Evaluating the Performance of Large Language Models and Transformer Models in Bangla Natural Language Inference

Core Concepts

Large Language Models (LLMs) like GPT-3.5 Turbo and Gemini 1.5 Pro can achieve comparable or superior performance to fine-tuned state-of-the-art (SOTA) Transformer models in few-shot Bangla Natural Language Inference (NLI) tasks, but further research is needed to enhance their understanding in low-resource language contexts.

Abstract

This study provides a comprehensive evaluation of the performance of prominent Large Language Models (LLMs) and state-of-the-art (SOTA) Transformer models in Bangla Natural Language Inference (NLI) tasks.
The key findings are:

LLMs generally demonstrate lower zero-shot performance compared to SOTA fine-tuned models in Bangla NLI, suggesting that existing LLMs may not adequately meet the requirements of low-resource Bengali tasks. The phenomenon of "hallucination" in LLM outputs also poses a challenge.

LLMs exhibit impressive performance in zero-shot scenarios within the English language, but their performance falls short in languages with fewer resources, such as Bengali. This underscores the significance of exploring the constraints faced by LLMs in modest-resource language communities.

The performance of LLMs significantly improves with just a few shots (5, 10, 15) compared to zero-shot scenarios, and this improvement can even surpass the performance of SOTA models. This highlights the vast potential of LLMs and indicates promising avenues for further exploration and enhancement.

The study utilizes the XNLI dataset for Bangla NLI and compares the performance of LLMs like GPT-3.5 Turbo and Gemini 1.5 Pro with SOTA Transformer models such as BanglaBERT, Bangla BERT Base, DistilBERT, mBERT, and sahajBERT. The findings emphasize the importance of continued efforts in exploring LLM capabilities across diverse linguistic contexts.

Stats

"LLMs generally demonstrate lower zero-shot performance compared to SOTA fine-tuned models, especially in the context of Bangla NLI."
"LLMs exhibit impressive performance in zero-shot scenarios within the English language, but their performance falls short in languages with fewer resources, such as Bengali."
"The performance of LLMs significantly improves with just a few shots (5, 10, 15) compared to zero-shot scenarios, and this improvement can even surpass the performance of SOTA models."

Quotes

"Despite their impressive capabilities, LLMs are prone to producing erroneous data, necessitating the use of techniques such as Reinforcement Learning from Human Feedback (RLHF) to assure the development of dependable responses."
"This underscores the significance of delving into the constraints faced by LLMs customized for various modest-resource language communities."
"This underscores the vast potential of LLMs and indicates promising avenues for further exploration and enhancement."

Key Insights Distilled From

Unraveling the Dominance of Large Language Models Over Transformer Models for Bangla Natural Language Inference: A Comprehensive Study

by Fatema Tuj J... at arxiv.org 05-07-2024

https://arxiv.org/pdf/2405.02937.pdf

Deeper Inquiries

How can the performance of LLMs in Bangla NLI tasks be further improved through advanced prompt engineering and fine-tuning techniques?

In order to enhance the performance of Large Language Models (LLMs) in Bangla Natural Language Inference (NLI) tasks, advanced prompt engineering and fine-tuning techniques play a crucial role. Here are some strategies to further improve the performance of LLMs in Bangla NLI tasks:

Optimized Prompt Design: Crafting precise and effective prompts is essential for guiding LLMs in making accurate inferences. Advanced prompt engineering involves providing clear instructions, incorporating both premise and hypothesis for inference, and strategically using keywords to direct the model's focus. By optimizing the prompts to capture the nuances of the Bangla language and the specific requirements of NLI tasks, the model's performance can be significantly improved.

Automatic Prompt Optimization Algorithms: Leveraging automatic prompt optimization algorithms can streamline the process of prompt design and refinement. These algorithms can tailor task-specific and model-specific prompts, taking into account the intricacies of the Bangla language and the complexities of NLI tasks. By automating the prompt optimization process, researchers can save time and effort while ensuring that the prompts are optimized for maximum performance.

Chain-of-Thought (CoT) Prompting: Integrating CoT Prompting into Bangla NLI tasks can offer valuable insights into enhancing reasoning abilities and addressing fidelity and transparency issues in NLI statements. CoT Prompting guides LLMs through multi-step reasoning processes, enabling them to make more informed and accurate decisions. By incorporating CoT Prompting techniques, researchers can improve the model's decision-making process and overall performance in Bangla NLI tasks.

