In-sample Curriculum Learning by Sequence Completion for Natural Language Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing work on curriculum learning rely on task-specific expertise and cannot generalize to different tasks. |
| Approach: | They propose to do in-sample curriculum learning for natural language generation tasks using human-crafted rules and a numeric score for each sample based on domain expertise to rank the model. |
| Outcome: | The proposed learning strategy generalizes well to different tasks and achieves significant improvements over baselines. |
Similar Papers
Beyond Random Sampling: Efficient Language Model Pretraining via Curriculum Learning (2026.eacl-long)
Copied to clipboard
| Challenge: | Curriculum learning has improved efficiency across machine learning domains, but remains underexplored for language model pretraining. |
| Approach: | They present a systematic investigation of curriculum learning in LLM pretraining . they use vanilla curriculum learning, pacing-based sampling, and interleaved curricula . |
| Outcome: | The proposed framework accelerates convergence in early and mid-training phases, reducing training steps by 18-45% to reach baseline performance. |
Curriculum Learning for Natural Language Understanding (2020.acl-main)
Copied to clipboard
| Challenge: | Pre-trained language models can be fine tuned to perform NLU tasks in a straightforward manner. |
| Approach: | They propose a pretrain-finetune paradigm for natural language understanding (NLU) they propose 'a cross-trainset' approach that allows users to distinguish easy from difficult examples . |
| Outcome: | The proposed approach achieves significant performance improvements on a wide range of NLU tasks. |
Your Pretrained Model Tells the Difficulty Itself: A Self-Adaptive Curriculum Learning Paradigm for Natural Language Understanding (2025.acl-srw)
Copied to clipboard
| Challenge: | Existing curriculum learning approaches rely on manually defined difficulty metrics which may not accurately reflect the model’s own perspective. |
| Approach: | They propose a self-adaptive curriculum learning paradigm that prioritizes fine-tuning examples based on difficulty scores predicted by pre-trained language models (PLMs) they evaluate four datasets covering binary and multi-class classification tasks. |
| Outcome: | The proposed model leads to faster convergence and improved performance compared to standard random sampling. |
Does the Order of Training Samples Matter? Improving Neural Data-to-Text Generation with Curriculum Learning (2021.eacl-main)
Copied to clipboard
| Challenge: | Recent advances in data-to-text generation have been focused on curriculum learning, which is a process of presenting training data in a specific order, starting from easy examples and moving on to more difficult ones, as the learner becomes more competent. |
| Approach: | They propose to use a curriculum learning process to change the order of training samples in a model based on the model's competence to improve model performance and convergence speed. |
| Outcome: | The proposed model shows faster convergence speed and reduced training time by 38.7% and performance by 4.84 BLEU. |
Token-wise Curriculum Learning for Neural Machine Translation (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing curriculum learning approaches to Neural Machine Translation (NMT) require sampling sufficient amounts of “easy” samples from training data at the early stage of training. |
| Approach: | They propose a token-wise curriculum learning approach that creates sufficient amounts of easy samples from training data. |
| Outcome: | The proposed approach outperforms baselines on five language pairs on low-resource languages. |
On Curriculum Learning for Commonsense Reasoning (2022.naacl-main)
Copied to clipboard
| Challenge: | Recent research suggests that data order can have a significant impact on the performance of finetuned models for natural language understanding. |
| Approach: | They use paced curriculum learning to rank data and sample training mini-batches with increasing levels of difficulty during finetuning. |
| Outcome: | The proposed model improves performance for socialIQA, CosmosQA, CODAH, HellaSwag, WinoGrande in both tuning settings. |
Curriculum Learning for Domain Adaptation in Neural Machine Translation (N19-1)
Copied to clipboard
| Challenge: | Neural machine translation (NMT) performance drops when domains do not match and in-domain training data is scarce. |
| Approach: | They propose a curriculum learning approach to adapt generic neural machine translation models to a specific domain. |
| Outcome: | The proposed approach outperforms unadapted and adapted baselines in two domains and two language pairs. |
Controlled Language Generation for Language Learning Items (2022.emnlp-industry)
Copied to clipboard
| Challenge: | Recent advances in pre-trained language models have resulted in success in generating fluent English text. |
| Approach: | They propose to employ natural language generation to rapidly generate English language items . they experiment with deep pretrained models and develop methods for controlling items for factors relevant in language learning . |
| Outcome: | The proposed framework shows high grammatically scores for all models and higher complexity over baseline models. |
HuCurl: Human-induced Curriculum Discovery (2023.acl-long)
Copied to clipboard
| Challenge: | Existing curriculum learning frameworks can be used to discover effective curricula for NLP tasks based on prior knowledge about sample difficulty. |
| Approach: | They propose a framework for curriculum learning based on prior knowledge about sample difficulty. |
| Outcome: | The proposed framework outperforms existing curriculum learning approaches on several NLP tasks and can prune and weight samples for better learning. |
LLM-driven Instruction Following: Progresses and Concerns (2023.emnlp-tutorial)
Copied to clipboard
| Challenge: | a tutorial on task instruction is aimed at researchers and practitioners interested in NLP generalization . labeled examples are unlikely to be available in large numbers or do not exist . |
| Approach: | This tutorial will examine the progress of natural language processing (NLP) using labeled examples. authors propose that task instructions act as a novel resource for supervision. |
| Outcome: | This tutorial aims to answer questions about instruction-driven NLP . it focuses on the use of task instructions in a low-shot scenario . |