BayesPrompt: Few-Shot Inference with Debiased Domain Abstraction

Conceitos Básicos

BayesPrompt proposes a debiased domain abstraction approach to improve few-shot inference by generating discriminative prompts for pre-trained language models.

Resumo

Abstract:

Prompt-tuning aims to bridge the gap between downstream tasks and pre-training objectives.
BayesPrompt addresses the issue of over-multitudinous conceptual knowledge in PLMs affecting few-shot scenarios.
The method approximates debiased factual distributions of downstream domains to generate discriminative prompts.
Introduction:

PLMs excel in general NLP tasks but face challenges in specialized downstream tasks, especially in few-shot scenarios.
Existing prompt-tuning methods struggle with domain-specific knowledge and interference from irrelevant information.
Data Extraction:

"Our method achieves state-of-the-art performance on benchmarks."
"The proposed BayesPrompt achieves the tighter upper bound of the classification error on the downstream inference of PLMs."

Estatísticas

BayesPrompt achieves state-of-the-art performance on benchmarks.
The proposed BayesPrompt achieves the tighter upper bound of the classification error on the downstream inference of PLMs.

Citações

"Our method achieves state-of-the-art performance on benchmarks."
"The proposed BayesPrompt achieves the tighter upper bound of the classification error on the downstream inference of PLMs."

Principais Insights Extraídos De

BayesPrompt

by Jiangmeng Li... às arxiv.org 03-21-2024

https://arxiv.org/pdf/2401.14166.pdf

Perguntas Mais Profundas

How does BayesPrompt compare to other prompt-tuning methods in terms of training complexity

BayesPrompt has a slightly higher training complexity compared to other prompt-tuning methods. This is due to the need to add prompts containing domain discriminative information for downstream tasks during each prediction, providing de-ambiguous guidance for PLMs. In experiments conducted on datasets like SemEval and TACREV, BayesPrompt showed a higher time complexity in terms of training time cost per epoch compared to baseline methods like KnowPrompt. Despite this slight increase in training complexity, BayesPrompt consistently outperformed the baselines in terms of performance improvement.

What are potential limitations or challenges when applying domain adaptation techniques to prompt-tuning

Applying domain adaptation techniques to prompt-tuning may face several limitations or challenges. One major challenge is that traditional domain adaptation assumes identically distributed source and target domains, which may not hold true in prompt-tuning scenarios where the pre-training data distribution differs from the downstream task data distribution. Additionally, fine-tuning models with aligned distributions between specific subsets of PLM domains and downstream domains can perturb the model's ability to capture discriminative information effectively. The limited availability of labeled data for few-shot learning tasks further complicates the application of standard domain adaptation techniques.

How can BayesPrompt's approach be extended to address knowledge ambiguity in other NLP tasks

BayesPrompt's approach can be extended to address knowledge ambiguity in other NLP tasks by approximating debiased factual distributions specific to those tasks and generating prompts containing domain discriminative information against interference from irrelevant knowledge. By leveraging known distributions through Gaussian Mixture Models (GMM) and employing Stein Variational Gradient Descent (SVGD), BayesPrompt can provide de-ambiguous guidance for PLMs across various NLP applications beyond relation extraction tasks studied in this context. This method could enhance model performance by mitigating knowledge ambiguity issues commonly encountered when dealing with diverse datasets or complex language understanding tasks.