BenLLM-Eval: Evaluation of Large Language Models on Bengali NLP

核心概念

Large Language Models (LLMs) performance in Bengali NLP tasks varies, highlighting the need for further research and understanding.

摘要

Introduction

Pre-trained language models have revolutionized NLP.
LLMs like GPT-3.5, LLaMA-2-13b-chat, Claude-2 evaluated in Bengali.

Methodology

Tasks include summarization, QA, paraphrasing, NLI, transliteration, text classification, sentiment analysis.

Results and Discussion

Performance varies across tasks with some LLMs outperforming SOTA models.
Task contamination analysis reveals potential prior exposure to test tasks in some cases.

Conclusions and Future Work

Further evaluation needed for open-source LLMs in low-resource languages like Bengali.

統計資料

"Our experimental results demonstrate that while in some Bengali NLP tasks, zero-shot LLMs could achieve performance on par, or even better than current SOTA fine-tuned models; in most tasks, their performance is quite poor (with the performance of open-source LLMs like LLaMA-2-13b-chat being significantly bad) in comparison to the current SOTA results."
"Despite some exceptional cases, the zero-shot performance of LLMs is generally inferior compared to the SOTA fine-tuned models across the majority of the tasks in our evaluation."
"GPT-3.5 performed exceptionally well on the IndicSentiment dataset (Doddapaneni et al., 2022), attaining a new SOTA accuracy of 90.20%."

引述

從以下內容提煉的關鍵洞見

BenLLMEval

by Mohsinul Kab... 於 arxiv.org 03-20-2024

https://arxiv.org/pdf/2309.13173.pdf

深入探究

How can task contamination be mitigated in evaluating large language models?

Task contamination in evaluating large language models can be mitigated through several strategies:

Careful Dataset Selection: Ensure that the evaluation datasets are distinct from the training data used for pre-training the language model. This helps prevent the model from simply memorizing examples it has seen before.

Prompt Design: Craft unique and diverse prompts for each task to minimize overlap with training data tasks. By providing specific instructions tailored to the evaluation task, you reduce the likelihood of generating responses based on prior exposure.

Task Example Extraction (TEE): Use TEE techniques to identify any potential overlaps between test tasks and training data tasks by extracting instances related to specific evaluation tasks from instruction-tuned models.

Membership Inference: For generative tasks like summarization or paraphrasing, conduct membership inference tests to check if generated outputs match exact examples from the original dataset, indicating direct exposure rather than general learning ability.

Cross-Validation: Implement cross-validation techniques where different subsets of data are used for training and testing to ensure that no leakage occurs between these sets.

By implementing these measures, researchers can enhance the integrity of evaluations and provide a more accurate assessment of a model's true capabilities without being influenced by prior exposure.

What are the implications of poor performance by open-source LLMs like LLaMA-2-13b-chat?

The poor performance exhibited by open-source LLMs like LLaMA-2-13b-chat carries significant implications:

Limited Applicability: Poor performance restricts their usability in real-world applications requiring high accuracy and reliability, especially in critical domains such as healthcare or finance where precision is crucial.

Resource Allocation: Organizations investing resources into deploying such models may face setbacks due to subpar results, leading to wasted time and effort without achieving desired outcomes.

Trust Issues: Consistent underperformance might erode trust in open-source LLMs among users who rely on them for various NLP tasks, potentially hindering adoption rates.

Competitive Disadvantage: Entities leveraging poorly performing models may lag behind competitors using more effective solutions, impacting their overall competitiveness in NLP applications.

Research Direction Shift : Suboptimal results could prompt a shift towards improving existing models or developing new approaches tailored specifically for modest-resourced languages like Bengali.

How can the findings from this study be applied to improve real-world applications using Bengali language models?

The findings from this study offer valuable insights that can enhance real-world applications utilizing Bengali language models:

Model Selection: Based on comparative evaluations conducted in this study, organizations can make informed decisions regarding which type of LLM would best suit their specific application needs - whether it's GPT-3 variants or other specialized fine-tuned SOTA models depending on task requirements.

2 . Training Data Augmentation: Understanding where current LLMs fall short provides guidance on areas needing improvement; hence additional annotated datasets could be created specifically targeting those weaknesses identified during evaluation
3 . Prompt Optimization: Crafting well-designed prompts following successful patterns observed during zero-shot evaluations enhances model understanding while minimizing errors caused by vague instructions
4 . Domain-Specific Fine-Tuning: Leveraging domain-specific fine-tuning post zero-shot evaluations allows tailoring generic pre-trained LLMs towards better performance within particular industries or use cases
5 . Ethical Considerations & Bias Mitigation : Recognizing limitations highlighted ensures ethical deployment practices focusing on fairness & transparency when integrating Bengali language technology into various sectors