ChainStream: An LLM-based Framework for Unified Synthetic Sensing

• URL: http://arxiv.org/abs/2412.15240v1
• Date: Fri, 13 Dec 2024 08:25:26 GMT
• Title: ChainStream: An LLM-based Framework for Unified Synthetic Sensing
• Authors: Jiacheng Liu, Yuanchun Li, Liangyan Li, Yi Sun, Hao Wen, Xiangyu Li, Yao Guo, Yunxin Liu,
• Abstract summary: We propose to use natural language as the unified interface to process personal data and sense user context. Our work is inspired by large language models (LLMs) and other generative models. To evaluate the performance of natural language-based context sensing, we create a benchmark that contains 133 context sensing tasks.
• Score: 20.589289717423597
• License:
• Abstract: Many applications demand context sensing to offer personalized and timely services. Yet, developing sensing programs can be challenging for developers and using them is privacy-concerning for end-users. In this paper, we propose to use natural language as the unified interface to process personal data and sense user context, which can effectively ease app development and make the data pipeline more transparent. Our work is inspired by large language models (LLMs) and other generative models, while directly applying them does not solve the problem - letting the model directly process the data cannot handle complex sensing requests and letting the model write the data processing program suffers error-prone code generation. We address the problem with 1) a unified data processing framework that makes context-sensing programs simpler and 2) a feedback-guided query optimizer that makes data query more informative. To evaluate the performance of natural language-based context sensing, we create a benchmark that contains 133 context sensing tasks. Extensive evaluation has shown that our approach is able to automatically solve the context-sensing tasks efficiently and precisely. The code is opensourced at https://github.com/MobileLLM/ChainStream.

Related papers

• Likelihood as a Performance Gauge for Retrieval-Augmented Generation [78.28197013467157]
  We show that likelihoods serve as an effective gauge for language model performance. We propose two methods that use question likelihood as a gauge for selecting and constructing prompts that lead to better performance.
  arXiv  Detail & Related papers  (2024-11-12T13:14:09Z)
• Context is Key: A Benchmark for Forecasting with Essential Textual Information [87.3175915185287]
  "Context is Key" (CiK) is a forecasting benchmark that pairs numerical data with diverse types of carefully crafted textual context. We evaluate a range of approaches, including statistical models, time series foundation models, and LLM-based forecasters. We propose a simple yet effective LLM prompting method that outperforms all other tested methods on our benchmark.
  arXiv  Detail & Related papers  (2024-10-24T17:56:08Z)
• The Compressor-Retriever Architecture for Language Model OS [20.56093501980724]
  This paper explores the concept of using a language model as the core component of an operating system (OS) A key challenge in realizing such an LM OS is managing the life-long context and ensuring statefulness across sessions. We introduce compressor-retriever, a model-agnostic architecture designed for life-long context management.
  arXiv  Detail & Related papers  (2024-09-02T23:28:15Z)
• Making Task-Oriented Dialogue Datasets More Natural by Synthetically Generating Indirect User Requests [6.33281463741573]
  Indirect User Requests (IURs) are common in human-human task-oriented dialogue and require world knowledge and pragmatic reasoning from the listener. While large language models (LLMs) can handle these requests effectively, smaller models deployed on virtual assistants often struggle due to resource constraints.
  arXiv  Detail & Related papers  (2024-06-12T01:18:04Z)
• Text2Data: Low-Resource Data Generation with Textual Control [100.5970757736845]
  Text2Data is a novel approach that utilizes unlabeled data to understand the underlying data distribution. It undergoes finetuning via a novel constraint optimization-based learning objective that ensures controllability and effectively counteracts catastrophic forgetting.
  arXiv  Detail & Related papers  (2024-02-08T03:41:39Z)
• Making Retrieval-Augmented Language Models Robust to Irrelevant Context [55.564789967211844]
  An important desideratum of RALMs, is that retrieved information helps model performance when it is relevant. Recent work has shown that retrieval augmentation can sometimes have a negative effect on performance.
  arXiv  Detail & Related papers  (2023-10-02T18:52:35Z)
• IR Design for Application-Specific Natural Language: A Case Study on Traffic Data [29.50290358564987]
  We propose a design for an intermediate representation (IR) that caters to Application-Specific Natural Language (ASNL) Our proposed IR design can achieve a speed improvement of over forty times compared to direct usage of standard XML format data.
  arXiv  Detail & Related papers  (2023-07-13T15:52:05Z)
• Pathway: a fast and flexible unified stream data processing framework for analytical and Machine Learning applications [7.850979932441607]
  Pathway is a new unified data processing framework that can run workloads on both bounded and unbounded data streams. We describe the system and present benchmarking results which demonstrate its capabilities in both batch and streaming contexts.
  arXiv  Detail & Related papers  (2023-07-12T08:27:37Z)
• OverPrompt: Enhancing ChatGPT through Efficient In-Context Learning [49.38867353135258]
  We propose OverPrompt, leveraging the in-context learning capability of LLMs to handle multiple task inputs. Our experiments show that OverPrompt can achieve cost-efficient zero-shot classification without causing significant detriment to task performance.
  arXiv  Detail & Related papers  (2023-05-24T10:08:04Z)
• Self-augmented Data Selection for Few-shot Dialogue Generation [18.794770678708637]
  We adopt the self-training framework to deal with the few-shot MR-to-Text generation problem. We propose a novel data selection strategy to select the data that our generation model is most uncertain about.
  arXiv  Detail & Related papers  (2022-05-19T16:25:50Z)
• SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
  In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines. This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
  arXiv  Detail & Related papers  (2021-12-22T14:45:37Z)

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.