Evaluating In-Context Learning of Libraries for Code Generation

• URL: http://arxiv.org/abs/2311.09635v2
• Date: Thu, 4 Apr 2024 18:54:01 GMT
• Title: Evaluating In-Context Learning of Libraries for Code Generation
• Authors: Arkil Patel, Siva Reddy, Dzmitry Bahdanau, Pradeep Dasigi,
• Abstract summary: Large Language Models (LLMs) exhibit a high degree of code generation and comprehension capability. Recent work has shown that large proprietary LLMs can learn novel library usage in-context from demonstrations.
• Score: 35.57902679044737
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Contemporary Large Language Models (LLMs) exhibit a high degree of code generation and comprehension capability. A particularly promising area is their ability to interpret code modules from unfamiliar libraries for solving user-instructed tasks. Recent work has shown that large proprietary LLMs can learn novel library usage in-context from demonstrations. These results raise several open questions: whether demonstrations of library usage is required, whether smaller (and more open) models also possess such capabilities, etc. In this work, we take a broader approach by systematically evaluating a diverse array of LLMs across three scenarios reflecting varying levels of domain specialization to understand their abilities and limitations in generating code based on libraries defined in-context. Our results show that even smaller open-source LLMs like Llama-2 and StarCoder demonstrate an adept understanding of novel code libraries based on specification presented in-context. Our findings further reveal that LLMs exhibit a surprisingly high proficiency in learning novel library modules even when provided with just natural language descriptions or raw code implementations of the functions, which are often cheaper to obtain than demonstrations. Overall, our results pave the way for harnessing LLMs in more adaptable and dynamic coding environments.

Related papers

• Codellm-Devkit: A Framework for Contextualizing Code LLMs with Program Analysis Insights [9.414198519543564]
  We present codellm-devkit (hereafter, CLDK'), an open-source library that significantly simplifies the process of performing program analysis. CLDK offers developers an intuitive and user-friendly interface, making it incredibly easy to provide rich program analysis context to code LLMs.
  arXiv  Detail & Related papers  (2024-10-16T20:05:59Z)
• LLMBox: A Comprehensive Library for Large Language Models [109.15654830320553]
  This paper presents a comprehensive and unified library, LLMBox, to ease the development, use, and evaluation of large language models (LLMs) This library is featured with three main merits: (1) a unified data interface that supports the flexible implementation of various training strategies, (2) a comprehensive evaluation that covers extensive tasks, datasets, and models, and (3) more practical consideration, especially on user-friendliness and efficiency.
  arXiv  Detail & Related papers  (2024-07-08T02:39:33Z)
• Perplexed: Understanding When Large Language Models are Confused [3.4208414448496027]
  This paper introduces perplexed, a library for exploring where a language model is perplexed. We conducted a case study focused on Large Language Models (LLMs) for code generation using an additional tool we built to help with the analysis of code models called codetokenizer. We found that our studied code LLMs had their worst performance on coding structures where the code was not syntactically correct.
  arXiv  Detail & Related papers  (2024-04-09T22:03:39Z)
• Rethinking Interpretability in the Era of Large Language Models [76.1947554386879]
  Large language models (LLMs) have demonstrated remarkable capabilities across a wide array of tasks. The capability to explain in natural language allows LLMs to expand the scale and complexity of patterns that can be given to a human. These new capabilities raise new challenges, such as hallucinated explanations and immense computational costs.
  arXiv  Detail & Related papers  (2024-01-30T17:38:54Z)
• If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents [81.60906807941188]
  Large language models (LLMs) are trained on a combination of natural language and formal language (code) Code translates high-level goals into executable steps, featuring standard syntax, logical consistency, abstraction, and modularity.
  arXiv  Detail & Related papers  (2024-01-01T16:51:20Z)
• Video Understanding with Large Language Models: A Survey [97.29126722004949]
  Given the remarkable capabilities of large language models (LLMs) in language and multimodal tasks, this survey provides a detailed overview of recent advancements in video understanding. The emergent capabilities Vid-LLMs are surprisingly advanced, particularly their ability for open-ended multi-granularity reasoning. This survey presents a comprehensive study of the tasks, datasets, benchmarks, and evaluation methodologies for Vid-LLMs.
  arXiv  Detail & Related papers  (2023-12-29T01:56:17Z)
• Supervised Knowledge Makes Large Language Models Better In-context Learners [94.89301696512776]
  Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering. The challenge of improving the generalizability and factuality of LLMs in natural language understanding and question answering remains under-explored. We propose a framework that enhances the reliability of LLMs as it: 1) generalizes out-of-distribution data, 2) elucidates how LLMs benefit from discriminative models, and 3) minimizes hallucinations in generative tasks.
  arXiv  Detail & Related papers  (2023-12-26T07:24:46Z)
• The potential of LLMs for coding with low-resource and domain-specific programming languages [0.0]
  This study focuses on the econometric scripting language named hansl of the open-source software gretl. Our findings suggest that LLMs can be a useful tool for writing, understanding, improving, and documenting gretl code.
  arXiv  Detail & Related papers  (2023-07-24T17:17:13Z)
• Large Language Models are Few-Shot Summarizers: Multi-Intent Comment Generation via In-Context Learning [34.006227676170504]
  This study investigates the feasibility of utilizing large language models (LLMs) to generate comments that can fulfill developers' diverse intents. Experiments on two large-scale datasets demonstrate the rationale of our insights.
  arXiv  Detail & Related papers  (2023-04-22T12:26:24Z)
• Low-code LLM: Graphical User Interface over Large Language Models [115.08718239772107]
  This paper introduces a novel human-LLM interaction framework, Low-code LLM. It incorporates six types of simple low-code visual programming interactions to achieve more controllable and stable responses. We highlight three advantages of the low-code LLM: user-friendly interaction, controllable generation, and wide applicability.
  arXiv  Detail & Related papers  (2023-04-17T09:27:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.