MergeDistill: Merging Pre-trained Language Models using Distillation
- URL: http://arxiv.org/abs/2106.02834v1
- Date: Sat, 5 Jun 2021 08:22:05 GMT
- Title: MergeDistill: Merging Pre-trained Language Models using Distillation
- Authors: Simran Khanuja, Melvin Johnson, Partha Talukdar
- Abstract summary: We propose MergeDistill, a framework to merge pre-trained LMs in a way that can best leverage their assets with minimal dependencies.
We demonstrate the applicability of our framework in a practical setting by leveraging pre-existing teacher LMs and training student LMs that perform competitively with or even outperform teacher LMs trained on several orders of magnitude more data and with a fixed model capacity.
- Score: 5.396915402673246
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Pre-trained multilingual language models (LMs) have achieved state-of-the-art
results in cross-lingual transfer, but they often lead to an inequitable
representation of languages due to limited capacity, skewed pre-training data,
and sub-optimal vocabularies. This has prompted the creation of an ever-growing
pre-trained model universe, where each model is trained on large amounts of
language or domain specific data with a carefully curated, linguistically
informed vocabulary. However, doing so brings us back full circle and prevents
one from leveraging the benefits of multilinguality. To address the gaps at
both ends of the spectrum, we propose MergeDistill, a framework to merge
pre-trained LMs in a way that can best leverage their assets with minimal
dependencies, using task-agnostic knowledge distillation. We demonstrate the
applicability of our framework in a practical setting by leveraging
pre-existing teacher LMs and training student LMs that perform competitively
with or even outperform teacher LMs trained on several orders of magnitude more
data and with a fixed model capacity. We also highlight the importance of
teacher selection and its impact on student model performance.
Related papers
- Extracting and Transferring Abilities For Building Multi-lingual Ability-enhanced Large Language Models [104.96990850774566]
We propose a Multi-lingual Ability Extraction and Transfer approach, named as MAET.
Our key idea is to decompose and extract language-agnostic ability-related weights from large language models.
Experiment results show MAET can effectively and efficiently extract and transfer the advanced abilities, and outperform training-based baseline methods.
arXiv Detail & Related papers (2024-10-10T11:23:18Z) - Self-Translate-Train: Enhancing Cross-Lingual Transfer of Large Language Models via Inherent Capability [31.025371443719404]
Self-Translate-Train is a method that lets large language models translate training data into the target language and fine-tunes the model on its own generated data.
By demonstrating that Self-Translate-Train outperforms zero-shot transfer, we encourage further exploration of better methods to elicit cross-lingual capabilities of LLMs.
arXiv Detail & Related papers (2024-06-29T14:40:23Z) - ColBERT-XM: A Modular Multi-Vector Representation Model for Zero-Shot
Multilingual Information Retrieval [10.664434993386523]
Current approaches circumvent the lack of high-quality labeled data in non-English languages.
We present a novel modular dense retrieval model that learns from the rich data of a single high-resource language.
arXiv Detail & Related papers (2024-02-23T02:21:24Z) - Cross-Lingual NER for Financial Transaction Data in Low-Resource
Languages [70.25418443146435]
We propose an efficient modeling framework for cross-lingual named entity recognition in semi-structured text data.
We employ two independent datasets of SMSs in English and Arabic, each carrying semi-structured banking transaction information.
With access to only 30 labeled samples, our model can generalize the recognition of merchants, amounts, and other fields from English to Arabic.
arXiv Detail & Related papers (2023-07-16T00:45:42Z) - PolyLM: An Open Source Polyglot Large Language Model [57.64420154135178]
We present PolyLM, a multilingual large language model (LLMs) trained on 640 billion (B) tokens, avaliable in two model sizes: 1.7B and 13B.
To enhance its multilingual capabilities, we 1) integrate bilingual data into training data; and 2) adopt a curriculum learning strategy that increases the proportion of non-English data from 30% in the first stage to 60% in the final stage during pre-training.
Further, we propose a multilingual self-instruct method which automatically generates 132.7K diverse multilingual instructions for model fine-tuning.
arXiv Detail & Related papers (2023-07-12T09:00:37Z) - Cross-Lingual Text Classification with Multilingual Distillation and
Zero-Shot-Aware Training [21.934439663979663]
Multi-branch multilingual language model (MBLM) built on Multilingual pre-trained language models (MPLMs)
Method based on transferring knowledge from high-performance monolingual models with a teacher-student framework.
Results on two cross-lingual classification tasks show that, with only the task's supervised data used, our method improves both the supervised and zero-shot performance of MPLMs.
arXiv Detail & Related papers (2022-02-28T09:51:32Z) - Bilingual Alignment Pre-training for Zero-shot Cross-lingual Transfer [33.680292990007366]
In this paper, we aim to improve the zero-shot cross-lingual transfer performance by aligning the embeddings better.
We propose a pre-training task named Alignment Language Model (AlignLM) which uses the statistical alignment information as the prior knowledge to guide bilingual word prediction.
The results show AlignLM can improve the zero-shot performance significantly on MLQA and XNLI datasets.
arXiv Detail & Related papers (2021-06-03T10:18:43Z) - Mixed-Lingual Pre-training for Cross-lingual Summarization [54.4823498438831]
Cross-lingual Summarization aims at producing a summary in the target language for an article in the source language.
We propose a solution based on mixed-lingual pre-training that leverages both cross-lingual tasks like translation and monolingual tasks like masked language models.
Our model achieves an improvement of 2.82 (English to Chinese) and 1.15 (Chinese to English) ROUGE-1 scores over state-of-the-art results.
arXiv Detail & Related papers (2020-10-18T00:21:53Z) - Reusing a Pretrained Language Model on Languages with Limited Corpora
for Unsupervised NMT [129.99918589405675]
We present an effective approach that reuses an LM that is pretrained only on the high-resource language.
The monolingual LM is fine-tuned on both languages and is then used to initialize a UNMT model.
Our approach, RE-LM, outperforms a competitive cross-lingual pretraining model (XLM) in English-Macedonian (En-Mk) and English-Albanian (En-Sq)
arXiv Detail & Related papers (2020-09-16T11:37:10Z) - Multilingual Translation with Extensible Multilingual Pretraining and
Finetuning [77.33262578776291]
Previous work has demonstrated that machine translation systems can be created by finetuning on bitext.
We show that multilingual translation models can be created through multilingual finetuning.
We demonstrate that pretrained models can be extended to incorporate additional languages without loss of performance.
arXiv Detail & Related papers (2020-08-02T05:36:55Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.