---
annotations_creators:
  - human-annotated
language:
  - en
license: cc-by-4.0
multilinguality:
  - monolingual
pretty_name: LLMAdBench
size_categories:
  - 10K<n<100K
source_datasets:
  - other
task_categories:
  - text-classification
  - text-ranking
tags:
  - advertising
  - human-preferences
  - pairwise-comparison
  - llm-as-a-judge
  - preference-modeling
  - sponsored-content
  - datasets
configs:
  - config_name: labeled
    data_files:
      - split: train
        path: labeled_train.parquet
      - split: test
        path: labeled_test.parquet
  - config_name: unlabeled
    data_files:
      - split: train
        path: unlabeled_train.parquet
      - split: test
        path: unlabeled_test.parquet
---

# LLMAdBench: A Human Preference Benchmark for Advertising in LLM Responses

LLMAdBench is a controlled, human-annotated benchmark for studying where advertisements should be placed in LLM-generated responses. Given a user conversation, a base response, and a matched advertisement, the benchmark compares response pairs that differ in advertisement placement while holding the underlying conversation, answer, advertised entity, core advertising message, and disclosure condition fixed.

The benchmark contains **258 source conversations**, each expanded into four placement candidates—**front**, **early**, **late**, and **end**—and all six unordered placement pairs. The same materials are evaluated under two disclosure conditions:

- **Labeled:** the advertisement is explicitly marked as `[Sponsored: <ad text>]`.
- **Unlabeled:** the advertisement is integrated without an explicit sponsorship marker.

Human annotators compare Version A and Version B and select **A**, **B**, or **Tie** along six dimensions. In total, the source data contain **1,548 pairwise comparisons per disclosure condition** and **18,576 dimension-level human judgments**.

## Links

- **Dataset:** [ruiai-work/LLMADBENCH](https://huggingface.co/datasets/ruiai-work/LLMADBENCH)
- **Paper:** [arXiv:2609.32533](https://arxiv.org/abs/2609.32533)

## Supported Tasks

LLMAdBench supports research on:

- three-way human-preference prediction (`A`, `B`, or `T`);
- pairwise ranking of advertisement placements;
- preference-model and LLM-judge training;
- robustness to A/B presentation-order swaps;
- the effects of advertisement placement and sponsorship disclosure;
- trade-offs among user experience, advertisement visibility, and stated click likelihood.

For benchmark evaluation, report **exact three-way accuracy** separately for the labeled and unlabeled conditions. Because rows derived from the same source comparison are related, use the provided train/test files rather than creating a row-level random split.

## Benchmark Results

We report the held-out exact-match accuracy (%) for three-way A/B/T prediction. The first eight models are evaluated as zero-shot judges. **Pre-DPO** and **Post-DPO** refer to Qwen3-8B before and after five epochs of Direct Preference Optimization on the corresponding disclosure condition.

| Condition | GPT-5.4 | Claude Opus 4.6 | Gemini 3.1 Pro | Grok 4.1 | Kimi K3 | DeepSeek V4 Pro | Llama 4 Maverick | Mistral Large 3 | Pre-DPO | Post-DPO |
| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
| Labeled | 47.85 | 46.96 | 48.14 | 46.31 | 47.23 | 44.00 | 44.51 | 46.39 | 40.25 | **50.54** |
| Unlabeled | 50.32 | 48.74 | 49.92 | 47.52 | 49.87 | 46.18 | 48.09 | 48.76 | 36.52 | **55.76** |

## Evaluation Dimensions

| Dimension | Annotation question |
| --- | --- |
| Naturalness of Flow | Which version reads more smoothly and feels less disruptive? |
| Ad Visibility | In which version is the advertisement easier to notice? Visibility is a neutral perceptual judgment. |
| Content Helpfulness | Which version better preserves the usefulness of the non-advertising content for answering the user? |
| Click Likelihood | If required to click one advertisement, which version's advertisement would the annotator be more likely to click? |
| Appropriateness | Which version feels more trustworthy and less manipulative, including whether the advertisement is suitable, transparent, and not overly pushy? |
| Overall Preference | Which version does the annotator prefer, all things considered? |

Click likelihood represents **stated behavioral intention**, not observed clicking behavior.

