Code Efficiency

Code efficiency, focusing on generating computationally inexpensive and fast-running code, is a burgeoning research area within large language model (LLM) development. Current research emphasizes benchmarking LLM-generated code against human-written solutions, exploring the impact of pre-training data (including code), and developing novel evaluation metrics that go beyond simple correctness to encompass runtime performance. These efforts aim to improve the efficiency of LLMs themselves and the code they produce, ultimately impacting software development speed, resource consumption, and the broader sustainability of AI applications.