the effectiveness of ETPO within a simulated environment that models data science code generation as a series of multi-step interactive tasks.
  arXiv  Detail & Related papers  (2024-02-09T07:45:26Z)
• Contextualization Distillation from Large Language Model for Knowledge Graph Completion [51.126166442122546]
  We introduce the Contextualization Distillation strategy, a plug-in-and-play approach compatible with both discriminative and generative KGC frameworks. Our method begins by instructing large language models to transform compact, structural triplets into context-rich segments. Comprehensive evaluations across diverse datasets and KGC techniques highlight the efficacy and adaptability of our approach.
  arXiv  Detail & Related papers  (2024-01-28T08:56:49Z)
• Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to Systems [14.355768064425598]
  generative large language models (LLMs) stand at the forefront, revolutionizing how we interact with our data. However, the computational intensity and memory consumption of deploying these models present substantial challenges in terms of serving efficiency. This survey addresses the imperative need for efficient LLM serving methodologies from a machine learning system (MLSys) research perspective.
  arXiv  Detail & Related papers  (2023-12-23T11:57:53Z)
• Enhancing Explainability in Mobility Data Science through a combination of methods [0.08192907805418582]
  This paper introduces a comprehensive framework that harmonizes pivotal XAI techniques. LIMEInterpretable Model-a-gnostic Explanations, SHAP, Saliency maps, attention mechanisms, direct trajectory visualization, and Permutation Feature (PFI) To validate our framework, we undertook a survey to gauge preferences and reception among various user demographics.
  arXiv  Detail & Related papers  (2023-12-01T07:09:21Z)
• CLIP-based Synergistic Knowledge Transfer for Text-based Person Retrieval [66.93563107820687]
  We introduce a CLIP-based Synergistic Knowledge Transfer (CSKT) approach for Person Retrieval (TPR) To explore the CLIP's knowledge on input side, we first propose a Bidirectional Prompts Transferring (BPT) module constructed by text-to-image and image-to-text bidirectional prompts and coupling projections. CSKT outperforms the state-of-the-art approaches across three benchmark datasets when the training parameters merely account for 7.4% of the entire model.
  arXiv  Detail & Related papers  (2023-09-18T05:38:49Z)
• End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes [52.818579746354665]
  This paper proposes the first end-to-end differentiable meta-BO framework that generalises neural processes to learn acquisition functions via transformer architectures. We enable this end-to-end framework with reinforcement learning (RL) to tackle the lack of labelled acquisition data.
  arXiv  Detail & Related papers  (2023-05-25T10:58:46Z)

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.