Leveraging Task-Adaptive Continual Pre-Training to Enhance the Classification Ability of Language Models

: pretrained domain-specific language model for recognition of Indonesian skill

A Review of Long-Context Processing Ability in Large Language Models: Technical Progress and Challenges

It is argued that the ability of long context should not only come from increasing the context window, but also from the ability of the model to locate, integrate and reason about important information in long text.

Evolution and Adaptation of Large Language Models for Bahasa Indonesia

This paper investigates the engineering methodologies of cross-lingual vocabulary adaptation, parameter initialization heuristics, and language-adaptive pre-training strategies designed to address text overfragmentation, representational misalignment, and tokenization cost inefficiencies in Bahasa Indonesia and its low-resource regional dialects.

Bangla Sentence Function Classification: Corpus Development, Model Benchmarking, and Interpretability

Automatic sentence function identification is important for many downstream natural language processing (NLP) applications such as dialogue systems, text-to-speech synthesis, and machine translation. However, benchmark resources for Bangla sentence function classification remain limited. To mitigate this gap, this paper introduces a corpus of 10,000 Bangla sentences, manually annotated into four functional categories, namely declarative, interrogative, imperative, and exclamatory. The corpus is nearly balanced across the four classes, with high annotation reliability reflected by a Fleiss\'Kappa of 0.82. Furthermore, we evaluate multiple feature representations, including Bag-of-Words (BoW), TF-IDF, and Word2Vec, with several classical machine learning classifiers. In addition, two heterogeneous ensemble models, namely Single-Level Ensemble (SLE) and Double-Level Ensemble (DLE), are utilized to improve classification performance. Experimental results show that TF-IDF consistently outperforms Word2Vec, likely due to its ability to emphasize discriminative lexical cues associated with sentence functions, particularly given the relatively small corpus used to train Word2Vec. The DLE model with TF-IDF features achieves the best performance with accuracy and macro-F1 of 0.95, demonstrating the effectiveness of sparse lexical representations and heterogeneous ensemble learning for this task. Further cross-validation confirms the robustness of the approach, while LIME-based interpretability provides insights into model predictions. The developed corpus and model benchmarking establish strong baselines for Bangla sentence function classification.

Applications of Natural Language Processing: A Comprehensive Study

A comprehensive review of the evolution of NLP from traditional rule-based approaches to modern transformer models including BERT and GPT demonstrates that NLP continues to transform intelligent systems and is expected to play an increasingly significant role in the development of next-generation AI technologies.

Instructing the Learning of Language Model with the Token Interpretation to Improve Language Understanding

Pretrained language models (PLMs) have established state-of-the-art performance across diverse natural language understanding (NLU) tasks. This study reveals that seman-tic-rich explanations of lexical units can effectively guide PLM learning processes. We propose a novel language understanding enhancement method with token interpretation (LUETI) that addresses two critical limitations in conventional PLMs: Incomplete token semantics caused by isolated contextual learning and insufficient semantic encoding in embedding matrices. LUETI operates through dual mechanisms, augmenting token represen-tations by integrating hidden states with corresponding token interpretations and refining embedding spaces using interpretation-derived semantic vectors for token prediction. LUETI, which is implemented as a plug-in module for standard architectures, demonstrates significant improvements on BERT and GLM, achieving average performance gains of 3.36% and 4.87% respectively on the SuperGLUE benchmark with equivalent parameters and training data. Note that LUETI-equipped models attain comparable performance to baseline PLMs using only 60% of pretraining data. Findings establish token interpretation as a computationally efficient but semantically powerful enhancement strategy for language model pretraining.