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🚨 Excited to share our new paper to appear in the Findings of ACL 2026! "JudgeMeNot" asks: Can LLMs learn the specific reasoning signature of an individual judge? We tackle this in the low-resource setting of Hebrew case law. 🔗 arxiv.org/abs/2604.18041 🧵👇

2/8🧑‍⚖️ Judges aren't interchangeable; they have distinct judicial philosophies. Yet, most Legal NLP treats them as one generic voice. The result? AI "mode collapse." Standard models generate long, encyclopedic text that completely misses the human legal fingerprint.

3/8 🛠️ The bottleneck for personalization is the lack of reasoning supervision. To fix this, we built a "synthetic-organic" pipeline. We use an agentic workflow to extract raw reasoning sentences from unstructured Israeli verdicts and generate targeted synthetic questions that prompted them.

5/8 📊 The results? CoLA consistently outperforms all baselines. RAG captures surface-level style (like POS distributions), but fails in reasoning. CoLA-generated reasoning is so faithful that an LLM trained specifically on each judge cannot tell the difference from the actual judge. 🤯

6/8 While all data analyzed here is entirely public, by public officials operating in their official capacity for public scrutiny, the sensitivity of the domain + personalization requires further ethical considerations beyond simple regulatory compliance as detailed in Section 9 of the paper.

7/8 🛤️ An important caveat is that our work successfully emulates sentence-level, granular reasoning, which is considerable easier that modeling the full trajectory of case.