---
license: cc-by-nc-4.0
pretty_name: Shopping Reasoning Bench
language:
- en
task_categories:
- text-generation
tags:
- shopping
- e-commerce
- multi-turn
- reasoning
- llm-evaluation
- llm-as-a-judge
- rubric
configs:
- config_name: full_single_turn
  data_files:
  - split: test
    path: data/full_single_turn.jsonl
- config_name: full_multi_turn
  data_files:
  - split: test
    path: data/full_multi_turn.jsonl
- config_name: hard_single_turn
  data_files:
  - split: test
    path: data/hard_single_turn.jsonl
- config_name: hard_multi_turn
  data_files:
  - split: test
    path: data/hard_multi_turn.jsonl
---

# Shopping Reasoning Bench

Conversational shopping assistants now serve hundreds of millions of customers, yet no existing benchmark jointly evaluates the open-ended multi-turn reasoning, domain expertise, and criterion-level quality that real shopping conversations demand. We introduce the **Shopping Reasoning Bench**, an expert-authored benchmark of 525 missions (232 single-turn, 293 multi-turn) with 10,863 importance-weighted binary rubrics authored by retail domain experts. These criteria are organized under a taxonomy of five reasoning categories and fifteen subcategories covering diverse demands such as preference refinement, trade-off analysis, and compatibility assessment.

Shopping reasoning is unique among language model applications. Unlike factual question answering or verifiable code generation, it requires balancing subjective preferences, budget constraints, and cross-product trade-offs across multi-turn dialogue, capabilities absent from previous e-commerce and general-purpose benchmarks.

- 📄 **Paper:** [Shopping Reasoning Bench: An Expert-Authored Benchmark for Multi-Turn Conversational Shopping Assistants](https://arxiv.org/abs/2606.12608)
- 🏷️ **License:** CC-BY-NC-4.0 (research / non-commercial use)

## Dataset Summary

- **Expert-authored multi-turn shopping dataset.** 232 single-turn queries and 293 multi-turn missions (1,764 turns) authored by retail domain experts across five product families.
- **Importance-weighted atomic rubric framework.** 10,863 binary criteria (85.0% required) that decompose expert shopping reasoning into independently verifiable pass/fail checks.
- **First taxonomy of pre-purchase shopping reasoning.** Five categories and fifteen subcategories grounded in expert-annotated turns (Product Recommendation, Shopping Guidance, Product Comparison, Product Inquiry, Conversational Navigation).
- **Validated LLM-as-judge.** A single LLM judge applies uniform decision criteria across the benchmark; its reliability is validated against expert annotations. The judge prompt is included.

## Dataset Structure

The benchmark is released as four configurations, each a single `test` split. The `full_*` configs are the complete benchmark of 525 missions and 10,863 rubrics. The `hard_*` configs are a focused **Shopping Reasoning Bench-Hard** subset of the 108 missions where the nine-model average weighted pass rate falls below 60%, representing the missions that current models collectively struggle with; it is designed for tracking progress on the most demanding shopping reasoning problems.

| Config | Missions | Turns | Rubrics |
|---|---|---|---|
| `full_single_turn` | 232 | 232 | 821 |
| `full_multi_turn` | 293 | 1,764 | 10,042 |
| `hard_single_turn` | 69 | 69 | 252 |
| `hard_multi_turn` | 39 | 235 | 1,411 |

Each line is one **mission**: top-level metadata (`mission_id`, `mission_type`, `product_family`, …) plus a list of `turns`. Each turn carries the customer `messages` and a list of `rubrics`, where each rubric has a `text`, an `importance` (`required` / `optional`), and taxonomy tags. See the [paper](https://arxiv.org/abs/2606.12608) (Appendix G) for the full field reference.

## Usage

```python
from datasets import load_dataset

ds = load_dataset("amazon/ShoppingReasoningBench", "full_multi_turn", split="test")

mission = ds[0]
print(mission["mission_name"])
for turn in mission["turns"]:
    print(turn["messages"][-1]["content"])
    for rubric in turn["rubrics"]:
        print(f"  [{rubric['importance']}] {rubric['text']}")
```

Configs: `full_single_turn`, `full_multi_turn`, `hard_single_turn`, `hard_multi_turn`.

## Evaluation

Shopping Reasoning Bench aggregates atomic rubric judgments into per-turn, per-mission, and dataset-level scores via importance-weighted pass rates. Each rubric carries an importance weight (`w_i = 5` for required rubrics, `w_i = 1` for optional), and the weighted pass rate for a response is the sum of weights of passed rubrics over the sum of all weights. Scores aggregate hierarchically: the per-mission score is the mean of its per-turn scores, and the dataset-level score is the mean of per-mission scores.

Each rubric is scored by a single LLM judge that produces a binary pass/fail decision with a brief rationale; the paper uses Claude Sonnet 4.5 as the judge at temperature 0. The judge prompt is released as [`judge_prompt.txt`](judge_prompt.txt); its `<<current_conversation>>`, `<<rubric_text>>`, and `<<conversation_history>>` placeholders are filled at evaluation time with the current turn, rubric text, and (for multi-turn) conversation history through the preceding turn.

## Citation

```bibtex
@article{fan2026shopping,
  title={Shopping Reasoning Bench: An Expert-Authored Benchmark for Multi-Turn Conversational Shopping Assistants},
  author={Fan, Shuxian and Min, Seonwoo and Hu, Youna and Xia, Botao and Unnikrishnan, Jayakrishnan and Musselmann, Rowan and Gao, Yifan and Yin, Qingyu and Nigam, Priyanka and Yin, Bing},
  journal={arXiv preprint arXiv:2606.12608},
  year={2026}
}
```