## Dataset Files

### Source comparison files

| File | Rows | Description |
| --- | ---: | --- |
| `Labeled.parquet` | 1,548 | Human comparisons with explicit sponsorship disclosure. |
| `Unlabeled.parquet` | 1,548 | Human comparisons without explicit sponsorship disclosure. |

Each row contains one placement pair and all six human judgments.

### Preference-model files

| Configuration | Train | Test | Total |
| --- | ---: | ---: | ---: |
| Labeled | 23,530 | 3,714 | 27,244 |
| Unlabeled | 21,381 | 3,716 | 25,097 |

These files transform the source comparisons into dimension-level A/B/T preference examples, including A/B-swapped variants and preference-versus-tie examples. The split is constructed at the original comparison level before augmentation, using an 80/20 split, so related augmented rows do not cross train and test.

## Loading the Dataset

```python
from datasets import load_dataset

labeled = load_dataset("ruiai-work/LLMADBENCH", "labeled")
unlabeled = load_dataset("ruiai-work/LLMADBENCH", "unlabeled")

print(labeled["train"][0])
```

To load the source comparison tables directly:

```python
from datasets import load_dataset

source_labeled = load_dataset(
		"parquet",
		data_files="hf://datasets/ruiai-work/LLMADBENCH/Labeled.parquet",
		split="train",
)
```

## Data Schema

### Source comparison schema

The files `Labeled.parquet` and `Unlabeled.parquet` share the following fields:

| Field | Type | Description |
| --- | --- | --- |
| `original_ad` | string | Original matched advertisement sentence or brief. |
| `context` | string | Previous conversation context, when available. |
| `question` | string | Current user request. |
| `original_response` | string | Base assistant response before advertisement insertion. |
| `rewritten_ad_a` | string | Advertisement wording used in Version A. |
| `rewritten_ad_b` | string | Advertisement wording used in Version B. |
| `response_a` | string | Candidate response shown as Version A. |
| `response_b` | string | Candidate response shown as Version B. |
| `question_naturalness` | string | Naturalness annotation prompt. |
| `answer_naturalness` | string | Human A/B/T judgment for naturalness. |
| `question_visibility` | string | Visibility annotation prompt. |
| `answer_visibility` | string | Human A/B/T judgment for visibility. |
| `question_helpfulness` | string | Helpfulness annotation prompt. |
| `answer_helpfulness` | string | Human A/B/T judgment for helpfulness. |
| `question_click_likelihood` | string | Click-likelihood annotation prompt. |
| `answer_click_likelihood` | string | Human A/B/T judgment for stated click likelihood. |
| `question_appropriateness` | string | Appropriateness annotation prompt. |
| `answer_appropriateness` | string | Human A/B/T judgment for appropriateness. |
| `question_overall_preference` | string | Overall-preference annotation prompt. |
| `answer_overall_preference` | string | Human A/B/T overall judgment. |

### Preference-model schema

The files `labeled_{train,test}.parquet` and `unlabeled_{train,test}.parquet` share the following fields:

| Field | Type | Description |
| --- | --- | --- |
| `split` | string | `train` or `test`. |
| `question_id` | string | Rubric question identifier (`Q1`–`Q6`). |
| `dimension` | string | Human-readable evaluation dimension. |
| `dimension_instruction` | string | Instruction defining the dimension. |
| `ab_variant` | string | `orig` for the original presentation or `swap` for the A/B-swapped presentation. |
| `pair_type` | string | Transformation type: `pref_vs_alt`, `pref_vs_tie`, or `tie_vs_alt`. |
| `gold` | string | Preferred completion label: `A`, `B`, or `T`. |
| `rejected` | string | Rejected completion label: `A`, `B`, or `T`. |
| `previous_context` | string | Previous conversation turns, or `None`. |
| `user_question` | string | Current user request. |
| `version_a` | string | First candidate response. |
| `version_b` | string | Second candidate response. |
| `prompt` | string | Complete judge prompt requesting a JSON choice. |

