Papers with over-fitting
AFLoRA: Adaptive Freezing of Low Rank Adaptation in Parameter Efficient Fine-Tuning of Large Models (2024.acl-short)
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| Challenge: | Pre-trained language models have demonstrated commendable performance on various NLP tasks. |
| Approach: | They propose a Parameter-Efficient Fine-Tuning (PEFT) method that incrementally freezes low-rank matrices during fine-tuning to reduce computation and alleviate over-fitting. |
| Outcome: | The proposed method achieves state-of-the-art performance with an average improvement of 0.85% on the GLUE benchmark while yielding up to 1.86 improvement as opposed to similar PEFT alternatives. |
Rapid Adaptation of Neural Machine Translation to New Languages (D18-1)
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| Challenge: | Existing approaches to adapt neural machine translation systems to low-resource languages are difficult to implement and require large amounts of training data. |
| Approach: | They propose a method to train neural machine translation systems to new low-resource languages . they propose to start with massively multilingual "seed models" and continue training on data related to the LRL . |
| Outcome: | The proposed method achieves BLEU scores of up to 15.5 with no data from the LRL and improves over other adaptation methods by 1.7 BLUE points average over 4 LRL settings. |
Modality Adaption or Regularization? A Case Study on End-to-End Speech Translation (2023.acl-short)
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| Challenge: | End-to-end speech translation models have limited training data and are often inefficient due to the inconsistency of length and representation between speech and text. |
| Approach: | They find that the "modality gap" between speech and text data is not a major problem in E2E ST . they decouple the encoder to speech encoder and text encoder, and they find that there is a 'capacity gap' |
| Outcome: | The proposed model achieves 29.0 for en-de and 40.3 for fr on the MuST-C dataset. |
Nearest Neighbour Few-Shot Learning for Cross-lingual Classification (2021.emnlp-main)
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| Challenge: | Existing pre-trained models can cause over-fitting when limited data are available. |
| Approach: | They propose to use a nearest-neighbor few-shot technique to improve cross-lingual adaptation using 16 distinct languages across two NLP tasks. |
| Outcome: | The proposed approach improves fine-tuning using only a handful of labeled samples in target locales and also generalizes across tasks. |
A Lightweight Mixture-of-Experts Neural Machine Translation Model with Stage-wise Training Strategy (2024.findings-naacl)
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| Challenge: | Using mixture-of-experts (MoE) to deal with language heterogeneity is a challenge in neural machine translation (NMT). |
| Approach: | They propose a lightweight MoE-based NMT model that is trained via an elaborate stage-wise training strategy. |
| Outcome: | The proposed model achieves stable improvements in translation tasks by introducing fewer extra parameters compared to baseline models. |
On the Limitations of Dataset Balancing: The Lost Battle Against Spurious Correlations (2022.findings-naacl)
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| Challenge: | Recent work shows that deep learning models are sensitive to low-level correlations between simple features and specific output labels, leading to over-fitting and lack of generalization. |
| Approach: | They propose to eliminate single-word correlations altogether to mitigate this problem . they highlight several alternatives to dataset balancing to enhance contexts . |
| Outcome: | The proposed approach to balancing datasets is insufficient, the authors argue . they suggest enhancing datasets with richer contexts and abstaining from interaction . |
Online Distilling from Checkpoints for Neural Machine Translation (N19-1)
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| Challenge: | Existing neural machine translation models have a deep structure with large amounts of parameters, making them hard to train. |
| Approach: | They propose an online method to generate a teacher model from checkpoints . they show steady improvement over a strong self-attention-based baseline system . |
| Outcome: | The proposed method improves on-the-fly on several datasets and language pairs. |
MERIt: Meta-Path Guided Contrastive Learning for Logical Reasoning (2022.findings-acl)
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| Challenge: | Existing methods to infer logical relations with annotated training data suffer from over-fitting and poor generalization problems due to the dataset sparsity. |
| Approach: | They propose a MEta-path guided contrastive learning method for logical ReasonIng of text that performs self-supervised pre-training on abundant unlabeled text data. |
| Outcome: | The proposed method outperforms the baselines on two logical reasoning benchmarks with significant improvements. |
TreeMix: Compositional Constituency-based Data Augmentation for Natural Language Understanding (2022.naacl-main)
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| Challenge: | Existing data augmentation methods miss the important characteristic of compositionality, meaning of a complex expression is built from its sub-parts. |
| Approach: | They propose a compositional data augmentation approach for natural language understanding called TreeMix that leverages constituency parsing tree to decompose sentences into constituent sub-structures and the Mixup data enhancing technique to recombine them to generate new sentences. |
| Outcome: | The proposed approach outperforms current state-of-the-art methods on text classification and SCAN. |
Prediction Difference Regularization against Perturbation for Neural Machine Translation (2022.acl-long)
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| Challenge: | Existing methods for regularizing input perturbation are limited by under-fitting of training data. |
| Approach: | They propose a method that can reduce over-fitting and under-fitting at the same time. |
| Outcome: | The proposed method can reduce over-fitting and under-fitturing while making the model less sensitive to small input changes and more robust to under-perturbed training data. |
Two-Stage Fine-Tuning for Improved Bias and Variance for Large Pretrained Language Models (2023.acl-long)
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| Challenge: | Recent work challenges the bias-variance trade-off . large pretrained models can have large variance and overfit domain-specific data . |
| Approach: | They propose a bias-variance trade-off that implies learning methods need to balance complexity with data size to minimize under-fitting and over-fit. |
| Outcome: | The proposed method achieves strong results on SuperGLUE and clinical information extraction tasks. |
Text Representation Distillation via Information Bottleneck Principle (2023.emnlp-main)
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| Challenge: | Pre-trained language models (PLMs) have recently shown great success in text representation field, however, the high computational cost and high-dimensional representation of PLMs pose significant challenges for practical applications. |
| Approach: | They propose a Knowledge Distillation method that distills large models into smaller representation models to reduce performance degradation after distillation. |
| Outcome: | Empirical results on two main downstream applications of the proposed method show that it reduces the risk of over-fitting and maximizes the mutual information between the model and the input data. |
Fixing MoE Over-Fitting on Low-Resource Languages in Multilingual Machine Translation (2023.findings-acl)
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| Challenge: | Sparsely gated Mixture of Experts (MoE) models are a compute-efficient method to scale model capacity for multilingual machine translation tasks. |
| Approach: | They propose a regularization strategy that prevents over-fitting of MoE models on low-resource tasks and conditional MoE Routing and curriculum learning methods that prevent over- fitting. |
| Outcome: | The proposed methods improve the performance of MoE models on low-resource tasks without adversely affecting high-res tasks. |