| --- |
| license: apache-2.0 |
| task_categories: |
| - question-answering |
| - text-generation |
| language: |
| - en |
| tags: |
| - agent |
| size_categories: |
| - n<1K |
| --- |
| # GISA: A Benchmark for General Information-Seeking Assistant</h1> |
|
|
| ## Benchmark Highlights |
| GISA is a benchmark for General Information-Seeking Assistants with 373 human-crafted queries that reflect real-world information needs. It includes both stable and live subsets, four structured answer formats (item, set, list, table), and complete human search trajectories for every query. |
| - **Diverse answer formats with deterministic evaluation.** |
| GISA uses four structured answer types (item, set, list, table) with strict matching metrics for reproducible evaluation, avoiding subjective LLM judging while preserving task diversity. |
| - **Unified deep + wide search capabilities.** |
| Tasks require both vertical reasoning and horizontal information aggregation across sources, evaluating long-horizon exploration and summarization in one benchmark. |
| - **Dynamic, anti-static evaluation.** |
| Queries are split into stable and live subsets; the live subset is periodically updated to reduce memorization and keep the benchmark challenging over time. |
| - **Process-level supervision via human trajectories.** |
| Full human search trajectories are provided for every query, serving as gold references for process reward modeling and imitation learning while validating task solvability. |
|
|
| ## Evaluation |
| Please refer to our [GitHub](https://anonymous.4open.science/r/GISA/). |
|
|
| ## Data Schema |
|
|
| #### 1. encrypted_question.jsonl |
| Each row contains: |
| - id (int): the ID of the question (it is **not** continuous) |
| - question (str): the question after encryption |
| - answer_type (str): the type of the answer, can be item, set, list, or table |
| - question_type (str): the type of the question, can be stable or live |
| - topic (str): the topic of the question, can be TV Shows \& Movies, Science \& Technology, Art, History, Sports, Music, Video Games, Geography, Politics, or Other |
| - canary (str): the password used for decryption |
| |
| #### 2. answer/[id].csv |
| The file contains the answer corresponds to the question [id]. |
| |
| #### 3. trace/[id].json |
| The file conatins the human trajectory of the question [id], with the following keys: |
| - search (list): the queries issued by the annotator |
| - result (dict): the search result of each query |
| - click (list): the click behaviors made by the annotator |
| - |
| ## Loading Method |
| ```python |
| def derive_key(password: str, length: int) -> bytes: |
| hasher = hashlib.sha256() |
| hasher.update(password.encode()) |
| key = hasher.digest() |
| return key * (length // len(key)) + key[: length % len(key)] |
| def decrypt(ciphertext_b64: str, password: str) -> str: |
| encrypted = base64.b64decode(ciphertext_b64) |
| key = derive_key(password, len(encrypted)) |
| decrypted = bytes(a ^ b for a, b in zip(encrypted, key)) |
| return decrypted.decode() |
| obj["question"] = decrypt(str(obj["question"]), str(obj["canary"])) |
| ``` |
| |
