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 .

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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.
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ECom-Bench: Can LLM Agent Resolve Real-World E-commerce Customer Support Issues? (2025.emnlp-industry)

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Challenge: ECom-Bench is a benchmark framework for evaluating LLM agent with multimodal capabilities in e-commerce customer support domain.
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PlanningArena: A Modular Benchmark for Multidimensional Evaluation of Planning and Tool Learning (2025.acl-long)

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Challenge: Recent studies have shown that LLMs can be significantly improved by integrating external tools.
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IntentionQA: A Benchmark for Evaluating Purchase Intention Comprehension Abilities of Language Models in E-commerce (2024.findings-emnlp)

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Challenge: Existing approaches that distill intentions from LMs fail to generate meaningful and human-centric intentions applicable in real-world E-commerce contexts.
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Multimedia Generative Script Learning for Task Planning (2023.findings-acl)

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Challenge: Goal-oriented generative script learning aims to generate subsequent steps to reach a specific goal . ability to capture historical states in visual modalities provides detailed information not covered by text .
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Take a Break in the Middle: Investigating Subgoals towards Hierarchical Script Generation (2023.findings-acl)

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Challenge: Existing work assumes that events are sequentially arranged in a script, while this assumption leads to linear generation that is far from sufficient for comprehensively acquiring the representation about how events are organized towards a task goal.
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Is Your Language Model Ready for Monetization Decisions? (2026.findings-acl)

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Challenge: Existing benchmarks focus on shopping-centric scenarios and user-facing data, overlooking intermediate decision stages and robustness considerations.
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UniToolBench: A Benchmark for Tool-Augmented LLMs in Cross-Domain, Universal Task Automation (2026.findings-eacl)

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Challenge: Existing benchmarks that focus on manually curated tool graphs lack scalability and diversity across domains.
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TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Industry Systems (2024.emnlp-industry)

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Challenge: Large language models have demonstrated proficiency in addressing tasks that necessitate a combination of task planning and the usage of external tools.
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Choice-75: A Dataset on Decision Branching in Script Learning (2024.lrec-main)

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Challenge: Existing studies only consider scripts as linear developments of events, ignoring the potential branches that arise due to people’s circumstantial choices.
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