LLM_Legal_Prompt_Generation下載LLM_Legal_Prompt

LLM_Legal_Prompt_Generation

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LLM-好，壞還是必不可少的？：關於法律法規預測和法律判決預測的用例

該存儲庫包含論文“ LLMS的相關數據和代碼 - 好，壞或不可或缺的？

這是目錄樹。

 LLM_Legal_Prompt_Generation
├── Judgment Prediction
│   ├── LLM
│   │   ├── Codes
│   │   │   ├── jp.py
│   │   │   ├── jpe.py
│   │   ├── Datasets
│   │   │   ├── JP.csv
│   │   │   ├── JPE.csv
│   │   │   ├── JPE_with_pet_res.csv
│   │   │   ├── JP_with_pet_res.csv
│   │   ├── readme.md
│   ├── Transformer based Models
│   │   ├── Codes
│   │   │   ├── Evalution on ILDC expert dataset.ipynb
│   │   │   ├── Legal_judgment_training_with_transformers.py
│   │   ├── Datasets
│   │   │   ├── readme.md
│   ├── surname_wordlist
│   │   ├── hindu_surname_file.txt
│   │   ├── muslim_surname_file.txt
├── Statute Prediction
│   ├── Baseline Models
│   │   ├── data_generator.py
│   │   ├── evaluate.py
│   │   ├── metrics.py
│   │   ├── train.py
│   │   ├── utils.py
│   │   ├── Model
│   │   │   ├── Multi-label Classification
│   │   │   │   ├── net.py
│   │   │   ├── Binary Classification
│   │   │   │   ├── net.py
│   │   ├── Experiments
│   │   │   ├── params
│   │   │   │   ├── params_inlegalbert.json
│   │   │   │   ├── params_legalbert.json
│   │   │   │   ├── params_xlnet.json
│   ├── LLM
│   │   ├── Codes
│   │   │   ├── ALL TASK CODE.ipynb
│   │   │   ├── ALL TASK CODE.py
│   │   ├── Datasets
│   │   │   ├── 13_Cases_Gender and Bias Prediction_with explanations.csv
│   │   │   ├── 245cases.csv
│   │   │   ├── Gender and Religion Bias cases.csv
│   │   │   ├── query.csv
│   │   │   ├── statute_pred_100_cases_without_exp-gender_religion_bias.csv
│   │   │   ├── statute_pred_100_cases_without_exp.csv
│   │   │   ├── statute_pred_45_cases_with_exp.csv
│   │   │   ├── statute_pred_45_cases_without_exp.csv
│   │   ├── readme.md
├── README.md

抽象的

大型語言模型（LLM）影響了許多現實生活任務。為了檢查LLM在像法律這樣的高風險領域的功效，我們已將最先進的LLMS應用於兩項流行的任務：對印度最高法院案件的法規預測和判斷預測。我們看到，儘管LLM在法規預測中表現出出色的預測性能，但與許多標準模型相比，它們的績效下降。 LLMS（以及預測）產生的解釋具有中等至不錯的質量。我們還看到LLM預測結果中性別和宗教偏見的證據。此外，我們還提供了一位高級法律專家的註釋，內容涉及在這些關鍵法律任務中部署LLM的道德問題。

作者

Shaurya Vats，Atharva Zope，Somsubhra de，Anurag Sharma，Apal Bhattacharya，Shubham Kumar Nigam，Shouvik Kumar Guha，Koustav Rudra，Kripabandhu Ghosh

引用

 @inproceedings{vats-etal-2023-llms,
    title = "{LLM}s {--} the Good, the Bad or the Indispensable?: A Use Case on Legal Statute Prediction and Legal Judgment Prediction on {I}ndian Court Cases",
    author = "Vats, Shaurya  and
      Zope, Atharva  and
      De, Somsubhra  and
      Sharma, Anurag  and
      Bhattacharya, Upal  and
      Nigam, Shubham  and
      Guha, Shouvik  and
      Rudra, Koustav  and
      Ghosh, Kripabandhu",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-emnlp.831",
    pages = "12451--12474",
    abstract = "The Large Language Models (LLMs) have impacted many real-life tasks. To examine the efficacy of LLMs in a high-stake domain like law, we have applied state-of-the-art LLMs for two popular tasks: Statute Prediction and Judgment Prediction, on Indian Supreme Court cases. We see that while LLMs exhibit excellent predictive performance in Statute Prediction, their performance dips in Judgment Prediction when compared with many standard models. The explanations generated by LLMs (along with prediction) are of moderate to decent quality. We also see evidence of gender and religious bias in the LLM-predicted results. In addition, we present a note from a senior legal expert on the ethical concerns of deploying LLMs in these critical legal tasks.",
}