LLaMA-E: Empowering E-commerce Authoring with Object-Interleaved Instruction Following

Challenge:	E-commerce authoring requires engaging, diverse, and targeted content . Large language models lack memorization of domain-specific features in e-commerce applications .
Approach:	They propose a unified e-commerce authoring models that address contextual preferences of customers, sellers, and platforms . they propose to integrate interleaved features presented by participating objects into the models to empower authoring applications with comprehensive scenario understanding .
Outcome:	The proposed models achieve state-of-the-art evaluation performance and exhibit the advantage in zero-shot practical applications.

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Challenge

Approach

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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

Language Models

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Llama2Vec: Unsupervised Adaptation of Large Language Models for Dense Retrieval (2024.acl-long)

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Chaofan Li, Zheng Liu, Shitao Xiao, Yingxia Shao, Defu Lian

Semantic Text Processing

Challenge:	Dense retrieval requires discriminative embeddings to represent the semantic relationship between query and document.
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I-SEE: An Instruction-tuned, SOP-Enhanced Quality Evaluator for Product Content (2025.emnlp-industry)

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Aniket Joshi, Cyrus Andre DSouza, Sejal Jain, Jitenkumar Babubhai Rana, Promod Yenigalla

Language Models

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Injecting Domain-Specific Knowledge into Large Language Models: A Comprehensive Survey (2025.findings-emnlp)

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Zirui Song, Bin Yan, Yuhan Liu, Miao Fang, Mingzhe Li, Rui Yan, Xiuying Chen

Language Models

Challenge:	specialized LLMs are often limited in domain-specific applications that require specialized knowledge.
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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

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Learning from LLM Agents: In-Context Generative Models for Text Casing in E-Commerce Ads (2025.emnlp-industry)

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Yingxue Zhou, Tan Zhu, Tao Zeng, Zigeng Wang, Wei Shen

Reasoning

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InstructPTS: Instruction-Tuning LLMs for Product Title Summarization (2023.emnlp-industry)

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Besnik Fetahu, Zhiyu Chen, Oleg Rokhlenko, Shervin Malmasi

Summarization

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Cost-Effective E-Commerce Catalog Translation at Scale Ensuring Named Entity Protection (2025.emnlp-industry)

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Asier Gutiérrez-Fandiño, Jorge Yero Salazar, Clement Ruin, Alejandro Quintero-Roba, Shangeetha Ravichandran, Jesus Perez-Martin, Pankaj Adsul, Suruchi Garg, Leonardo Lezcano

Multilinguality

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TrendPulse: A Simple yet Efficient Framework for Capturing Viral E-Commerce Spikes via LLM-Driven Contextualization (2026.acl-industry)

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Arin Jain, Devashish Gupta, Bhavuk Singhal, Divay Jindal, Vinit Rongata, Ravindra Kumar Yadav

Green & Sustainable NLP

Challenge:	Modern e-commerce platforms mostly depend on reactive discovery, where products surface only after users search for them.
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FABRIC: Fully-Automated Broad Intent Categorization in E-commerce (2025.emnlp-industry)

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Anna Tigunova, Philipp Schmidt, Damla Ezgi Akcora

Information Retrieval

Challenge:	Existing query classification models have excellent predictive performance on single-intent queries, but there is little research on predicting multiple-intentions for broad queries.
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