The increasing scale and complexity of large language models (LLMs) present significant computational and memory challenges, limiting their widespread deployment. Post-training quantization (PTQ) has emerged as a key technique for mitigating these challenges without costly retraining. However, compressing models to ultra-low bitwidths (e.g., 2-3 bits) while maintaining accuracy remains a major challenge. In this study, we present a comprehensive PTQ framework that addresses this problem by compressing LLM weights through three core innovations: (1) a calibration process guided by Kullback-Leibler divergence minimization to preserve the original weight distribution, (2) a learnable codebook optimization mechanism employing noise substitution for vector quantization to enable robust gradient estimation, and (3) a layer-grouping strategy based on statistical distribution similarity to improve parameter efficiency. Experimental evaluations on large-scale models show that the proposed framework achieves competitive performance compared with state-of-the-art quantization techniques. Importantly, these results are obtained without any post-quantization fine-tuning, highlighting the efficiency and practical applicability of our approach for deploying highly compressed LLMs.

This paper describes a method of generating a reduced phoneme set for dialogue-based computer assisted language learning (CALL)systems. We designed a reduced phoneme set consisting of classified phonemes more aligned with the learners’ speech characteristics than the canonical set of a target language. This reduced phoneme set provides an inherently more appropriate model for dealing with mispronunciation by second language speakers. In this study, we used a phonetic decision tree (PDT)-based top-down sequential splitting method to generate the reduced phoneme set and then applied this method to a translation-game type English CALL system for Japanese to determine its effectiveness. Experimental results showed that the proposed method improves the performance of recognizing non-native speech.

We developed a dialogue-based tutoring system for teaching English to Japanese students and plan to transfer the current software tutoring agent into an embodied robot in the hope that the robot will enrich conversation by allowing more natural interactions in small group learning situations. To enable smooth communication between an intelligent agent and the user, the agent must have realistic models on when to take turns, when to interrupt, and how to catch the partner's attention. For developing the realistic models applicable for computer assisted language learning systems, we also need to consider the differences between the mother tongue and second language that affect communication style. We collected a multimodal corpus of multi-party conversations in English as the second language to investigate the differences in communication styles. We describe our multimodal corpus and explore features of communication style e.g. filled pauses, and non-verbal information, such as eye-gaze, which show different characteristics between the mother tongue and second language.