Papers by Tala Bazazo

2 papers
Adapting Vision-Language Models for E-commerce Understanding at Scale (2026.eacl-industry)

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Challenge: Existing approaches to adapt VLMs to attribute-centric, multi-image, and noisy data are limited.
Approach: They propose a novel evaluation suite that incorporates deep product understanding, strict instruction following, and dynamic attribute extraction.
Outcome: The proposed model improves e-commerce performance while preserving broad multimodal capabilities.
Domain Adaptation of Foundation LLMs for e-Commerce (2025.acl-industry)

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Challenge: Large Language Models (LLMs) have greatly improved the performance on most natural language tasks, and often show surprisingly good zero-shot generalization to new domains.
Approach: They propose to continuously pretrain the Llama 3.1 base models on 1 trillion tokens of e-commerce data to introduce domain specific knowledge into the model while at the same time keeping the general capabilities intact.
Outcome: The proposed model can be adapted to the new domain without sacrificing performance on general domain tasks.

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