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

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 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 …

In the era of Large Language Models (LLMs), Knowledge Distillation (KD) emerges as a
pivotal methodology for transferring advanced capabilities from leading proprietary LLMs …

Most of the existing Large Language Model (LLM) benchmarks on scientific problem
reasoning focus on problems grounded in high-school subjects and are confined to …

The integration of biomolecular modeling with natural language (BL) has emerged as a
promising interdisciplinary area at the intersection of artificial intelligence, chemistry and …

In many scientific fields, large language models (LLMs) have revolutionized the way text and
other modalities of data (eg, molecules and proteins) are handled, achieving superior …

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

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 …

Large language models (LLM) have demonstrated emergent abilities in text generation,
question answering, and reasoning, facilitating various tasks and domains. Despite their …