Enhancing Low-Resource LLMs Classification with PEFT and Synthetic Data (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) operating in 0-shot or few-shot settings achieve competitive results in Text Classification tasks. |
| Approach: | They propose to make Large Language Models (LLMs) operating in 0-shot or few-shot settings as efficient as 0- shot text classifiers by leveraging a small number of samples. |
| Outcome: | The proposed model is able to perform better on multiple datasets than existing models on 0-shot or few-shot settings. |
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| Challenge: | Low-resource languages, especially those written in rare scripts, remain unsupported by large language models due to lack of training data. |
| Approach: | They evaluate 20 under-represented languages across three state-of-the-art multilingual LLMs and compare their methods to parameter-efficient fine-tuning. |
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LLMs Are Few-Shot In-Context Low-Resource Language Learners (2024.naacl-long)
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| Challenge: | In-context learning (ICL) empowers large language models to perform diverse tasks in underrepresented languages using only short in-contrast information. |
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LLMs Are Zero-Shot Context-Aware Simultaneous Translators (2024.emnlp-main)
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| Challenge: | Existing SiMT systems operate on a sentence level, disregarding the context established by previous sentences or the broader context implied by previous words. |
| Approach: | They show that open-source LLMs perform on par with or better than some state-of-the-art baselines in simultaneous machine translation tasks, zero-shot. |
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Better as Generators Than Classifiers: Leveraging LLMs and Synthetic Data for Low-Resource Multilingual Classification (2026.findings-eacl)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable multilingual capabilities, making them promising tools in both high- and low-resource languages. |
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ZeroDL: Zero-shot Distribution Learning for Text Clustering via Large Language Models (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have shown impressive performance on downstream tasks, but if they cannot be fully described in prompts, they could fail to perform the task. |
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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. |
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Scaling Performance and Low-Resource Annotation with Many-Shot In-Context Learning for Named Entity Recognition (2026.findings-acl)
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| Challenge: | Existing studies on ICL for Named Entity Recognition (NER) have mainly explored few-shot settings, but the potential of scaling to hundreds of demonstrations has not been thoroughly investigated. |
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Just Read the Codebook! Make Use of Quality Codebooks in Zero-Shot Classification of Multilabel Frame Datasets (2025.coling-main)
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| Challenge: | Recent development of Large Language Models has led to more scrutiny of their performance on complex datasets. |
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Zero-Shot Text Classification with Self-Training (2022.emnlp-main)
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| Challenge: | Recent advances in large pretrained language models have increased attention to zero-shot text classification. |
| Approach: | They propose a plug-and-play method to bridge this gap by requiring only class names along with an unlabeled dataset. |
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Generation-driven Contrastive Self-training for Zero-shot Text Classification with Instruction-following LLM (2024.eacl-long)
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| Challenge: | a novel method to train a smaller model with LLMs for zero-shot text classification requires immense computational resources due to their substantial model size. |
| Approach: | They propose a method which leverages the generative power of large language models to train a smaller model. |
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