Comparing Specialised Small and General Large Language Models on Text Classification: 100 Labelled Samples to Achieve Break-Even Performance (2025.emnlp-main)
Copied to clipboard
| Challenge: | specialised small models outperform general large models with few labelled samples (on average 100) performance variance is taken into account when comparing the number of labelled sample required to tune a specialised model with a larger number of samples. |
| Approach: | They find that specialised small models need only few labelled samples to outperform general large models with limited labelled data. |
| Outcome: | The proposed models outperform general large models with few labelled samples and take performance variance into account. |
Similar Papers
Language Models for Text Classification: Is In-Context Learning Enough? (2024.lrec-main)
Copied to clipboard
| 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. |
Unveiling the Generalization Power of Fine-Tuned Large Language Models (2024.naacl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have demonstrated exceptional multitasking abilities, but the comprehensive effects of fine-tuning on the LLMs’ generalization ability are not fully understood. |
| Approach: | They conduct extensive experiments across five distinct language tasks on different datasets to investigate whether fine-tuning affects the generalization ability intrinsic to LLMs. |
| Outcome: | The proposed model can generalize to different domains and tasks by integrating the in-context learning strategy during fine-tuning on generation tasks. |
On the Importance of Data Size in Probing Fine-tuned Models (2022.findings-acl)
Copied to clipboard
| Challenge: | Several studies have investigated the reasons behind the effectiveness of fine-tuning, usually through the lens of probing. |
| Approach: | They propose to investigate the reasons behind the effectiveness of fine-tuning by examining the impact of data size on the extent of encoded linguistic knowledge. |
| Outcome: | The proposed probes show that the size of the training data affects the recoverability of the changes made to the model’s linguistic knowledge. |
Revisiting Sample Size Determination in Natural Language Understanding (2023.findings-acl)
Copied to clipboard
Ernie Chang, Muhammad Hassan Rashid, Pin-Jie Lin, Changsheng Zhao, Vera Demberg, Yangyang Shi, Vikas Chandra
| Challenge: | Recent work has sought to reduce the annotation costs through the use of active learning and data sampling. |
| Approach: | They propose to estimate the training sample size needed to achieve a targeted model performance based on small amount of training samples. |
| Outcome: | The proposed approach predicts model performance within a small margin of mean absolute error (0.9%) with only 10% data. |
Small Language Models Are Good Too: An Empirical Study of Zero-Shot Classification (2024.lrec-main)
Copied to clipboard
| 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. |
MELABenchv1: Benchmarking Large Language Models against Smaller Fine-Tuned Models for Low-Resource Maltese NLP (2025.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have shown remarkable performance across various NLP tasks, largely due to their generalisability and ability to perform tasks without additional training. |
| Approach: | They evaluate the performance of 55 publicly available Large Language Models on Maltese, a low-resource language, using a newly introduced benchmark covering 11 discriminative and generative tasks. |
| Outcome: | The proposed models perform poorly on discriminative and generative tasks and smaller fine-tuned models perform better across all tasks. |
Understanding the effects of language-specific class imbalance in multilingual fine-tuning (2024.findings-eacl)
Copied to clipboard
| Challenge: | Existing methods to fine-tune large language models have been developed to reduce the amount of resources needed to perform classification tasks. |
| Approach: | They modify traditional class weighing approach to reduce imbalance by calculating class weights separately for each language. |
| Outcome: | The proposed model improves performance and reduces the promotion of uninformative features. |
Methods for Estimating and Improving Robustness of Language Models (2022.naacl-srw)
Copied to clipboard
| Challenge: | Large language models suffer from weak generalisation ability due to shallow textual relations over full semantic complexity of the problem. |
| Approach: | They propose to incorporate some of these measures into training objectives to enhance distributional robustness of LLMs. |
| Outcome: | The proposed models outperform human models on complex tasks and outperformed other models on deep networks. |
Smaller Language Models are capable of selecting Instruction-Tuning Training Data for Larger Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | Instruction tuning language models can be expensive and expensive to train . current methods require extensive training on large datasets, resulting in high training costs. |
| Approach: | They propose a novel approach to selecting training data based on the learning percentage of the samples. |
| Outcome: | The proposed model performs better on models ranging from 1B to 13B in size compared to training on the entire dataset. |
Specialist or Generalist? Instruction Tuning for Specific NLP Tasks (2023.emnlp-main)
Copied to clipboard
| Challenge: | Recent studies have shown that instruction tuning can be a data-efficient method for transforming large language models into generalist models, but their performance lags behind specialist models trained exclusively for specific tasks. |
| Approach: | They propose to incorporate broadcoverage generalist instruction tuning into large language models to build a specialist model by incorporating task specificity and skill requirements. |
| Outcome: | The proposed method improves model performance when task coverage is broad and when training data is limited. |