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Enhancing Amharic Language Models with Task-Specific and Generative Datasets


Core Concepts
Enhancing the LLAMA-2-Amharic model through the integration of task-specific and generative datasets to improve language model performance.
Abstract
Abstract: Large language models (LLMs) excel in understanding and generating human languages. Low-resource languages like Amharic face challenges due to limited resources. This work focuses on enhancing the LLAMA-2-Amharic model by integrating task-specific and generative datasets. Introduction: LLMs like GPT series demonstrate exceptional linguistic comprehension and text generation abilities. LLAMA-2 pre-training supports a limited number of languages, excluding low-resource languages like Amharic. The study aims to enhance the LLAMA-2-Amharic model by integrating specific datasets for improved performance. Related Work: Open-source LLMs enable specialized language models for various applications. Techniques like LoRA and QLoRA offer efficient methods for training large language models. Dataset Preparation: Creation of instruction-based datasets from existing NLP task-specific datasets. Development of new custom datasets for generation tasks in Amharic. Experiments: Evaluation of existing and fine-tuned models using different datasets. Exploration of prompts' impact on model performance in sentiment analysis and news classification tasks. Results: Improvement in classification scores for sentiment analysis, news classification, and question answering tasks. Enhanced generation abilities observed in text summarization, expansion, story generation, poetry, lyrics generation, etc. Conclusion: Creation of an Amharic instruction fine-tuning dataset to enhance model performance. Limitations: Challenges observed in spell correction tasks due to limitations in pre-processing techniques.
Stats
"Amharic is one of the Semitic languages under the Afroasiatic language family spoken in Ethiopia with more than 57M speakers." "The result shows a significant enhancement of the model’s ability to comprehend and execute instructions." "We used machine-translated datasets for supervised fine-tuning (SFT)." "To save GPT credits, we did our testing only on the first 1,300 items of this data."
Quotes
"We open-source our dataset creation pipeline, instruction datasets, trained models, and evaluation outputs." "Our experiments include evaluating existing models like LLAMA-2-Amharic model and GPT-4 on our task dataset."

Key Insights Distilled From

by Israel Abebe... at arxiv.org 03-21-2024

https://arxiv.org/pdf/2402.08015.pdf
Walia-LLM

Deeper Inquiries

How can the integration of task-specific and generative datasets benefit other low-resource languages?

Integrating task-specific and generative datasets can benefit other low-resource languages by improving the performance of language models in understanding and generating text. Task-specific datasets provide focused training data for specific NLP tasks, enhancing the model's capabilities in those areas. On the other hand, generative datasets help in expanding the model's creativity and ability to generate diverse content. By combining these two types of datasets, language models can be fine-tuned to perform better across a range of tasks while also being able to generate more varied and contextually relevant outputs.

What are potential implications or biases introduced by using machine-translated instruction datasets?

Using machine-translated instruction datasets may introduce several implications and biases that need to be considered: Translation Accuracy: Machine translation may not always capture the nuances or cultural context accurately, leading to errors in instructions provided to the model. Cultural Bias: Translations done by machines might carry inherent biases present in their training data, potentially perpetuating stereotypes or misrepresentations. Loss of Context: Machine translations may lose subtleties or idiomatic expressions unique to a language, impacting how well a model understands and generates text based on these instructions. Quality Variability: The quality of machine translations can vary depending on factors like language complexity, domain specificity, or linguistic structures.

How might cultural nuances impact the effectiveness of language-specific studies on these models?

Cultural nuances play a significant role in shaping language use and understanding; hence they can have several impacts on language-specific studies with large language models: Language Appropriateness: Cultural nuances influence what is considered appropriate or polite speech within a community, affecting how well a model trained solely on linguistic patterns performs when generating text sensitive to cultural norms. Contextual Understanding: Cultural references embedded within languages require an understanding beyond literal translation; failure to grasp these references could lead to inaccuracies or misunderstandings in generated content. Bias Amplification: If not accounted for during training data collection or fine-tuning processes, cultural biases present in texts used for instruction could be amplified by large language models when generating output related to those cultures. Generalization Challenges: Models trained without considering cultural nuances may struggle with generalizing across diverse contexts where different cultures interact linguistically. These considerations highlight why it is crucial for researchers working with large language models on low-resource languages to carefully navigate cultural sensitivities throughout their studies for more effective outcomes.
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