Meta-Task Prompting Elicits Embeddings from Large Language Models

• URL: http://arxiv.org/abs/2402.18458v2
• Date: Mon, 22 Jul 2024 09:35:08 GMT
• Title: Meta-Task Prompting Elicits Embeddings from Large Language Models
• Authors: Yibin Lei, Di Wu, Tianyi Zhou, Tao Shen, Yu Cao, Chongyang Tao, Andrew Yates,
• Abstract summary: We introduce a new unsupervised text embedding method, Meta-Task Prompting with Explicit One-Word Limitation. We generate high-quality sentence embeddings from Large Language Models without the need for model fine-tuning. Our findings suggest a new scaling law, offering a versatile and resource-efficient approach for embedding generation across diverse scenarios.
• Score: 54.757445048329735
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a new unsupervised text embedding method, Meta-Task Prompting with Explicit One-Word Limitation (MetaEOL), for generating high-quality sentence embeddings from Large Language Models (LLMs) without the need for model fine-tuning. Leveraging meta-task prompting, MetaEOL guides LLMs to produce embeddings through a series of carefully designed prompts that address multiple representational aspects. Our comprehensive experiments demonstrate that embeddings averaged from various meta-tasks are versatile embeddings that yield competitive performance on Semantic Textual Similarity (STS) benchmarks and excel in downstream tasks, surpassing contrastive-trained models. Our findings suggest a new scaling law, offering a versatile and resource-efficient approach for embedding generation across diverse scenarios.

Related papers

• Unified Generative and Discriminative Training for Multi-modal Large Language Models [88.84491005030316]
  Generative training has enabled Vision-Language Models (VLMs) to tackle various complex tasks. Discriminative training, exemplified by models like CLIP, excels in zero-shot image-text classification and retrieval. This paper proposes a unified approach that integrates the strengths of both paradigms.
  arXiv  Detail & Related papers  (2024-11-01T01:51:31Z)
• LangSuitE: Planning, Controlling and Interacting with Large Language Models in Embodied Text Environments [70.91258869156353]
  We introduce LangSuitE, a versatile and simulation-free testbed featuring 6 representative embodied tasks in textual embodied worlds. Compared with previous LLM-based testbeds, LangSuitE offers adaptability to diverse environments without multiple simulation engines. We devise a novel chain-of-thought (CoT) schema, EmMem, which summarizes embodied states w.r.t. history information.
  arXiv  Detail & Related papers  (2024-06-24T03:36:29Z)
• MetaGPT: Merging Large Language Models Using Model Exclusive Task Arithmetic [6.46176287368784]
  We propose textbfModel textbfExclusive textbfTask textbfArithmetic for merging textbfGPT-scale models. Our proposed MetaGPT is data-agnostic and bypasses the heavy search process, making it cost-effective and easy to implement for LLMs.
  arXiv  Detail & Related papers  (2024-06-17T10:12:45Z)
• Mixture-of-Instructions: Comprehensive Alignment of a Large Language Model through the Mixture of Diverse System Prompting Instructions [7.103987978402038]
  We introduce a novel technique termed Mixture-of-Instructions (MoI) MoI employs a strategy of instruction concatenation combined with diverse system prompts to boost the alignment efficiency of language models. Our methodology was applied to the open-source Qwen-7B-chat model, culminating in the development of Qwen-SFT-MoI.
  arXiv  Detail & Related papers  (2024-04-29T03:58:12Z)
• Exploring the Transferability of Visual Prompting for Multimodal Large Language Models [47.162575147632396]
  Transferable Visual Prompting (TVP) is a simple and effective approach to generate visual prompts that can transfer to different models and improve their performance on downstream tasks after trained on only one model. We introduce two strategies to address the issue of cross-model feature corruption of existing visual prompting methods and enhance the transferability of the learned prompts.
  arXiv  Detail & Related papers  (2024-04-17T09:39:07Z)
• Towards Unified Task Embeddings Across Multiple Models: Bridging the Gap for Prompt-Based Large Language Models and Beyond [16.913115978881866]
  We propose a framework for unified task embeddings (FUTE), task embeddings from various models, including smaller language models and Large Language Models with varied prompts, within a single vector space. Such uniformity enables comparison and analysis of similarities amongst different models, broadening the scope and utility of existing task embedding methods in multi-model scenarios.
  arXiv  Detail & Related papers  (2024-02-22T13:13:31Z)
• Multitask Multimodal Prompted Training for Interactive Embodied Task Completion [48.69347134411864]
  Embodied MultiModal Agent (EMMA) is a unified encoder-decoder model that reasons over images and trajectories. By unifying all tasks as text generation, EMMA learns a language of actions which facilitates transfer across tasks.
  arXiv  Detail & Related papers  (2023-11-07T15:27:52Z)
• MetricPrompt: Prompting Model as a Relevance Metric for Few-shot Text Classification [65.51149771074944]
  MetricPrompt eases verbalizer design difficulty by reformulating few-shot text classification task into text pair relevance estimation task. We conduct experiments on three widely used text classification datasets across four few-shot settings. Results show that MetricPrompt outperforms manual verbalizer and other automatic verbalizer design methods across all few-shot settings.
  arXiv  Detail & Related papers  (2023-06-15T06:51:35Z)

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.