A Method for Detecting Legal Article Competition for Korean Criminal Law Using a Case-augmented Mention Graph

As social systems become increasingly complex, legal articles are also growing more intricate, making it progressively harder for humans to identify any potential competitions among them, particularly when drafting new laws or applying existing laws. Despite this challenge, no method for detecting such competitions has been proposed so far. In this paper, we propose a new legal AI task called Legal Article Competition Detection (LACD), which aims to identify competing articles within a given law. Our novel retrieval method, CAM-Re2, outperforms existing relevant methods, reducing false positives by 20.8% and false negatives by 8.3%, while achieving a 98.2% improvement in precision@5, for the LACD task. We release our codes at https://github.com/asmath472/LACD-public.

RL-MILP Solver: A Reinforcement Learning Approach for Solving Mixed-Integer Linear Programs with Graph Neural Networks

Mixed-Integer Linear Programming (MILP) is an optimization technique widely used in various fields. Primal heuristics, which reduce the search space of MILP, have enabled traditional solvers (e.g., Gurobi) to efficiently find high-quality solutions. However, traditional primal heuristics rely on expert knowledge, motivating the advent of machine learning (ML)-based primal heuristics that learn repetitive patterns in MILP. Nonetheless, existing ML-based primal heuristics do not guarantee solution feasibility (i.e., satisfying all constraints) and primarily focus on prediction for binary decision variables. When addressing MILP involving non-binary integer variables using ML-based approaches, feasibility issues can become even more pronounced. Since finding an optimal solution requires satisfying all constraints, addressing feasibility is critical. To overcome these limitations, we propose a novel reinforcement learning (RL)-based solver that interacts with MILP to find feasible solutions, rather than delegating sub-problems to traditional solvers. We design reward functions tailored for MILP, which enables the RL agent to learn relationships between decision variables and constraints. Additionally, to effectively model complex relationships among decision variables, we leverage a Transformer encoder-based graph neural network (GNN). Our experimental results demonstrate that the proposed method can solve MILP problems and find near-optimal solutions without delegating the remainder to traditional solvers. The proposed method provides a meaningful step forward as an initial study in solving MILP problems end-to-end based solely on ML.

PlanRAG: A Plan-then-Retrieval Augmented Generation for Generative Large Language Models as Decision Makers

In this paper, we conduct a study to utilize LLMs as a solution for decision making that requires complex data analysis. We define Decision QA as the task of answering the best decision, $d_{best}$, for a decision-making question $Q$, business rules $R$ and a database $D$. Since there is no benchmark that can examine Decision QA, we propose Decision QA benchmark, DQA. It has two scenarios, Locating and Building, constructed from two video games (Europa Universalis IV and Victoria 3) that have almost the same goal as Decision QA. To address Decision QA effectively, we also propose a new RAG technique called the iterative plan-then-retrieval augmented generation (PlanRAG). Our PlanRAG-based LM generates the plan for decision making as the first step, and the retriever generates the queries for data analysis as the second step. The proposed method outperforms the state-of-the-art iterative RAG method by 15.8% in the Locating scenario and by 7.4% in the Building scenario, respectively. We release our code and benchmark at https://github.com/myeon9h/PlanRAG.