@inproceedings{zhao-etal-2024-representation-degeneration,
title = "Representation Degeneration Problem in Prompt-based Models for Natural Language Understanding",
author = "Zhao, Qingyan and
He, Ruifang and
Zhang, Jinpeng and
Liu, Chang and
Wang, Bo",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.1217",
pages = "13946--13957",
abstract = "Prompt-based fine-tuning (PF), by aligning with the training objective of pre-trained language models (PLMs), has shown improved performance on many few-shot natural language understanding (NLU) benchmarks. However, the word embedding space of PLMs exhibits anisotropy, which is called the representation degeneration problem. In this paper, we explore the self-similarity within the same context and identify the anisotropy of the feature embedding space in PF model. Given that the performance of PF models is dependent on feature embeddings, we inevitably pose the hypothesis: this anisotropy limits the performance of the PF models. Based on our experimental findings, we propose CLMA, a Contrastive Learning framework based on the [MASK] token and Answers, to alleviate the anisotropy in the embedding space. By combining our proposed counter-intuitive SSD, a Supervised Signal based on embedding Distance, our approach outperforms mainstream methods on the many NLU benchmarks in the few-shot experimental settings. In subsequent experiments, we analyze the capability of our method to capture deep semantic cues and the impact of the anisotropy in the feature embedding space on the performance of the PF model.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="zhao-etal-2024-representation-degeneration">
<titleInfo>
<title>Representation Degeneration Problem in Prompt-based Models for Natural Language Understanding</title>
</titleInfo>
<name type="personal">
<namePart type="given">Qingyan</namePart>
<namePart type="family">Zhao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ruifang</namePart>
<namePart type="family">He</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jinpeng</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Chang</namePart>
<namePart type="family">Liu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Bo</namePart>
<namePart type="family">Wang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-05</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Nicoletta</namePart>
<namePart type="family">Calzolari</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Min-Yen</namePart>
<namePart type="family">Kan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Veronique</namePart>
<namePart type="family">Hoste</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alessandro</namePart>
<namePart type="family">Lenci</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sakriani</namePart>
<namePart type="family">Sakti</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nianwen</namePart>
<namePart type="family">Xue</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>ELRA and ICCL</publisher>
<place>
<placeTerm type="text">Torino, Italia</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Prompt-based fine-tuning (PF), by aligning with the training objective of pre-trained language models (PLMs), has shown improved performance on many few-shot natural language understanding (NLU) benchmarks. However, the word embedding space of PLMs exhibits anisotropy, which is called the representation degeneration problem. In this paper, we explore the self-similarity within the same context and identify the anisotropy of the feature embedding space in PF model. Given that the performance of PF models is dependent on feature embeddings, we inevitably pose the hypothesis: this anisotropy limits the performance of the PF models. Based on our experimental findings, we propose CLMA, a Contrastive Learning framework based on the [MASK] token and Answers, to alleviate the anisotropy in the embedding space. By combining our proposed counter-intuitive SSD, a Supervised Signal based on embedding Distance, our approach outperforms mainstream methods on the many NLU benchmarks in the few-shot experimental settings. In subsequent experiments, we analyze the capability of our method to capture deep semantic cues and the impact of the anisotropy in the feature embedding space on the performance of the PF model.</abstract>
<identifier type="citekey">zhao-etal-2024-representation-degeneration</identifier>
<location>
<url>https://aclanthology.org/2024.lrec-main.1217</url>
</location>
<part>
<date>2024-05</date>
<extent unit="page">
<start>13946</start>
<end>13957</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Representation Degeneration Problem in Prompt-based Models for Natural Language Understanding
%A Zhao, Qingyan
%A He, Ruifang
%A Zhang, Jinpeng
%A Liu, Chang
%A Wang, Bo
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F zhao-etal-2024-representation-degeneration
%X Prompt-based fine-tuning (PF), by aligning with the training objective of pre-trained language models (PLMs), has shown improved performance on many few-shot natural language understanding (NLU) benchmarks. However, the word embedding space of PLMs exhibits anisotropy, which is called the representation degeneration problem. In this paper, we explore the self-similarity within the same context and identify the anisotropy of the feature embedding space in PF model. Given that the performance of PF models is dependent on feature embeddings, we inevitably pose the hypothesis: this anisotropy limits the performance of the PF models. Based on our experimental findings, we propose CLMA, a Contrastive Learning framework based on the [MASK] token and Answers, to alleviate the anisotropy in the embedding space. By combining our proposed counter-intuitive SSD, a Supervised Signal based on embedding Distance, our approach outperforms mainstream methods on the many NLU benchmarks in the few-shot experimental settings. In subsequent experiments, we analyze the capability of our method to capture deep semantic cues and the impact of the anisotropy in the feature embedding space on the performance of the PF model.
%U https://aclanthology.org/2024.lrec-main.1217
%P 13946-13957
Markdown (Informal)
[Representation Degeneration Problem in Prompt-based Models for Natural Language Understanding](https://aclanthology.org/2024.lrec-main.1217) (Zhao et al., LREC-COLING 2024)
ACL