Comprehensive Evaluation of Zero- and Few-Shot Prompting with Large Language Models for Bangla Sentiment Analysis

Belangrijkste concepten

Large language models can effectively perform sentiment analysis on Bangla text through zero- and few-shot prompting, though fine-tuned models still outperform them.

Samenvatting

This study presents a comprehensive evaluation of zero- and few-shot prompting with large language models (LLMs) for Bangla sentiment analysis. The authors developed a new dataset called MUBASE, which contains 33,606 manually annotated Bangla news tweets and Facebook comments.

The key highlights and insights from the study are:

1. The authors compared the performance of classical models, fine-tuned models, and LLMs (Flan-T5, GPT-4, BLOOMZ) in both zero-shot and few-shot settings.
2. Fine-tuned models, particularly the monolingual BanglaBERT, consistently outperformed the LLMs across various metrics.
3. While the LLMs surpassed the random and majority baselines, they fell short compared to the fine-tuned models.
4. The performance of the smaller BLOOMZ model (560m) was better than the larger one (1.7B), suggesting the need for more training data to effectively train large models.
5. The authors observed little to no performance difference between zero- and few-shot learning with the GPT-4 model, while BLOOMZ yielded better performance in the majority of zero- and few-shot experiments.
6. The authors also explored the impact of different prompting strategies, finding that native language instructions achieved comparable performance to English instructions for Bangla sentiment analysis.
7. The authors conducted an error analysis, revealing that Flan-T5 struggled to predict the negative class, BLOOMZ failed to label posts as neutral, and GPT-4 had difficulty with the positive class.

Overall, the study provides valuable insights into the effectiveness of LLMs for Bangla sentiment analysis and highlights the continued need for fine-tuned models, especially for low-resource languages.

Statistieken

The dataset contains 33,606 Bangla news tweets and Facebook comments.
The dataset is divided into 23,472 training, 3,427 development, and 6,707 test instances.
The class distribution is: 10,560 positive, 6,197 neutral, and 16,849 negative instances.

Citaten

"Fine-tuned models, particularly the monolingual BanglaBERT, consistently outperformed the LLMs across various metrics."
"While the LLMs surpassed the random and majority baselines, they fell short compared to the fine-tuned models."
"The performance of the smaller BLOOMZ model (560m) was better than the larger one (1.7B), suggesting the need for more training data to effectively train large models."

Belangrijkste Inzichten Gedestilleerd Uit

Zero- and Few-Shot Prompting with LLMs

by Md. Arid Has... om arxiv.org 04-08-2024

https://arxiv.org/pdf/2308.10783.pdf

Diepere vragen

How can the performance of LLMs be further improved for Bangla sentiment analysis, especially in the zero- and few-shot settings?

In order to enhance the performance of Large Language Models (LLMs) for Bangla sentiment analysis, particularly in zero- and few-shot settings, several strategies can be implemented:

Fine-tuning with Bangla-specific data: Fine-tuning the LLMs with more Bangla-specific data can help them better understand the nuances of the language and improve their performance in sentiment analysis tasks.

Optimizing prompts: Crafting more effective prompts tailored to the Bangla language can guide the LLMs to generate more accurate sentiment predictions. Experimenting with different prompt structures and languages can help identify the most effective approach.

Ensemble methods: Combining the outputs of multiple LLMs through ensemble methods can potentially improve performance. By leveraging the strengths of different models, an ensemble approach can provide more robust sentiment analysis results.

Further exploration of few-shot learning: Conducting more in-depth research on few-shot learning techniques specific to Bangla sentiment analysis can lead to better utilization of limited training data and improved model performance.

Addressing class imbalances: Given the skew towards negative instances in the dataset, techniques to address class imbalances, such as oversampling or undersampling, can help LLMs better learn from the data and make more balanced sentiment predictions.

Continuous evaluation and iteration: Regularly evaluating the performance of LLMs, analyzing errors, and iteratively refining the models based on feedback can lead to continuous improvement in sentiment analysis accuracy.

How can the potential challenges and limitations of using LLMs for sentiment analysis in low-resource languages like Bangla?

Using Large Language Models (LLMs) for sentiment analysis in low-resource languages like Bangla comes with several challenges and limitations:

Data scarcity: Low-resource languages often lack sufficient labeled data for training LLMs, which can hinder model performance. Limited data availability can lead to overfitting and reduced generalization capabilities.

Language complexity: Low-resource languages may have complex linguistic structures, dialectal variations, and informal expressions that pose challenges for LLMs in accurately capturing sentiment nuances.

Bias and fairness: LLMs trained on inadequate or biased data can perpetuate stereotypes and biases in sentiment analysis. Ensuring fairness and mitigating bias in low-resource language models is crucial for ethical AI applications.

Cross-lingual transferability: LLMs trained on high-resource languages may not transfer well to low-resource languages like Bangla due to linguistic differences, resulting in suboptimal performance in sentiment analysis tasks.

Fine-tuning difficulties: Fine-tuning LLMs for low-resource languages requires expertise and resources. The process can be time-consuming, computationally intensive, and may not always yield significant performance improvements.

Interpretability: LLMs are often criticized for their lack of interpretability, making it challenging to understand how they arrive at sentiment predictions in low-resource languages, which can impact trust and usability.

How can the insights from this study be applied to develop more effective sentiment analysis systems for other low-resource languages?

The insights from this study can be leveraged to enhance sentiment analysis systems for other low-resource languages in the following ways:

Dataset creation: Following the methodology of creating a manually annotated dataset with high-quality annotations can serve as a blueprint for developing similar datasets in other low-resource languages.

Model selection: The comparative analysis of different models, including classical models, small language models, and large language models, can guide the selection of appropriate models for sentiment analysis tasks in other low-resource languages.

Prompt optimization: Experimenting with zero- and few-shot prompting strategies, as explored in the study, can help tailor prompts for specific languages and improve the performance of sentiment analysis systems.

Fine-tuning techniques: Understanding the effectiveness of fine-tuning with monolingual text and the impact of model size can inform the fine-tuning process for sentiment analysis models in other low-resource languages.

Ensemble methods: The use of ensemble methods to combine outputs from different models can enhance the robustness and accuracy of sentiment analysis systems in low-resource languages.

Ethical considerations: Incorporating ethical considerations, such as bias mitigation and fairness, based on the study's insights can ensure the development of more responsible sentiment analysis systems for diverse languages and cultures.