The recent success of large language models (LLMs) trained on static, pre-collected,
general datasets has sparked numerous research directions and applications. One such …

Recent advances in large language models (LLMs) have demonstrated notable progress on
many mathematical benchmarks. However, most of these benchmarks only feature problems …

Recent methodologies in LLM self-training mostly rely on LLM generating responses and
filtering those with correct output answers as training data. This approach often yields a low …

Large Language Models (LLMs) have emerged as a transformative power in enhancing
natural language comprehension, representing a significant stride toward artificial general …

This survey presents an in-depth exploration of knowledge distillation (KD) techniques
within the realm of Large Language Models (LLMs), spotlighting the pivotal role of KD in …

This survey article delves into the emerging and critical area of symbolic knowledge
distillation in large language models (LLMs). As LLMs such as generative pretrained …

Scientific reasoning poses an excessive challenge for even the most advanced Large
Language Models (LLMs). To make this task more practical and solvable for LLMs, we …

Abstract Large Language Models (LLMs) have demonstrated exceptional abilities in
comprehending and generating text, motivating numerous researchers to utilize them for …

Scientific literature understanding is crucial for extracting targeted information and garnering
insights, thereby significantly advancing scientific discovery. Despite the remarkable …

Recent advancements in Large Multi-modal Models (LMMs) underscore the importance of
scaling by increasing image-text paired data, achieving impressive performance on general …