Meta-Task Prompting Elicits Embeddings from Large Language Models
(2402.18458)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 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.
Overview
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Introduces Meta-Task Prompting with Explicit One-Word Limitation (MetaEOL), a novel method for generating sentence embeddings from LLMs without fine-tuning.
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Demonstrates MetaEOL's competitive performance in Semantic Textual Similarity (STS) tasks and superiority in various downstream tasks through comprehensive experiments.
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Employs a unique methodology of using meta-task prompts for embedding generation, highlighting the versatility and efficiency of this approach.
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Flags the theoretical and practical implications of MetaEOL, including its potential in multilingual contexts and scalability challenges, setting a direction for future research.
Meta-Task Prompting with Explicit One-Word Limitation (MetaEOL) Enhances Embedding Extraction from LLMs
Introduction to MetaEOL
The paper presents Meta-Task Prompting with Explicit One-Word Limitation (MetaEOL), an innovative unsupervised approach for generating sentence embeddings from LLMs like GPT-3 and LLaMA. Unlike traditional methods requiring model fine-tuning or specific task engineering, MetaEOL utilizes a series of meta-task prompts to guide LLMs in producing nuanced embeddings. This approach leverages the inherent strength of LLMs in understanding language context without additional training, thus aligning with the zero-resource setting philosophy.
Comprehensive Experiments
The empirical evaluation underscores MetaEOL’s efficiency, positioning it as a competitive alternative to contrastive-trained models for Semantic Textual Similarity (STS) tasks and superior in downstream tasks. Key findings from the experimentation include:
- Meta-Task Averaging: Simply aggregating embeddings from various meta-tasks, without extra training, results in embeddings that rival those from contrastive-trained models across STS benchmarks.
- Integration of Meta-Tasks: Incrementally adding meta-tasks consistently enhances performance across STS tasks, underscoring the value of incorporating diverse perspectives.
- Layer Selection Strategy: Instead of solely relying on the final layer for embedding extraction, employing a proportional layer selection strategy based on models' size yields further improvement.
Methodology
MetaEOL stands out by employing meta-task prompting, where each prompt is tailored to a specific usage scenario or task context. This process generates multiple embeddings for each sentence, reflecting varied representational facets. An exemplary application of MetaEOL involves generating different templates via ChatGPT-4 to capture distinct semantic aspects like Text Classification (TC), Sentiment Analysis (SA), Paraphrase Identification (PI), and Information Extraction (IE).
Analysis and Findings
A thorough analysis reveals several insights:
- Ablation Study: Demonstrated the complementary nature of meta-tasks, with embeddings derived from diverse tasks showing improved performance.
- Task Influence: A direct relationship between the number of meta-tasks used and the overall performance on STS tasks, advocating for the multi-faceted nature of sentence representation.
- Prompt Influence: Investigated the impact of employing multiple prompts for the same meta-task, illustrating that more prompts lead to nuanced embeddings and better STS performance.
- Output Layer Influence: Identified optimal layers for embedding extraction, challenging the conventional wisdom of using only the final layer.
Implications and Future Directions
The novel approach of MetaEOL offers both theoretical and practical implications:
- Theoretical: It challenges existing embedding generation paradigms by demonstrating the effectiveness of unsupervised, prompt-based approaches.
- Practical: MetaEOL presents a viable, cost-effective alternative for embedding generation in resource-constrained settings, mitigating the need for extensive computational resources associated with model training.
MetaEOL's success paves the way for future exploration into multilingual contexts and broader application scenarios, such as document retrieval. As language models continue to evolve, the scalability and adaptability of methods like MetaEOL will undoubtedly play a pivotal role in advancing the state-of-the-art in NLP.
Limitations and Further Work
The paper acknowledges the computational overhead of MetaEOL, given the necessity to process multiple prompts per sentence. Additionally, current evaluations are limited to English and specific types of NLP tasks. Future research could extend MetaEOL's methodology to multilingual settings and explore its efficacy in more expansive task benchmarks, potentially enhancing its utility and applicability in the rapidly evolving landscape of generative AI and NLP.