hsuvaskakoty/DRIP-R
DRIP-R: Decision-making and Reasoning In ambiguous Policy for Retail 📄 Paper (arXiv:2605.07699) · 💻 Code Dataset Summary DRIP-R is a benchmark for evaluating LLM customer-service agents when the governing policy is ambiguous. It pairs 40 return tasks, each built on at least one ambiguous clause of Amazon's return policy so that no single resolution is "correct", with 10 customer personas, giving 400 scenarios. Dataset at a Glance Attribute… See the full description on the dataset page: https://huggingface.co/datasets/hsuvaskakoty/DRIP-R.
| Author / organization | hsuvaskakoty |
|---|---|
| Downloads | 0 (as reported by Hugging Face) |
| Likes | 0 |
| Created | 23 October 2026 |
| Last updated at source | 23 October 2026 |
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- 23 Oct 2026 License: mit → none
- 23 Oct 2026 Description: none → DRIP-R: Decision-making and Reasoning In ambiguous Policy for Retail 📄 Paper (arXiv:2605.07699) · 💻 Code Dataset Summary DRIP-R is a benchmark for evaluating LLM customer-service agents when the governing policy is ambiguous. It pairs 40
- 23 Oct 2026 Topics added task_categories:text-generation, agent, benchmarking, policy-adherence
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