PromptBERT: Improving BERT Sentence Embeddings with Prompts (2201.04337v2)

Abstract: We propose PromptBERT, a novel contrastive learning method for learning better sentence representation. We firstly analyze the drawback of current sentence embedding from original BERT and find that it is mainly due to the static token embedding bias and ineffective BERT layers. Then we propose the first prompt-based sentence embeddings method and discuss two prompt representing methods and three prompt searching methods to make BERT achieve better sentence embeddings. Moreover, we propose a novel unsupervised training objective by the technology of template denoising, which substantially shortens the performance gap between the supervised and unsupervised settings. Extensive experiments show the effectiveness of our method. Compared to SimCSE, PromptBert achieves 2.29 and 2.58 points of improvement based on BERT and RoBERTa in the unsupervised setting.

An Analysis of "PromptBERT: Improving BERT Sentence Embeddings with Prompts"

The paper under consideration, "PromptBERT: Improving BERT Sentence Embeddings with Prompts," offers an in-depth exploration into enhancing the efficacy of BERT-derived sentence embeddings by employing a novel prompt-based contrastive learning framework. This research identifies significant limitations present in traditional BERT sentence embeddings and introduces innovative strategies to mitigate these issues, thereby improving performance without necessitating supervised training data.

Key Findings and Methodology

PromptBERT primarily addresses two critical limitations with BERT in sentence embeddings: static token embedding bias and the ineffectiveness of certain BERT layers. The research extends beyond typical heuristic enhancements, proposing the first prompt-based sentence embedding method that leverages prompt representations and template denoising to sidestep biases and harness BERT's intrinsic capabilities more effectively.

Two significant prompts-related methodologies are proposed:

1. Prompt-based Sentence Representation: This involves two approaches where sentences are reformulated as fill-in-the-blank tasks. The paper evaluates the efficacy of representing sentences using the [MASK] token's hidden vector relative to averaging top-k predicted tokens, focusing on reducing the inherent biases present in static embeddings.
2. Prompt Search Strategies: The paper explores various prompt search techniques, including manual crafting, T5-based generation, and OptiPrompt, demonstrating substantial performance improvements when employing optimized templates.

Empirical Evaluation and Results

An impressive array of empirical evaluations supports PromptBERT's utility. Notably, when compared against baseline models, PromptBERT achieves a substantial improvement in the unsupervised setting with 2.29 and 2.58 points higher performance using BERT and RoBERTa respectively when compared to SimCSE.

Furthermore, PromptBERT demonstrates that sentence embeddings derived from BERT can significantly benefit from prompt-based reformulation, achieving performance competitive with supervised fine-tuned models. Results across various Semantic Textual Similarity (STS) tasks further emphasize the robustness of the proposed method.

Implications and Future Directions

The theoretical implications of this research extend to the foundational understanding of sentence embeddings and BERT's role therein. By uncovering latent capabilities within BERT through prompt-based learning, the research points toward a paradigm where unsupervised learning can approach or even achieve parity with supervised models.

Practically, adopting such prompts strategies could enhance the flexibility and scalability of NLP applications where labeled data are scarce or costly to obtain, thus democratizing access to high-quality LLMs in various domains.

The paper also speculates on future research pathways, including automatic generation of optimized templates and deeper investigations into the dynamics between different prompt designs and model architectures. The code and methodologies outlined by PromptBERT may spur further explorations into automated prompt engineering and its applications across diverse NLP tasks.

In conclusion, "PromptBERT: Improving BERT Sentence Embeddings with Prompts" represents a methodologically sound and impactful contribution to the field of NLP, offering rich insights into leveraging prompt strategies to overcome inherent biases and elevate the performance of language representation models. As NLP models and their application domains continue to evolve, such research signifies meaningful strides toward more nuanced and efficient multimodality in language processing technologies.