**Fine-Tuning a Low-Resource LLM for Multimodal Translation:

Fine-Tuning a Low-Resource LLM for Multimodal Translation: A Novel Challenge

The rapid development of Large Language Models (LLMs) has led to a proliferation of high-performance applications. However, the majority of these models have been fine-tuned on massive datasets in the English language, leaving a significant gap in developing models that can effectively navigate the complexities of low-resource languages and modalities.

The Challenge:

We propose a unique fine-tuning challenge that tests the capabilities of LLMs in translating multimodal content (images and text) from one language to another, particularly in low-resource languages such as Amharic, Sinhala, or Burmese. Specifically, we aim to:

1. Take an image as input, and generate a translated text in a target language (e.g., English to Amharic).
2. Provide a captioned image in a source language, and translate the image and its caption into the target language.
3. Take a text description and generate a translated image that illustrates the description, leveraging the capability of the model to understand the semantic relationships between text and images.

Constraints:

1. Dataset constraints: You will have access to a limited dataset of 1,000 training images, with corresponding annotations in two low-resource languages (e.g., Amharic and Sinhala).
2. Size constraints: The fine-tuned model should be deployable on consumer-grade GPUs or TPUs, with a maximum size of 1.5 GB.
3. Evaluation metrics: The model will be evaluated on both translation accuracy (BLEU score) and multimodal coherence (i.e., the ability of the model to generate coherent and relevant translations for both text and images).
4. Submission format: Teams are expected to submit their fine-tuned models, along with a brief report detailing the methodology, architecture used, and any notable insights or observations during the experimentation process.

Judging criteria:

1. Translation accuracy (30%): Model should demonstrate strong performance on both text and image translation tasks.
2. Multimodal coherence (20%): The model should be able to generate coherent and relevant translations for both text and images.
3. Efficiency and deployability (20%): The fine-tuned model should be size-efficient and deployable on consumer-grade hardware.
4. Methodology and insights (30%): Teams should provide a clear and well-documented methodology, including any notable observations during experimentation.

Timeline:

• February 14, 2026: Challenge announcement
• March 11, 2026: Submission deadline
• March 20, 2026: Evaluation and shortlisting
• March 31, 2026: Announcement of winners

Prizes:

• A $5,000 cash prize
• A special feature in a top-tier AI publication
• Opportunities for collaboration with leading AI research institutions

We invite researchers, engineers, and enthusiasts to participate in this exciting challenge. Join us in pushing the boundaries of multimodal LLMs and demonstrating their potential in real-world applications.

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