The field of natural language processing is moving towards improving the robustness and versatility of large language models (LLMs). Recent research has focused on enhancing the tokenization process, exploring the use of entropy-driven pre-tokenization strategies to better handle unsegmented languages. Additionally, there is a growing interest in advancing mathematical reasoning capabilities of LLMs, with proposals for self-adaptive solutions that promote diverse strategic outcomes and error feedback mechanisms. The development of novel frameworks for efficient vision-language models and the distillation of tool knowledge into LLMs through natural language are also noteworthy trends. Furthermore, the application of automatic prompt optimization techniques has shown promise in improving the performance of LLMs in tasks such as knowledge graph construction. Notable papers include:

• Entropy-Driven Pre-Tokenization for Byte-Pair Encoding, which demonstrates substantial improvements in segmentation precision, recall, and F1 score.
• Towards Advanced Mathematical Reasoning for LLMs via First-Order Logic Theorem Proving, which proposes a self-adaptive solution that enhances the diversity and reasonability of LLMs' generation strategies.
• Distilling Tool Knowledge into Language Models via Back-Translated Traces, which presents a new paradigm for distilling tool knowledge into LLMs purely through natural language.