When LLMs Lag Behind: Knowledge Conflicts from Evolving APIs in Code Generation

• URL: http://arxiv.org/abs/2604.09515v1
• Date: Fri, 10 Apr 2026 17:37:26 GMT
• Title: When LLMs Lag Behind: Knowledge Conflicts from Evolving APIs in Code Generation
• Abstract summary: "context-memory conflict" arises when external instructions contradict a model's internal parametric knowledge.<n>This paper presents a benchmark of 270 real-world updates from eight Python libraries.
• Score: 12.67668690329239
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid evolution of software libraries creates a significant challenge for Large Language Models (LLMs), whose static parametric knowledge often becomes stale post-training. While retrieval-augmented generation (RAG) is commonly used to provide up-to-date API specifications, "context-memory conflict" arises when external instructions contradict a model's internal parametric knowledge. This paper presents a systematic empirical study of LLM code generation under API evolution (e.g., API deprecation, API modification, and API addition), by constructing a benchmark of 270 real-world updates from eight Python libraries. We evaluate four LLM families of 11 models. Our results show that without comprehensive documentation, LLMs struggle to prioritize external context, averaging only 42.55% of generated code examples are executable in the target environment. While structured documentation and larger model scales improve LLMs' ability to update adoption, they do not fully resolve executability issues with a low 66.36% executable rate. In addition, reasoning-based strategies (e.g., Self-Reflection) significantly boost LLMs' performance with 11% improvement on executable rate. Our findings highlight the persistence of outdated patterns from LLMs, even when API update specifications are provided, and emphasize the need for evolution-aware benchmarks and techniques.

Related papers

• RealBench: A Repo-Level Code Generation Benchmark Aligned with Real-World Software Development Practices [54.956760584923295]
  Researchers have made substantial progress using Large Language Models (LLMs) for code generation.<n>However, developers typically write code based on structured designs or specifications rather than raw natural language descriptions.<n>This gap between existing benchmarks and real industry development practices means that current benchmark scores may not accurately reflect how much code generation can help automate development tasks.
  arXiv  Detail & Related papers  (2026-04-24T15:35:54Z)
• Framework-Aware Code Generation with API Knowledge Graph-Constructed Data: A Study on HarmonyOS [52.483888557864326]
  APIKG4SYN is a framework designed to exploit API knowledge graphs for the construction of API-oriented question-code pairs.<n>We build the first benchmark for HarmonyOS code generation using APIKG4SYN.
  arXiv  Detail & Related papers  (2025-11-29T08:13:54Z)
• When LLMs Meet API Documentation: Can Retrieval Augmentation Aid Code Generation Just as It Helps Developers? [10.204379646375182]
  Retrieval-augmented generation (RAG) has increasingly shown its power in extending large language models' (LLMs') capability beyond their pre-trained knowledge.<n>We study the factors that affect the effectiveness of using the documentation of less common API libraries as additional knowledge for retrieval and generation.
  arXiv  Detail & Related papers  (2025-03-19T14:08:47Z)
• Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language Models [79.41139393080736]
  Large language models (LLMs) have rapidly advanced and demonstrated impressive capabilities. In-Context Learning (ICL) and. Efficient Fine-Tuning (PEFT) are currently two mainstream methods for augmenting. LLMs to downstream tasks. We propose Reference Trustable Decoding (RTD), a paradigm that allows models to quickly adapt to new tasks without fine-tuning.
  arXiv  Detail & Related papers  (2024-09-30T10:48:20Z)
• APILOT: Navigating Large Language Models to Generate Secure Code by Sidestepping Outdated API Pitfalls [15.865915079829943]
  APILOT maintains a realtime, quickly updatable dataset of outdated APIs. It uses an augmented generation method to navigate LLMs in generating secure, version-aware code. It can reduce outdated code recommendations by 89.42% on average with limited performance overhead.
  arXiv  Detail & Related papers  (2024-09-25T00:37:40Z)
• CodeUpdateArena: Benchmarking Knowledge Editing on API Updates [77.81663273436375]
  We present CodeUpdateArena, a benchmark for knowledge editing in the code domain.<n>An instance in our benchmark consists of a synthetic API function update paired with a program synthesis example.<n>Our benchmark covers updates of various types to 54 functions from seven diverse Python packages.
  arXiv  Detail & Related papers  (2024-07-08T17:55:04Z)
• Applying RLAIF for Code Generation with API-usage in Lightweight LLMs [15.366324461797582]
  Reinforcement Learning from AI Feedback (RLAIF) has demonstrated significant potential across various domains. This paper introduces an RLAIF framework for improving the code generation abilities of lightweight (1B parameters) LLMs.
  arXiv  Detail & Related papers  (2024-06-28T17:16:03Z)
• DARG: Dynamic Evaluation of Large Language Models via Adaptive Reasoning Graph [70.79413606968814]
  We introduce Dynamic Evaluation of LLMs via Adaptive Reasoning Graph Evolvement (DARG) to dynamically extend current benchmarks with controlled complexity and diversity. Specifically, we first extract the reasoning graphs of data points in current benchmarks and then perturb the reasoning graphs to generate novel testing data. Such newly generated test samples can have different levels of complexity while maintaining linguistic diversity similar to the original benchmarks.
  arXiv  Detail & Related papers  (2024-06-25T04:27:53Z)
• Octopus: On-device language model for function calling of software APIs [9.78611123915888]
  Large Language Models (LLMs) play a crucial role due to their advanced text processing and generation abilities. This study introduces a new strategy aimed at harnessing on-device LLMs in invoking software APIs.
  arXiv  Detail & Related papers  (2024-04-02T01:29:28Z)
• (Why) Is My Prompt Getting Worse? Rethinking Regression Testing for Evolving LLM APIs [8.403074015356594]
  Large Language Models (LLMs) are increasingly integrated into software applications. LLMs are often updated silently and scheduled to be deprecated. This can cause performance regression and affect prompt design choices.
  arXiv  Detail & Related papers  (2023-11-18T17:11:12Z)
• ML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Code [76.84199699772903]
  ML-Bench is a benchmark rooted in real-world programming applications that leverage existing code repositories to perform tasks. To evaluate both Large Language Models (LLMs) and AI agents, two setups are employed: ML-LLM-Bench for assessing LLMs' text-to-code conversion within a predefined deployment environment, and ML-Agent-Bench for testing autonomous agents in an end-to-end task execution within a Linux sandbox environment.
  arXiv  Detail & Related papers  (2023-11-16T12:03:21Z)

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.