Update README.md

README.md

@@ -7,4 +7,22 @@ language:
 tags:
 - legal
 pretty_name: Prediction of Chinese Judicial Documents
----
+---
+
+## summary
+The current research on large language models (LLMs) has demonstrated that general-purpose LLMs can retain considerable capability in vertical domains, which is partly attributed to the advancements in existing high-quality research on large model reasoning techniques. The following phenomena have been observed in the application of large language models to legal judgment prediction:
+
+1. LLMs exhibit certain biases in predicting criminal charges, tending to favor common and frequently occurring offenses.
+
+2. When faced with charges of weak inclination, the models may disregard instructions and opt for charges with stronger inclinations.
+
+To fairly evaluate the legal judgment prediction capabilities of large models, we designed the Prediction of Chinese Judicial Documents (PCJD) and developed a sampling-based reasoning method called Elements Reward Guided Inference (ERGI). The PCJD consists of two components: the "Original Set"(ori) and the "Adversarial Set"(adv). The data can be accessed by running the following code:
+
+```python
+from datasets import load_dataset
+dataset = load_dataset("knockknock404/PCJD", "all", split="test")
+# Load "Original Set"(ori)
+dataset_math = load_dataset("knockknock404/PCJD", "ori", split="test")
+# Load "Adversarial Set"
+dataset_math = load_dataset("knockknock404/PCJD", "adv", split="test")
+```