Manifold-based Verbalizer Space Re-embedding for Tuning-free Prompt-based Classification

• URL: http://arxiv.org/abs/2309.04174v2
• Date: Mon, 29 Jan 2024 12:31:57 GMT
• Title: Manifold-based Verbalizer Space Re-embedding for Tuning-free Prompt-based Classification
• Authors: Haochun Wang, Sendong Zhao, Chi Liu, Nuwa Xi, Muzhen Cai, Bing Qin, Ting Liu
• Abstract summary: We propose a tuning-free manifold-based space re-embedding method called Locally Linear Embedding with Intra-class Neighborhood Constraint. Our approach further enhances prompt-based tuning by up to 3.2%.
• Score: 34.33544689818836
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Prompt-based classification adapts tasks to a cloze question format utilizing the [MASK] token and the filled tokens are then mapped to labels through pre-defined verbalizers. Recent studies have explored the use of verbalizer embeddings to reduce labor in this process. However, all existing studies require a tuning process for either the pre-trained models or additional trainable embeddings. Meanwhile, the distance between high-dimensional verbalizer embeddings should not be measured by Euclidean distance due to the potential for non-linear manifolds in the representation space. In this study, we propose a tuning-free manifold-based space re-embedding method called Locally Linear Embedding with Intra-class Neighborhood Constraint (LLE-INC) for verbalizer embeddings, which preserves local properties within the same class as guidance for classification. Experimental results indicate that even without tuning any parameters, our LLE-INC is on par with automated verbalizers with parameter tuning. And with the parameter updating, our approach further enhances prompt-based tuning by up to 3.2%. Furthermore, experiments with the LLaMA-7B&13B indicate that LLE-INC is an efficient tuning-free classification approach for the hyper-scale language models.

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