The Larger the Better? Improved LLM Code-Generation via Budget Reallocation

Michael Hassid, Tal Remez, Jonas Gehring, Roy Schwartz, Yossi Adi

Research Area: Compute efficient LMs

Keywords: code-generation, budget constraint, model size

TL;DR: We compare between small and large LMs in code-generation tasks when both are given the same budget constraint; we show that smaller models often outperform large ones in this setting

Abstract: It is a common belief that large language models (LLMs) are better than smaller-sized ones. However, larger models also require significantly more time and compute during inference. This begs the question: what happens when both models operate under the same budget? (e.g., compute, run-time). To address this question, we analyze code generation LLMs of various sizes and make comparisons such as running a 70B model once vs. generating five outputs from a 13B model. We consider a standard unit-test setup, which can be used to select the correct output from the smaller model. Our findings reveal that the repeated use of smaller models can yield consistent improvements, with gains of up to 15% across five tasks. On the other hand, in scenarios where unit-tests are unavailable, a ranking-based selection of candidates from the smaller model falls short of the performance of a single output from larger ones. Our results highlight the potential of using smaller models instead of larger ones, and the importance of studying approaches for ranking LLM outputs.

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Submission Number: 217