| Challenge: | Pretrained language models demonstrate strong performance in most NLP tasks when fine-tuned on small task-specific datasets. |
| Approach: | They propose a two-stage procedure to learn from a small set of demonstrations and a simple reinforcement learning algorithm to improve by interacting with an environment. |
| Outcome: | The proposed method improves with only 1.2% of the demonstrations and a simple reinforcement learning algorithm over existing methods in the ALFWorld environment. |
Similar Papers
Making Pre-trained Language Models Better Few-shot Learners (2021.acl-long)
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| Challenge: | Recent studies show that the GPT-3 model can perform few-shots on language understanding tasks with a natural-language prompt and a few task demonstrations. |
| Approach: | They propose a technique for fine-tuning language models using a few examples . they propose LM-BFF, which uses prompt-based fine-uning and a pipeline for automating prompt generation . |
| Outcome: | The proposed approach outperforms standard fine-tuning procedures on a range of NLP tasks. |
Language Models for Text Classification: Is In-Context Learning Enough? (2024.lrec-main)
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| Challenge: | Existing research on text classification models with prompts is limited in scale and lacks understanding of how these methods compare to more established methods. |
| Approach: | They compare the performance of large and smaller language models with prompts to achieve state-of-the-art performance in many NLP tasks. |
| Outcome: | The proposed models outperform the more standard approaches in binary, multiclass, and multilabel tasks in a large scale evaluation of 16 text classification datasets. |
It’s Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners (2021.naacl-main)
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| Challenge: | Pretraining ever-larger language models on massive corpora requires enormous amounts of compute. |
| Approach: | They propose to convert textual inputs into cloze questions that contain a task description . they also exploit unlabeled data to improve their performance . |
| Outcome: | The proposed model outperforms GPT-3 with PET/iPET with cloze questions and unlabeled data. |
Recent Advances in Pre-trained Language Models: Why Do They Work and How Do They Work (2022.aacl-tutorials)
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| Challenge: | Pre-trained language models are language models that are pre-taught on large-scaled corpora in a self-supervised fashion. |
| Approach: | This tutorial provides a broad and comprehensive introduction to pre-trained language models . it focuses on emerging methods that enable PLMs to perform diverse downstream tasks . |
| Outcome: | This tutorial focuses on the benefits of pre-trained language models and how to use them in NLP tasks. |
On the Importance of Effectively Adapting Pretrained Language Models for Active Learning (2022.acl-short)
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| Challenge: | Recent active learning approaches in NLP use off-the-shelf pretrained language models (LMs) . a poor training strategy can be catastrophic for AL, authors argue . |
| Approach: | They propose to first adapt the pretrained LM to the target task and then use it for AL. |
| Outcome: | The proposed approach provides substantial data efficiency improvements compared to the standard fine-tuning approach. |
Mini But Mighty: Efficient Multilingual Pretraining with Linguistically-Informed Data Selection (2023.findings-eacl)
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| Challenge: | AfriBERTa shows that training transformer models from scratch on 1GB of data from many unrelated African languages outperforms massively multilingual models on downstream NLP tasks. |
| Approach: | They propose that training on smaller amounts of data but from related languages could match the performance of models trained on large, unrelated data. |
| Outcome: | The proposed model outperforms models trained on large, unrelated datasets on downstream NLP tasks. |
Noise-Robust Fine-Tuning of Pretrained Language Models via External Guidance (2023.findings-emnlp)
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| Challenge: | Pretrained Language Models (PLMs) are advanced but data labels are noisy due to the complex annotation process. |
| Approach: | They propose a framework for fine-tuning PLMs using noisy labels that incorporates guidance from Large Language Models like ChatGPT. |
| Outcome: | Experiments on synthetic and real-world noisy datasets show that the proposed framework outperforms the state-of-the-art framework. |
From Word to World: Can Large Language Models be Implicit Text-based World Models? (2026.acl-long)
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Yixia Li, Hongru Wang, Jiahao Qiu, Zhenfei Yin, Dongdong Zhang, Cheng Qian, Zeping Li, Xiaoteng Ma, Guanhua Chen, Heng Ji
| Challenge: | Agentic learning increasingly hinges on interaction, yet real-world experience is expensive, limited, and often irreversible at inference time. |
| Approach: | They propose a framework that reframes language modeling as next-state prediction under interaction. |
| Outcome: | The proposed framework evaluates world models in text-based environments . it shows that sufficiently trained models capture coherent environment dynamics . |
Small Language Models Are Good Too: An Empirical Study of Zero-Shot Classification (2024.lrec-main)
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| Challenge: | Using small language models, we challenge the dominance of large models in text classification by prompting. |
| Approach: | They compare the performance of small and large language models in a zero-shot context using different architectures and scoring functions. |
| Outcome: | The proposed model outperforms large models in a zero-shot context. |
Tending Towards Stability: Convergence Challenges in Small Language Models (2024.findings-emnlp)
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| Challenge: | Increasing the number of parameters in language models is a common strategy to enhance performance, but smaller models often underperform compared to their larger counterparts due to their reduced representational capacity. |
| Approach: | They use the Pythia model suite to analyse the training dynamics that underlie this phenomenon. |
| Outcome: | The proposed model suite enables us to examine the training dynamics of small models. |