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README.md

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----
-license: apache-2.0
----
+# CommunityBench
+
+## Dataset Description
+
+CommunityBench is a benchmark dataset for evaluating language models' ability to understand and align with online community preferences. The dataset is constructed from Reddit posts and comments, focusing on real-world scenarios where models need to reason about community values, predict preference distributions, identify community-specific communication patterns, and generate content that aligns with community norms.
+
+## Dataset Structure
+
+The dataset consists of two splits:
+- **train.jsonl**: Training set
+- **test.jsonl**: Test/evaluation set
+
+Each line in the JSONL files contains a JSON object representing a single sample.
+
+## Task Types
+
+The dataset includes four distinct tasks:
+
+1. **`pref_id`** (Preference Identification): Identify which option best matches a community's preferences
+2. **`dist_pred`** (Distribution Prediction): Predict the popularity distribution across multiple options
+3. **`com_pred`** (Communication Prediction): Predict community-specific communication patterns
+4. **`steer_gen`** (Steering Generation): Generate content that aligns with community norms and preferences
+
+## Dataset Statistics
+
+- **Task distribution**: Each task type (`pref_id`, `dist_pred`, `com_pred`, `steer_gen`) has an equal number of samples
+- **Options per sample** (for tasks with options): Average ~4.0 options per sample
+
+## Usage
+
+You can load and use the dataset with the Hugging Face `datasets` library:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("jylin001206/communitybench", split="train")
+```
+
+Or load specific splits:
+
+```python
+train_dataset = load_dataset("jylin001206/communitybench", split="train")
+test_dataset = load_dataset("jylin001206/communitybench", split="test")
+```
+
+## Data Fields
+
+Each sample in the dataset contains community portraits, request-option sets, and task-specific annotations. The exact schema depends on the task type and includes information about:
+- Subreddit and thread context
+- Community portraits
+- Request-option pairs
+- Ground truth labels or distributions

test.jsonl

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train.jsonl

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