Fine-Tuning Strategies: Fine-tuning LLMs on specific Bangla NLI datasets and tasks can further enhance their performance. By adjusting hyperparameters, such as learning rates, batch sizes, and epochs, researchers can optimize the model's training process and improve its ability to make accurate inferences. Fine-tuning techniques tailored to the nuances of the Bangla language and the requirements of NLI tasks can help LLMs achieve better performance in these specific domains.

By implementing these advanced prompt engineering and fine-tuning techniques, researchers can significantly improve the performance of LLMs in Bangla NLI tasks, enabling more accurate and reliable results in natural language inference applications.

What are the potential biases and limitations in the training data and model architectures that contribute to the lower zero-shot performance of LLMs in low-resource languages like Bengali?

The lower zero-shot performance of Large Language Models (LLMs) in low-resource languages like Bengali can be attributed to several potential biases and limitations in the training data and model architectures. Some of the key factors contributing to this phenomenon include:

Limited Pre-training Data: LLMs rely on large amounts of pre-training data to learn language patterns and make accurate predictions. In low-resource languages like Bengali, the availability of annotated datasets for pre-training is limited, leading to a lack of diverse and representative data. This scarcity of training data can result in biases and gaps in the model's understanding of the language, affecting its zero-shot performance.

Dataset Biases: Training data for LLMs may contain biases that impact the model's performance, especially in zero-shot scenarios. Biases in the training data can lead to skewed representations of certain language patterns or concepts, making it challenging for the model to generalize effectively to unseen data. In low-resource languages, where training datasets are smaller and less diverse, these biases can have a more pronounced effect on the model's performance.

Complex Language Nuances: Languages like Bengali have intricate linguistic nuances and structures that may be challenging for LLMs to capture accurately, especially in zero-shot settings. The model architectures may struggle to understand and interpret these nuances without sufficient exposure to diverse language patterns and contexts. This limitation can hinder the model's ability to make accurate inferences and predictions in zero-shot scenarios.

Model Adaptation: LLMs trained on data from other languages may not adapt well to the specific characteristics of low-resource languages like Bengali. The model architectures and pre-training objectives may not be optimized for capturing the unique linguistic features of Bengali, leading to lower zero-shot performance in NLI tasks. Adapting LLMs to the nuances of Bengali language structures and semantics is crucial for improving their performance in zero-shot scenarios.

By addressing these potential biases and limitations in the training data and model architectures, researchers can enhance the zero-shot performance of LLMs in low-resource languages like Bengali, enabling more accurate and reliable natural language inference capabilities in these linguistic contexts.

How can the reliability and transparency of LLM outputs be enhanced to address the issue of "hallucination" and ensure trustworthy results in Bangla NLI applications?

Enhancing the reliability and transparency of Large Language Models (LLMs) outputs is essential to address the issue of "hallucination" and ensure trustworthy results in Bangla Natural Language Inference (NLI) applications. Here are some strategies to improve the reliability and transparency of LLM outputs:

Explainability Techniques: Implementing explainability techniques can help users understand how LLMs arrive at their predictions and decisions. Techniques such as attention visualization, saliency maps, and feature attribution can provide insights into the model's reasoning process and highlight the key factors influencing its outputs. By making the model's decision-making process more transparent, researchers can enhance the reliability of LLM outputs in Bangla NLI applications.

Bias Detection and Mitigation: Conducting bias detection and mitigation strategies can help identify and address biases in LLM outputs that may lead to hallucinations or inaccurate results. By analyzing the model's predictions for biases related to gender, race, or other sensitive attributes, researchers can mitigate these biases and ensure fair and trustworthy results in NLI tasks. Implementing bias detection algorithms and debiasing techniques can improve the reliability of LLM outputs in Bangla NLI applications.

Human-in-the-Loop Validation: Incorporating human-in-the-loop validation processes can enhance the reliability of LLM outputs by involving human annotators or validators in the decision-making loop. Human annotators can review and verify the model's predictions, flagging any instances of hallucination or inaccuracies. This iterative validation process can help improve the overall quality and trustworthiness of LLM outputs in Bangla NLI applications.

Model Interpretability: Enhancing the interpretability of LLMs can improve the transparency of their outputs and make them more understandable to users. Techniques such as model distillation, model compression, and post-hoc interpretability methods can simplify complex LLM architectures and make their outputs more interpretable. By increasing the transparency of LLM outputs, researchers can address the issue of hallucination and ensure that the model's predictions are reliable and trustworthy in Bangla NLI applications.

By implementing these strategies to enhance the reliability and transparency of LLM outputs, researchers can mitigate the issue of hallucination and improve the overall trustworthiness of LLM predictions in Bangla NLI applications.