The field of artificial intelligence in legal research is moving towards the development of more efficient and fair models for legal assistance and petition ranking. Recent studies have shown that large language models (LLMs) and transformer-based models can be effective in providing legal assistance and streamlining judicial workflows. However, these models also raise concerns about bias and fairness, particularly in the evaluation of vision-language models. Researchers are working to address these concerns by developing frameworks to evaluate bias in LLMs and vision-language models, and to identify attribution biases that can perpetuate stereotypes and influence decisions. Noteworthy papers in this area include:

• LLMPR, which proposes an automated petition ranking model that achieves high accuracy and correlation, and
• Legal Assist AI, which introduces a transformer-based model for effective legal assistance that outperforms state-of-the-art models in legal reasoning and accuracy.