Papers by Seyyed Hadi Hashemi
Adapting Vision-Language Models for E-commerce Understanding at Scale (2026.eacl-industry)
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Matteo Nulli, Vladimir Orshulevich, Tala Bazazo, Christian Herold, Michael Kozielski, Marcin Mazur, Szymon Tuzel, Cees G. M. Snoek, Seyyed Hadi Hashemi, Omar Javed, Yannick Versley, Shahram Khadivi
| 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. |
Unilogit: Robust Machine Unlearning for LLMs Using Uniform-Target Self-Distillation (2025.findings-acl)
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| Challenge: | Extensive experiments on public benchmarks and an in-house e-commerce dataset demonstrate Unilogit’s superior performance in balancing forget and retain objectives, outperforming state-of-the-art methods such as NPO and UnDIAL. |
| Approach: | They propose a self-distillation method that dynamically adjusts target logits to achieve a uniform probability for the target token. |
| Outcome: | Extensive experiments on public benchmarks and an in-house e-commerce dataset demonstrate Unilogit’s superior performance in balancing forget and retain objectives. |
Domain Adaptation of Foundation LLMs for e-Commerce (2025.acl-industry)
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Christian Herold, Michael Kozielski, Tala Bazazo, Pavel Petrushkov, Yannick Versley, Seyyed Hadi Hashemi, Patrycja Cieplicka, Dominika Basaj, Shahram Khadivi
| 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. |
Evaluation of Attribution Bias in Generator-Aware Retrieval-Augmented Large Language Models (2025.findings-acl)
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| Challenge: | Prior work has focused on improving and evaluating the attribution quality of large language models (LLMs) but this may come at the expense of inducing biases in the attributed answers. |
| Approach: | They propose to evaluate attribution sensitivity and bias with respect to authorship information in large language models (LLMs) in retrieval-augmented generation pipelines. |
| Outcome: | The proposed framework can significantly improve the attribution quality of large language models (LLMs) in retrieval-augmented generation pipelines by adding authorship information to source documents. |
ClusComp: A Simple Paradigm for Model Compression and Efficient Finetuning (2025.findings-acl)
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| Challenge: | Weight-only quantization reduces model size but suffers from performance degradation at lower bit widths. |
| Approach: | They propose a weight-only quantization paradigm that clusters weight matrices into codebooks and finetunes them block-by-block. |
| Outcome: | The proposed paradigm outperforms quantization methods and fine tunes LLMs to 1-bit compression and fine tuning. |