IntentionQA: A Benchmark for Evaluating Purchase Intention Comprehension Abilities of Language Models in E-commerce (2024.findings-emnlp)
Copied to clipboard
Wenxuan Ding, Weiqi Wang, Sze Kwok, Minghao Liu, Tianqing Fang, Jiaxin Bai, Xin Liu, Changlong Yu, Zheng Li, Chen Luo, Qingyu Yin, Bing Yin, Junxian He, Yangqiu Song
| Challenge: | Existing approaches that distill intentions from LMs fail to generate meaningful and human-centric intentions applicable in real-world E-commerce contexts. |
| Approach: | They propose a double-task multiple-choice question answering benchmark to evaluate LMs' comprehension of purchase intentions in E-commerce. |
| Outcome: | The proposed benchmark consists of 4,360 carefully curated problems across three difficulty levels, constructed using an automated pipeline to ensure scalability on large E-commerce platforms. |
Similar Papers
Find the Intention of Instruction: Comprehensive Evaluation of Instruction Understanding for Large Language Models (2025.findings-naacl)
Copied to clipboard
| Challenge: | LLMs are prone to generate responses to instruction-formatted statements in an instinctive manner, rather than comprehending the underlying user intention within the given instructions. |
| Approach: | They propose to use an instruction-following capability benchmark to evaluate LLMs' instruction understanding capability. |
| Outcome: | The proposed benchmark analyzes the instruction understanding capability of large language models with four instruction candidates and a single candidate. |
A User-Centric Multi-Intent Benchmark for Evaluating Large Language Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing benchmarks focus on specific predefined model abilities, such as world knowledge, reasoning, etc., making it difficult for users to determine which LLM best suits their particular needs. |
| Approach: | They propose to evaluate large language models from a user-centric perspective and use real-world use cases to identify their effectiveness under distinct intents. |
| Outcome: | The proposed benchmarks achieve a correlation between human preference and the user-reported scenarios and human intents. |
DispatchQA: A Benchmark for Small Function Calling Language Models in E-Commerce Applications (2025.emnlp-industry)
Copied to clipboard
| Challenge: | DispatchQA is a benchmark to evaluate how well small language models (SLMs) translate openended search queries into executable API calls via explicit function calling. |
| Approach: | They propose a benchmark to evaluate how well small language models translate openended search queries into executable API calls via explicit function calling. |
| Outcome: | The proposed benchmark aims to evaluate how well small language models (SLMs) translate openended search queries into executable API calls via explicit function calling. |
MIND: Multimodal Shopping Intention Distillation from Large Vision-language Models for E-commerce Purchase Understanding (2024.emnlp-main)
Copied to clipboard
Baixuan Xu, Weiqi Wang, Haochen Shi, Wenxuan Ding, Huihao Jing, Tianqing Fang, Jiaxin Bai, Xin Liu, Changlong Yu, Zheng Li, Chen Luo, Qingyu Yin, Bing Yin, Long Chen, Yangqiu Song
| Challenge: | Existing methods for acquiring large-scale intentions generate product-centric intentions without product images and incur high costs for scalability. |
| Approach: | They propose a multimodal framework that allows Large Vision-Language Models to infer purchase intentions from multimodal product metadata and prioritize human-centric ones. |
| Outcome: | The proposed framework shows that it is robust to different prompts and superior to previous methods. |
Adapting Vision-Language Models for E-commerce Understanding at Scale (2026.eacl-industry)
Copied to clipboard
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. |
EcomScriptBench: A Multi-task Benchmark for E-commerce Script Planning via Step-wise Intention-Driven Product Association (2025.acl-long)
Copied to clipboard
Weiqi Wang, Limeng Cui, Xin Liu, Sreyashi Nag, Wenju Xu, Chen Luo, Sheikh Muhammad Sarwar, Yang Li, Hansu Gu, Hui Liu, Changlong Yu, Jiaxin Bai, Yifan Gao, Haiyang Zhang, Qi He, Shuiwang Ji, Yangqiu Song
| Challenge: | Goal-oriented script planning is used by humans to plan for typical activities . however, this capability remains underexplored due to several challenges . |
| Approach: | They propose a framework that enables product-enriched scripts by associating products with each step based on the semantic similarity between the actions and their purchase intentions. |
| Outcome: | The proposed framework can generate product-enriched scripts from 2.4 million scripts . human annotations are conducted to provide gold labels for a sampled subset . |
FolkScope: Intention Knowledge Graph Construction for E-commerce Commonsense Discovery (2023.findings-acl)
Copied to clipboard
Changlong Yu, Weiqi Wang, Xin Liu, Jiaxin Bai, Yangqiu Song, Zheng Li, Yifan Gao, Tianyu Cao, Bing Yin
| Challenge: | Existing intention-based studies on recommendation tasks are limited and use models to implicitly model the intention memberships. |
| Approach: | They propose a framework that leverages the generation power of large language models and human-in-the-loop annotation to semi-automatically construct the intention knowledge graph. |
| Outcome: | The proposed framework can model e-commerce knowledge and have many potential applications. |
A Usage-centric Take on Intent Understanding in E-Commerce (2024.emnlp-main)
Copied to clipboard
| Challenge: | Identifying and understanding user intents is a crucial task for E-Commerce. |
| Approach: | They propose to use intent understanding as a natural language reasoning task independent of product ontologies to identify and understand user intents. |
| Outcome: | The proposed framework can't be used to strongly align user intents with products with desirable properties and recommend useful products across diverse categories. |
Revisiting a Pain in the Neck: A Semantic Reasoning Benchmark for Language Models (2026.acl-long)
Copied to clipboard
| Challenge: | Semantic phrases (SP) are lexical combinations whose meanings or usages may not be fully derived from their individual components. |
| Approach: | They propose to consolidate existing multiword expression resources into a unified testbed to assess language models in semantic phrase processing tasks. |
| Outcome: | The evaluation suite covers idiomatic expressions, noun compounds, and verbal constructions. |
SessionIntentBench: A Multi-task Inter-session Intention-shift Modeling Benchmark for E-commerce Customer Behavior Understanding (2026.findings-acl)
Copied to clipboard
Yuqi Yang, Weiqi Wang, Baixuan Xu, Wei Fan, Qing Zong, Chunkit Chan, Zheye Deng, Xin Liu, Yifan Gao, Changlong Yu, Chen Luo, Yang Li, Zheng Li, Qingyu Yin, Bing Yin, Yangqiu Song
| Challenge: | Existing models fail to capture and model customer intention effectively because of insufficient information exploitation and only apparent information like descriptions and titles are used. |
| Approach: | They propose to exploit existing session data to capture and model intention in E-commerce product purchase sessions using a multimodal benchmark. |
| Outcome: | The proposed framework can bridge the gap between intention understanding in simplified research cases like co-buy intention and more complex yet practical scenarios like session history. |