Where Do Large Language Models Fail When Generating Code?

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Uncovering Weaknesses in Neural Code Generation

X Lian, S Wang, J Ma, F Liu, X Tan, L Zhang… - arxiv preprint arxiv …, 2024 - arxiv.org
Code generation, the task of producing source code from prompts, has seen significant
advancements with the advent of pre-trained large language models (PLMs). Despite these …
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BioAgents: Democratizing Bioinformatics Analysis with Multi-Agent Systems

N Mehandru, AK Hall, O Melnichenko… - arxiv preprint arxiv …, 2025 - arxiv.org
Creating end-to-end bioinformatics workflows requires diverse domain expertise, which
poses challenges for both junior and senior researchers as it demands a deep …
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Advancing Bug Detection in Fastjson2 with Large Language Models Driven Unit Test Generation

Z Zhong, S Wang, H Wang, S Wen, H Guan… - arxiv preprint arxiv …, 2024 - arxiv.org
Data-serialization libraries are essential tools in software development, responsible for
converting between programmable data structures and data persistence formats. Among …
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CodeHalu: Investigating Code Hallucinations in LLMs via Execution-based Verification

Y Tian, W Yan, Q Yang, X Zhao, Q Chen… - arxiv preprint arxiv …, 2024 - arxiv.org
Large Language Models (LLMs) have made significant progress in code generation, offering
developers groundbreaking automated programming support. However, LLMs often …
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