Researchers propose KDASO for automated liability adjudication
A new paper on arXiv introduces KDASO, a framework designed to improve how Large Language Models handle liability adjudication tasks, such as legal charge determination. The authors argue that current LLM approaches suffer from three main issues: skill libraries become outdated as real-world scenarios change, descriptions often fail to match the LLM's actual execution logic, and large skill…
Key points
- KDASO evolves skill libraries using recent data to prevent staleness in liability adjudication.
- The method uses context-budgeted routing to avoid omitting critical skills due to token limits.
- KDASO shows an 8.96% improvement when transferring skills to concept-similar scenarios.
KDASO addresses these problems by continuously evolving the skill library using recent adjudication data. It refines skills based on the LLM's execution trajectories and uses context-budgeted routing to select the most relevant skills without exceeding token limits. The method combines knowledge-driven and data-driven optimization to keep the system current and efficient.
Experiments in ride-hailing adjudication, legal liability, and logical reasoning show that KDASO outperforms existing general-purpose and domain-specific baselines. The authors report an 8.96% improvement when transferring skills to similar scenarios, suggesting the generated skills are stable and transferable. The paper highlights the potential for automated, interpretable reasoning in complex legal and liability contexts.
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