| | --- |
| | license: apache-2.0 |
| | task_categories: |
| | - question-answering |
| | language: |
| | - en |
| | tags: |
| | - factuality |
| | - search |
| | - retrieval |
| | - deep research |
| | - comprehensiveness |
| | - agent |
| | - posttraining |
| | - benchmark |
| | - Google DeepMind |
| | pretty_name: DeepSearchQA |
| | size_categories: |
| | - n<1K |
| | configs: |
| | - config_name: deepsearchqa |
| | default: true |
| | data_files: |
| | - split: eval |
| | path: DSQA-full.csv |
| | --- |
| | |
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| |
|
| | ▶ [Google DeepMind Release Blog Post](https://blog.google/technology/developers/deep-research-agent-gemini-api/)\ |
| | ▶ [DeepSearchQA Leaderboard on Kaggle](https://www.kaggle.com/benchmarks/google/dsqa)\ |
| | ▶ [Technical Report](https://storage.googleapis.com/deepmind-media/DeepSearchQA/DeepSearchQA_benchmark_paper.pdf)\ |
| | ▶ [Evaluation Starter Code](https://www.kaggle.com/code/andrewmingwang/deepsearchqa-starter-code) |
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| | DeepSearchQA is a 900-prompt benchmark for evaluating agents on difficult multi-step information-seeking tasks across 17 different fields. Unlike traditional benchmarks that target single-answer retrieval or broad-spectrum factuality, DeepSearchQA features a dataset of challenging, hand-crafted tasks designed to evaluate an agent’s ability to execute complex search plans to generate exhaustive answer lists. |
| |
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| | Each task is structured as a "causal chain", where discovering information for one step is dependent on the successful completion of the previous one, stressing long-horizon planning and context retention. All tasks are grounded in the open web with objectively verifiable answer sets. |
| |
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| | DeepSearchQA is meant to be used to evaluate LLMs or LLM agents with access to the web. |
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| | This dataset is a collection of 900 examples. Each example is composed of: |
| |
|
| | * A problem (`problem`) which is the prompt testing parametric knowledge. |
| | * A problem category (`problem_category`) specifying which of 17 different domains the problem belongs to. |
| | * A gold answer (`answer`) which is used in conjunction with the evaluation prompt to judge the correctness of an LLM's response. |
| | * An answer type classification (`answer_type`) specifying whether a single answer or set of answers is expected as a response. This information should NOT be given to the LLM during inference time. 65% of answers are of type `Set Answer`. |
| |
|
| | See the [Technical Report](https://storage.googleapis.com/deepmind-media/DeepSearchQA/DeepSearchQA_benchmark_paper.pdf) for methodology details. |
| |
|
| | |
| | While DeepSearchQA offers a robust framework for evaluating comprehensive retrieval, it relies on |
| | specific design choices that entail certain limitations. By employing an exclusively outcome-based |
| | evaluation, we effectively treat any agent that is evaluated as a black box. In the absence of trajectory data, it is difficult |
| | to distinguish between an agent that reasoned correctly and one that arrived at the correct list through |
| | inefficient or accidental means (e.g., lucky guessing). Additionally, the static web assumption, while |
| | necessary for reproducibility, limits the evaluation of “breaking news” retrieval where ground truth is |
| | volatile. A task’s ground truth may become outdated if source websites are removed or their content |
| | is significantly altered. This is a prevalent challenge for all benchmarks operating on the live web, |
| | necessitating periodic manual reviews and updates to the dataset. |
| |
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| | Questions, comments, or issues? Share your thoughts with us in the [discussion forum](https://www.kaggle.com/benchmarks/google/dsqa/discussion). |
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| | |
| | The autorater which should be used for DeepSearchQA is `gemini-2.5-flash` with the grading prompt found in the [starter notebook](https://www.kaggle.com/code/andrewmingwang/deepsearchqa-starter-code) on Kaggle. Using a different autorater model or grading prompt will likely result in statistically significant deviation in results. |
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| | Coming soon. |
