L2CEval: Evaluating Language-to-Code Generation Capabilities of Large Language Models

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Written by Ansong Ni, Pengcheng Yin, Yilun Zhao, Martin Riddell, Troy Feng, Rui Shen, Stephen Yin, Ye Liu, Semih Yavuz, Caiming Xiong, Shafiq Joty, Yingbo Zhou, Dragomir Radev, Arman Cohan

Category: “AI for IT”

Article Section: Code generation with AI

Publication Date: 2023-10-02

SEO Description: “L2CEval aims to comprehensively evaluate the capabilities and limitations of Large Language Models for language-to-code generation across various tasks.”

Claude.ai-Generated Paper Summary

Here are some key points about the novelty and commercial applications of this paper:

Novelty:

• Presents a new benchmark, L2CEval, for systematically evaluating language-to-code generation capabilities of large language models (LLMs).
• Comprehensively analyzes 54 LLMs ranging from 1B to 170B+ parameters on 7 diverse language-to-code tasks spanning semantic parsing, math reasoning, and Python programming.
• Provides insights into how factors like model size, training data, prompting methods, and instruction tuning affect LLM performance on language-to-code tasks.
• Analyzes model robustness to prompts and confidence calibration in addition to accuracy. Also identifies common error modes via human evaluation.
• Releases dataset of model outputs on all tasks to facilitate future research.

Commercial Applications:

• The benchmark and analysis provide guidance for companies to select the right LLM for their language-to-code application needs based on model size, training data etc.
• The identified strengths and weaknesses of current LLMs can inform development of commercial services and products for code generation.
• Companies building new LLMs for programming applications can use the benchmark and findings to test and improve their models.
• Startups offering AI coding assistants can license pretrained LLMs evaluated in the paper that match their application requirements.
• The dataset of model outputs can help train production systems to generate better code from natural language specifications.
• Insights on prompt engineering can help create robust commercial applications using LLMs for code generation.
• Analysis of model calibration can guide techniques to improve reliability in deployed coding tools relying on LLMs.

In summary, the comprehensive benchmark and analysis provides both novel insights to advance research as well as practical guidance for developing commercial language-to-code applications using current LLMs.

Keywords

Evaluating Language-to-Code Generation, Large Language Models, L2CEval, Semantic Parsing, Python Programming

Author’s Abstract

Recently, large language models (LLMs), especially those that are pretrained on code, have demonstrated strong capabilities in generating programs from natural language inputs in a few-shot or even zero-shot manner. Despite promising results, there is a notable lack of a comprehensive evaluation of these models language-to-code generation capabilities. Existing studies often focus on specific tasks, model architectures, or learning paradigms, leading to a fragmented understanding of the overall landscape. In this work, we present L2CEval, a systematic evaluation of the language-to-code generation capabilities of LLMs on 7 tasks across the domain spectrum of semantic parsing, math reasoning and Python programming, analyzing the factors that potentially affect their performance, such as model size, pretraining data, instruction tuning, and different prompting methods. In addition to assessing model performance, we measure confidence calibration for the models and conduct human evaluations of the output programs. This enables us to identify and analyze the typical failure modes across various tasks and models. L2CEval offers a comprehensive understanding of the capabilities and limitations of LLMs in language-to-code generation. We also release the evaluation framework and all model outputs, hoping to lay the groundwork for further future research in this domain.

Read the full paper here