Given a query, how can a QA system ask an informative clarifying question to accurately answer it? Open-domain conversational question answering requires not only detecting when a query is ambiguous but also asking clarifying questions that efficiently uncover the latent intent of the user. However, existing approaches either rely on prompt-only clarify-or-answer decisions with limited robustness or distill clarification trajectories without explicitly modeling intent convergence. In this paper, we propose D4CQ (Data-Efficient Diagnosticity based Dynamic Distillation for Clarifying Question), a data-efficient framework for learning diagnostic clarifying questions through dynamic distillation. Our approach distills a structured refinement protocol, prioritizes informative clarifications via diagnosticity-based reweighting, and improves sample efficiency through hindsight relabeling. Empirical results demonstrates that D4CQ consistently outperforms strong baselines, while maintaining comparable interaction budgets. Our code is available at https://github.com/ddddcq/d4cq.

While large language models (LLMs) have demonstrated significant capabilities in text generation, their utilization in areas requiring domain-specific expertise, such as law, must be approached cautiously. This caution is warranted due to the inherent challenges associated with LLM-generated texts, including the potential presence of factual errors. Motivated by this issue, we propose Eval-RAG, a new evaluation method for LLM-generated texts. Unlike existing methods, Eval-RAG evaluates the validity of generated texts based on the related document that are collected by the retriever. In other words, Eval-RAG adopts the idea of retrieval augmented generation (RAG) for the purpose of evaluation. Our experimental results on Korean Legal Question-Answering (QA) tasks show that conventional LLM-based evaluation methods can be better aligned with Lawyers’ evaluations, by combining with Eval-RAG. In addition, our qualitative analysis show that Eval-RAG successfully finds the factual errors in LLM-generated texts, while existing evaluation methods cannot.

This paper addresses the workflow for terminology construction for Korean-English patent MT system. The workflow consists of the stage for setting lexical goals and the semi- automatic terminology construction stage. As there is no comparable system, it is difficult to determine how many terms are needed. To estimate the number of the needed terms, we analyzed 45,000 patent documents. Given the limited time and budget, we resorted to the semi-automatic methods to create the bilingual term dictionary in electronics domain. We will show that parenthesis information in Korean patent documents and bilingual title corpus can be successfully used to build a bilingual term dictionary.

This paper addresses a customization process of a Korean-English MT system for patent translation. The major customization steps include terminology construction, linguistic study, and the modification of the existing analysis and generation-module. T o our knowledge, this is the first worth-mentioning large-scale customization effort of an MT system for Korean and English. This research was performed under the auspices of the MIC (Ministry of Information and Communication) of Korean government. A prototype patent MT system for electronics domain was installed and is being tested in the Korean Intellectual Property Office.