This study proposes a novel coding framework for analyzing science classroom discourse using large language models (LLMs), adopting fine-grained utterance-level chunking aligned with the analytical units of LLMs to address limitations of global, lesson-level observation tools. Authentic middle school science classroom discourse was annotated through a dual-expertise and iterative process integrating the theoretical knowledge of science education faculty with the experiential insights of in-service teachers, supported by systematic rater training to ensure conceptual alignment and interpretive consistency at the utterance level. Through this process, a science education glossary comprising 137 instructional terms organized into 20 thematic categories was developed using a primarily bottom-up approach informed by established observation frameworks. Building on this theory-informed foundation, we systematically examined LLM-based methods for predicting instructional themes and quality, comparing structured prompting strategies with domain-adaptive fine-tuning across model architectures. These contributions lay a foundation for future research on interpretable, scalable, and pedagogically meaningful automated formative feedback to support teachers’ self-reflection and professional growth.

Idioms have long posed a challenge due to their unique linguistic properties, which set them apart from other common expressions.While recent studies have leveraged large language models (LLMs) to handle idioms across various tasks, e.g., idiom-containing sentence generation and idiomatic machine translation, little is known about the underlying mechanisms of idiom processing in LLMs, particularly in multilingual settings.To this end, we introduce MI-DAS, a new large-scale dataset of idioms in six languages, each paired with its corresponding meaning.Leveraging this resource, we conduct a comprehensive evaluation of LLMs' idiom processing ability, identifying key factors that influence their performance.Our findings suggest that LLMs rely not only on memorization but also adopt a hybrid approach that integrates contextual cues and reasoning, especially when processing compositional idioms.This implies that idiom understanding in LLMs emerges from an interplay between internal knowledge retrieval and reasoning-based inference.

Large language models often retain unintended content, prompting growing interest in knowledge unlearning.Recent approaches emphasize localized unlearning, restricting parameter updates to specific regions in an effort to remove target knowledge while preserving unrelated general knowledge.However, their effectiveness remains uncertain due to the lack of robust and thorough evaluation of the tradeoff between the competing goals of unlearning.In this paper, we begin by revisiting existing localized unlearning approaches.We then conduct controlled experiments to rigorously evaluate whether local parameter updates causally contribute to unlearning.Our findings reveal that the set of parameters that must be modified for effective unlearning is not strictly determined, challenging the core assumption of localized unlearning that parameter locality is inherently indicative of effective knowledge removal.We release our code at https: //github.com/HYU-NLP/loc-unlearn