Duplicate from google/deepsearchqa

Co-authored-by: Lukas Haas <geolocal@users.noreply.huggingface.co>

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README.md

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+---
+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
+---
+# DeepSearchQA
+#### A 900-prompt factuality benchmark from Google DeepMind, designed to evaluate agents on difficult multi-step information-seeking tasks across 17 different fields.
+
+▶ [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)
+
+
+## Benchmark
+
+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.
+
+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. 
+
+DeepSearchQA is meant to be used to evaluate LLMs or LLM agents with access to the web.
+
+## Dataset Description
+
+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.
+
+## Limitations
+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.
+
+Questions, comments, or issues? Share your thoughts with us in the [discussion forum](https://www.kaggle.com/benchmarks/google/dsqa/discussion).
+
+## Evaluation Prompt
+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.
+
+## Citation
+
+Coming soon.