Learning sentence representations which capture
rich semantic meanings has been crucial
for many NLP tasks. Pre-trained language
models such as BERT have achieved great
success in NLP, but sentence embeddings extracted
directly from these models do not perform
well without fine-tuning. We propose
Contrastive Learning of Sentence Representations
(CLSR), a novel approach which applies
contrastive learning to learn universal sentence
representations on top of pre-trained language
models. CLSR utilizes semantic similarity of
two sentences to construct positive instance
for contrastive learning. Semantic information
that has been captured by the pre-trained
models is kept by getting sentence embeddings
from these models with proper pooling strategy.
An encoder followed by a linear projection
takes these embeddings as inputs and is
trained under a contrastive objective. To evaluate
the performance of CLSR, we run experiments
on a range of pre-trained language models
and their variants on a series of Semantic
Contextual Similarity tasks. Results show that
CLSR gains significant performance improvements
over existing SOTA language models.
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A Kernel-Based View of Language Model Fine-Tuning
It has become standard to solve NLP tasks by fine-tuning pre-trained language models (LMs), especially in low-data settings. There is minimal theoretical understanding of empirical success, e.g., why fine-tuning a model with $10^8$ or more parameters on a couple dozen training points does not result in overfitting. We investigate whether the Neural Tangent Kernel (NTK)—which originated as a model to study the gradient descent dynamics of infinitely wide networks with suitable random initialization—describes fine-tuning of pre-trained LMs. This study was inspired by the decent performance of NTK for computer vision tasks (Wei et al., 2022). We extend the NTK formalism to Adam and use Tensor Programs (Yang, 2020) to characterize conditions under which the NTK lens may describe fine-tuning updates to pre-trained language models. Extensive experiments on 14 NLP tasks validate our theory and show that formulating the downstream task as a masked word prediction problem through prompting often induces kernel-based dynamics during fine-tuning. Finally, we use this kernel view to propose an explanation for the success of parameter-efficient subspace-based fine-tuning methods.
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- Award ID(s):
- 2211779
- PAR ID:
- 10468147
- Publisher / Repository:
- Proceedings of the 40th International Conference on Machine Learning
- Date Published:
- Volume:
- 202
- Page Range / eLocation ID:
- 23610-23641
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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