Enhancing LLM Capabilities Beyond Scaling Up

General-purpose large language models (LLMs) are progressively expanding both in scale and access to unpublic training data. This has led to notable progress in a variety of AI problems. Nevertheless, two questions exist: i) Is scaling up the sole avenue of extending the capabilities of LLMs? ii) Instead of developing general-purpose LLMs, how to endow LLMs with specific knowledge? This tutorial targets researchers and practitioners who are interested in capability extension of LLMs that go beyond scaling up. To this end, we will discuss several lines of research that follow that direction, including: (i) optimizing input prompts to fully exploit LLM potential, (ii) enabling LLMs to self-improve responses through various feedback signals, (iii) updating or editing the internal knowledge of LLMs when necessary, (iv) leveraging incidental structural supervision from target tasks, and (v) defending against potential attacks and threats from malicious users. At last, we will conclude the tutorial by outlining directions for further investigation.