Context Minimization for Resource-Constrained Text Classification: Optimizing Performance-Efficiency Trade-offs through Linguistic Features (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Pretrained language models have transformed text classification, but their computational demands often render them impractical for resource-constrained settings. |
| Approach: | They propose a linguistically-grounded framework for context minimization that leverages theme-rheme structure to preserve critical classification signals while reducing input complexity. |
| Outcome: | The proposed framework preserves critical classification signals while reducing input complexity. |
Similar Papers
A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios (2021.naacl-main)
Copied to clipboard
| Challenge: | a growing body of work is focused on improving performance in low-resource settings . a goal of this study is to explain how these methods differ in their requirements . |
| Approach: | They propose to analyze data-lean scenarios across different dimensions of data availability to understand which approaches are effective in a specific low-resource setting. |
| Outcome: | The proposed methods enable learning when training data is sparse. |
Pretraining Context Compressor for Large Language Models with Embedding-Based Memory (2025.acl-long)
Copied to clipboard
| Challenge: | Efficient processing of long contexts in large language models is essential for real-world applications such as retrieval-augmented generation and in-context learning. |
| Approach: | They propose a decoupled compressor-LLM framework that preserves contextual information within condensed embedding representations. |
| Outcome: | The proposed framework outperforms baseline models in three domains and across eight datasets while adapting to different downstream LLMs. |
1+1>2: A Synergistic Sparse and Low-Rank Compression Method for Large Language Models (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Low-rank approximation compresses the model by retaining its essential structure with minimal information loss. |
| Approach: | They propose a method that leverages the strengths of pruning and low-rank approximation for LLMs. |
| Outcome: | The proposed methods surpass the existing methods on LLaMA and Qwen2.5 models. |
Complicate Then Simplify: A Novel Way to Explore Pre-trained Models for Text Classification (2022.coling-1)
Copied to clipboard
| Challenge: | Existing frameworks for text classification employing pre-trained models are constrained by the difficulty of the task. |
| Approach: | They propose a framework which implements a two-stage training strategy to fully exploit the knowledge in pre-trained models. |
| Outcome: | The proposed framework outperforms state-of-the-art classification models on six text classification corpora. |
Contextualized Weak Supervision for Text Classification (2020.acl-main)
Copied to clipboard
| Challenge: | Existing methods for weakly supervised text classification generate pseudo-labels in a context-free manner, thus, the ambiguous, context-dependent nature of human language has been long overlooked. |
| Approach: | They propose a framework that provides contextualized weak supervision for text classification . they leverage contextualized representations of word occurrences and seed word information . |
| Outcome: | The proposed framework provides contextualized weak supervision for text classification . it leverages representations of word occurrences and seed word information to differentiate interpretations . the proposed framework also disambiguates initial seed words, making it fully contextualized . |
Memory efficiency and resource-rational encoding in sentence processing (2026.acl-long)
Copied to clipboard
| Challenge: | Existing studies have shown that language models need to be constrained in their use of working memory for context, the analogue to human working memory (WM). |
| Approach: | They propose to inject noise into hidden representations of Transformer-based LMs to capture constraint on memory encoding. |
| Outcome: | The proposed model improves alignment with human reading times and makes them more compressed and categorical. |
Towards Realistic Low-resource Relation Extraction: A Benchmark with Empirical Baseline Study (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to extract relational facts from text are limited in their ability to learn from limited labeled data. |
| Approach: | They propose to use prompt-based methods with few-shot labeled data to evaluate performance . data augmentation technologies and self-training are also proposed to generate more labeles in-domain data. |
| Outcome: | The proposed methods perform well in low-resource settings with 8 relation extraction datasets. |
GrEmLIn: A Repository of Green Baseline Embeddings for 87 Low-Resource Languages Injected with Multilingual Graph Knowledge (2025.findings-naacl)
Copied to clipboard
| Challenge: | Contextualized word embeddings are available for many languages, but their coverage is limited for low resourced languages. |
| Approach: | They propose a method that integrates multilingual graph knowledge into the embeddings to make them green. |
| Outcome: | The proposed method outperforms state-of-the-art embeddings on lexical similarity task while being parameter-free at inference time. |
YuLan-Mini: Pushing the Limits of Open Data-efficient Language Model (2025.acl-long)
Copied to clipboard
Hu Yiwen, Huatong Song, Jie Chen, Jia Deng, Jiapeng Wang, Kun Zhou, Yutao Zhu, Jinhao Jiang, Zican Dong, Yang Lu, Xu Miao, Xin Zhao, Ji-Rong Wen
| Challenge: | prevailing pre-training approaches for large language models involve several complexities. |
| Approach: | They propose a low-cost training recipe and a robust optimization approach to mitigate training instability . they also propose synthesis, curriculum, and data selection pipelines to integrate data . |
| Outcome: | The proposed model achieves top-tier performance among models with similar parameter scale . it is comparable to industry-leading models that require significantly more data . |
Probing Structured Pruning on Multilingual Pre-trained Models: Settings, Algorithms, and Efficiency (2022.acl-long)
Copied to clipboard
| Challenge: | Structured pruning has been extensively studied on monolingual pre-trained models . but little attention has been paid to evaluating the effectiveness of structured pruning on multilingual models. |
| Approach: | They investigate settings, algorithms, and efficiency of structured pruning on multilingual models . authors propose a simple approach that allows training the model once and adapting to different model sizes at inference . |
| Outcome: | The proposed approach allows training the model once and adapting to different model sizes at inference. |