The expected judge output is exactly one of:

```json
{"choice": "A"}
```

```json
{"choice": "B"}
```

```json
{"choice": "T"}
```

## Dataset Construction

### Source data and matching

LLMAdBench combines conversational contexts from **WildChat** with advertisement material derived from the **Webis Generated Native Ads 2024** dataset. The processing unit is one WildChat conversation turn. The current user question and assistant response are embedded with `Qwen3-Embedding-0.6B` and matched to candidate advertisements by cosine similarity. Up to five candidates above a similarity threshold of 0.65 are retained, after which `Qwen3-8B` filters irrelevant matches.

Human reviewers then curate the candidate pool, removing non-English, malformed, incomplete, meaningless, overly specialized, or otherwise unsuitable records. At most one advertisement candidate is retained for each conversation turn.

### Placement candidates

Each retained conversation-advertisement pair produces four candidates:

1. **Front:** before the base response;
2. **Early:** at the earlier of the two selected internal sentence boundaries;
3. **Late:** at the later selected internal sentence boundary;
4. **End:** after the base response.

The two internal boundaries are selected by a fixed embedding-based continuity score. `Qwen3-8B` rewrites only the advertisement text to fit its local context while preserving the advertised product, brand, or entity and the core advertising information. The substantive base answer remains fixed.

### Human annotation and quality control

Candidate order and task order were randomized. Annotators answered all six rubric questions with Version A, Version B, or Tie.

Quality control used two attention checks with verifiable answers, including an advertisement-placement check and a text-similarity check. Records failing either check were recollected in a second annotation round; replacement annotations passed both checks.

## Intended Uses

The dataset is intended for research and evaluation involving advertisement placement in LLM responses, human-preference modeling, pairwise ranking, LLM judges, disclosure effects, and preference optimization.

It is **not** intended to:

- endorse any advertised product, brand, or claim;
- serve as evidence that an advertisement is truthful, safe, or legally compliant;
- predict real-world clicks, purchases, or conversion rates;
- justify undisclosed or deceptive advertising;
- replace user studies, legal review, or platform-specific safety evaluation.



## License

LLMAdBench is released under the [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/) (**CC BY 4.0**). Users are also responsible for respecting the terms and licenses of the underlying WildChat and Webis-derived source materials.

## Citation

If you use LLMAdBench, please cite:

```bibtex
@article{ai2026llmadbench,
	title   = {LLMAdBench: A Human Preference Benchmark for Advertising in LLM Responses},
	author  = {Ai, Rui and Liu, Yuqing and Qiu, Sitao and Qiao, Yun and Wang, Yuhan and Wang, Jessica Xiwen and Yang, Yiqi and Huang, Lihong and Sun, Ruiyao and Zhang, Kaifeng and Ding, Shengze and He, Jiaqi and Wang, Xinman and Gao, Tianhao and Qin, Jimmy and Lin, Jianghao and Wang, Chonghuan},
	journal = {arXiv preprint arXiv:2609.32533},
	year    = {2026},
	url     = {https://arxiv.org/abs/2609.32533}
}
```

## Authors

Rui Ai, Yuqing Liu, Sitao Qiu, Yun Qiao, Yuhan Wang, Jessica Xiwen Wang, Yiqi Yang, Lihong Huang, Ruiyao Sun, Kaifeng Zhang, Shengze Ding, Jiaqi He, Xinman Wang, Tianhao Gao, Jimmy Qin, Jianghao Lin, and Chonghuan Wang.