Duplicate from zhengyang6666/AISE-Bench

Co-authored-by: 王正阳 <zhengyang6666@users.noreply.huggingface.co>

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README.md

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+---
+license: mit
+pretty_name: AISE-Bench
+task_categories:
+  - question-answering
+language:
+  - zh
+  - en
+tags:
+  - academic-search
+  - knowledge-graph
+  - tool-use
+  - llm-agent
+  - benchmark
+  - question-answering
+  - information-seeking
+configs:
+  - config_name: default
+    default: true
+    data_files:
+      - split: test
+        path: test.json
+size_categories:
+  - n<1K
+---
+
+<div align="center">
+
+# AISE-Bench: A Full-Cycle Curated Benchmark for Information Seeking on Academic Knowledge Graphs
+
+</div>
+
+<p align="center">
+    &#127760; <a href="https://aise-bench.github.io/" target="_blank">Project Page</a> &bull;
+    &#128187; <a href="https://github.com/zai-org/AISE-Bench" target="_blank">GitHub</a> &bull;
+    &#128214; <a href="https://arxiv.org/abs/2607.20498" target="_blank">KDD 2026 Paper</a>
+</p>
+
+<div align="center">
+    <img src="assets/bench.png" width="100%" />
+</div>
+
+AISE-Bench is a real-world benchmark for information seeking on academic knowledge graphs. It is built from authentic AMiner user search queries and provides human-verified academic question-answering data with executable multi-step API trajectories, standardized tool inputs, API execution outputs, and source-grounded final answers.
+
+The benchmark is designed for evaluating LLM-based tool agents throughout the full information-seeking cycle: understanding user intent, planning API calls, filling parameters, executing multi-step academic KG queries, and producing final answers grounded in canonical references.
+
+## Dataset Files
+
+The current version places the latest data files at the dataset root. The previous version is archived under `v1/`.
+
+```text
+AISE-Bench/
+|-- README.md
+|-- double-review-remain.json
+|-- single-review.json
+|-- test.json
+`-- v1/
+    |-- README.md
+    |-- double-review-remain.json
+    |-- single-review.json
+    `-- test.json
+```
+
+### Current Version
+
+| File | Examples | Description |
+| --- | ---: | --- |
+| `single-review.json` | 673 | Examples annotated under the single-review setting. |
+| `test.json` | 500 | Benchmark test set from the double-review collection. |
+| `double-review-remain.json` | 195 | Remaining double-review examples that are not included in `test.json`. |
+
+The double-review collection is distributed as two non-overlapping files: `test.json` and `double-review-remain.json`. The combined `double-review.json` is intentionally omitted to avoid duplicating the same examples.
+
+The default Hugging Face dataset configuration exposes only the root-level `test.json` as the `test` split. Consequently, the Dataset Viewer displays exactly 500 rows; the other files remain available for direct download.
+
+### Previous Version
+
+The `v1/` directory contains the earlier release and is excluded from the default Dataset Viewer configuration.
+
+| File | Examples | Description |
+| --- | ---: | --- |
+| `v1/single-review.json` | 1,096 | Earlier single-review data. |
+| `v1/test.json` | 177 | Earlier test data. |
+| `v1/double-review-remain.json` | 77 | Earlier double-review examples whose normalized question text does not occur in `v1/test.json`. |
+
+The combined `v1/double-review.json` is also intentionally omitted. The archived test and remaining double-review files are kept separately.
+
+## Data Format
+
+Each JSON file contains a list of examples. Every example follows the same high-level schema.
+
+The `index` field is a one-based sequential position within each individual JSON file. Numbering
+restarts from `1` in every file and does not replace the stable question identifier in `qid`.
+
+Field descriptions:
+
+- `index`: One-based sequential position of the example within the current file.
+- `qid`: Unique question identifier.
+- `question`: Original academic information-seeking query.
+- `planning_text`: Gold multi-step API plan, including tool names, dependency relations, execution order, and parameters.
+- `api_input`: Standardized API input parameters used for execution.
+- `api_output`: Returned results from the academic knowledge graph APIs.
+- `result_edit`: Human-edited final answer grounded with reference links.
+
+## Practical Uses
+
+AISE-Bench can be used to evaluate and analyze academic-search agents and tool-using LLM systems.
+
+- Tool-use planning: evaluate whether an agent can decompose an academic query into executable API calls.
+- Parameter filling: test whether the agent can identify entities, constraints, keywords, institutions, authors, venues, and other required search parameters.
+- Multi-step execution: evaluate dependency-aware reasoning across chained academic KG calls.
+- Answer synthesis: test whether the model can generate final answers grounded in API outputs and reference links.
+- Agent framework comparison: compare different LLM agent workflows on the same academic information-seeking tasks.
+
+## Loading the Dataset
+
+Load the default 500-example test split with `datasets`:
+
+```bash
+pip install -U datasets
+```
+
+```python
+from datasets import load_dataset
+
+test_data = load_dataset("zhengyang6666/AISE-Bench", split="test")
+print(len(test_data))  # 500
+```
+
+To download all current and archived JSON files, use `huggingface_hub`:
+
+```bash
+pip install -U huggingface_hub
+hf download zhengyang6666/AISE-Bench --repo-type dataset --local-dir ./AISE-Bench
+```
+
+Individual JSON files can then be loaded directly:
+
+```python
+import json
+
+with open("AISE-Bench/test.json", "r", encoding="utf-8") as f:
+    data = json.load(f)
+
+print(len(data))
+print(data[0].keys())
+```
+
+## Citation
+
+If you use AISE-Bench in your research, please cite the paper:
+
+```bibtex
+@article{aisebench2026,
+  author={Zhang, Fanjin and Wang, Zhengyang and Huang, Ruixuan and Zhang, Kefan and Xin, Amy and Wang, Yuanchun and Zhao, Shu and Kharlamov, Evgeny and Tang, Jie and Li, Juanzi},
+  title={AISE-Bench: A Full-Cycle Curated Benchmark for Information Seeking on Academic Knowledge Graphs},
+  journal={arXiv preprint arXiv:2607.20498},
+  year={2026}
+}
+```

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v1/README.md

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+---
+license: mit
+---

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