Junseo Koh

Juhyeon Lee, Wonduk Seo, Junseo Koh et al.

Detecting harmful content in multi turn dialogue requires reasoning over the full conversational context rather than isolated utterances. However, most existing methods rely mainly on models internal parametric knowledge, without explicit grounding in external normative principles. This often leads to inconsistent judgments in socially nuanced contexts, limited interpretability, and redundant reasoning across turns. To address this, we propose RoTRAG, a retrieval augmented framework that incorporates concise human written moral norms, called Rules of Thumb (RoTs), into LLM based harm assessment. For each turn, RoTRAG retrieves relevant RoTs from an external corpus and uses them as explicit normative evidence for turn level reasoning and final severity classification. To improve efficiency, we further introduce a lightweight binary routing classifier that decides whether a new turn requires retrieval grounded reasoning or can reuse existing context. Experiments on ProsocialDialog and Safety Reasoning Multi Turn Dialogue show that RoTRAG consistently improves both harm classification and severity estimation over competitive baselines, with an average relative gain of around 40% in F1 across benchmark datasets and an average relative reduction of 8.4% in distributional error, while reducing redundant computation without sacrificing performance.

Wonduk Seo, Wonseok Choi, Junseo Koh et al.

Large Language Models (LLMs) increasingly support culturally sensitive decision making, yet often exhibit misalignment due to skewed pretraining data and the absence of structured value representations. Existing methods can steer outputs, but often lack demographic grounding and treat values as independent, unstructured signals, reducing consistency and interpretability. We propose OG-MAR, an Ontology-Guided Multi-Agent Reasoning framework. OG-MAR summarizes respondent-specific values from the World Values Survey (WVS) and constructs a global cultural ontology by eliciting relations over a fixed taxonomy via competency questions. At inference time, it retrieves ontology-consistent relations and demographically similar profiles to instantiate multiple value-persona agents, whose outputs are synthesized by a judgment agent that enforces ontology consistency and demographic proximity. Experiments on regional social-survey benchmarks across four LLM backbones show that OG-MAR improves cultural alignment and robustness over competitive baselines, while producing more transparent reasoning traces.

Wonduk Seo, Juhyeon Lee, Junseo Koh et al.

Prompt optimization has emerged as an effective alternative to retraining for improving the performance of Large Language Models (LLMs). However, most existing approaches treat evaluation as a black box, relying solely on numerical scores while offering limited insight into why a prompt succeeds or fails. They also depend heavily on trial-and-error refinements, which are difficult to interpret and control. In this paper, we introduce MA-SAPO, a Multi-Agent framework for Score-Aware Prompt Optimization. Compared to prior methods, MA-SAPO explicitly couples evaluation outcomes with structured reasoning to guide systematic edits. The framework specifically consists of two stages: during the Reasoning Phase, agents collaboratively explain metric scores, diagnose weaknesses, and synthesize targeted refinements that are stored as reusable reasoning assets; during the Test Phase, agents retrieve these assets to analyze optimized prompts and apply only evidence-grounded edits. By turning evaluation signals into interpretable reasoning chains, MA-SAPO produces prompt refinements that are more transparent, auditable, and controllable. Experiments on the HelpSteer1/2 benchmarks demonstrate consistent improvements over single-pass prompting, retrieval-augmented baselines, and prior multi-agent strategies, validating the effectiveness of our approach.