arXiv subjects

Zhongqi Li

Publications and source records attributed to Zhongqi Li.

6 recordsLinked to original sources

arXiv2026

Beyond Functional Correctness: Exploring Hallucinations in LLM-Generated Code

The rise of Large Language Models (LLMs) has significantly advanced various applications on software engineering tasks, particularly in code generation. Despite the promising performance, LLMs are prone to generate hallucinations, which means LLMs might produce outputs that deviate from users' intent, exhibit internal inconsistencies, or misaligned with the real-world knowledge, making the deployment of LLMs potentially risky in a wide range of applications. Existing work mainly focuses on investigating the hallucination in the domain of Natural Language Generation (NLG), leaving a gap in comprehensively understanding the types, causes, and impacts of hallucinations in the context of code generation. To bridge the gap, we conducted a thematic analysis of the LLM-generated code to summarize and categorize the hallucinations, as well as their causes and impacts. Our study established a comprehensive taxonomy of code hallucinations, encompassing 3 primary categories and 12 specific categories. Furthermore, we systematically analyzed the distribution of hallucinations, exploring variations among different LLMs and benchmarks. Moreover, we perform an in-depth analysis on the causes and impacts of various hallucinations, aiming to provide valuable insights into hallucination mitigation. Finally, to enhance the correctness and reliability of LLM-generated code in a lightweight manner, we explore training-free hallucination mitigation approaches by prompt enhancing techniques. We believe our findings will shed light on future research about code hallucination evaluation and mitigation, ultimately paving the way for building more effective and reliable code LLMs in the future. The replication package is available at https://github.com/Lorien1128/code_hallucination

cs.SE↗

arXiv2025

Transcranial Photoacoustic Imaging for Human Intracranial Pressure Evaluation

Photoacoustic imaging (PAI), by combining high optical contrast with ultrasonic resolution, offers a promising noninvasive approach for dynamic monitoring of cerebral vasculature. However, transcranial PAI still faces significant challenges due to strong attenuation of both optical and acoustic signals by the skull. In this study, we propose a multi-wavelength photoacoustic tomography system and method for intracranial pressure (ICP) assessment, enabling visualization of cross-sectional structures of the middle cerebral artery (MCA) through the human temporal bone. By utilizing multi-wavelength excitation in the near-infrared-I (NIR-I) window, quantitative maps of blood oxygen saturation ($\mathbf{sO_2}$) are reconstructed, and the relationship between oxygenation dynamics and ICP variations is established. Experimental results demonstrate that the proposed system can successfully capture dynamic $\mathbf{sO_2}$ fluctuations in the MCA despite skull attenuation, revealing its characteristic responses to ICP changes. This work provides a high-precision, noninvasive imaging tool for early stroke diagnosis, cerebral vascular function assessment, and neurointerventional guidance, highlighting the clinical translational potential of PAI in neuroscience.

physics.med-ph↗

arXiv2025

Smaller but Better: Self-Paced Knowledge Distillation for Lightweight yet Effective LCMs

Large code models (LCMs) have remarkably advanced the field of code generation. Despite their impressive capabilities, they still face practical deployment issues, such as high inference costs, limited accessibility of proprietary LCMs, and adaptability issues of ultra-large LCMs. These issues highlight the critical need for more accessible, lightweight yet effective LCMs. Knowledge distillation (KD) offers a promising solution, which transfers the programming capabilities of larger, advanced LCMs to smaller, less powerful LCMs. In this paper, we propose a novel Self-Paced knOwledge DistillAtion framework, named SODA, aiming at developing lightweight yet effective student LCMs. SODA consists of three stages in one cycle: (1) Correct-and-Fault Knowledge Delivery stage aims at improving the student models capability to recognize errors while ensuring its basic programming skill during the knowledge transferring, which involves correctness-aware supervised learning and fault-aware contrastive learning methods. (2) Multi-View Feedback stage aims at measuring the quality of results generated by the student model from two views, including model-based and static tool-based measurement, for identifying the difficult questions. (3) Feedback-based Knowledge Update stage aims at updating the student model adaptively by generating new questions at different difficulty levels, in which the difficulty levels are categorized based on the feedback in the second stage. Experimental results show that SODA improves the student model by 65.96% in terms of average Pass@1, outperforming the best baseline by 29.85%. Based on the SODA framework, we develop SodaCoder, a series of lightweight yet effective LCMs, which outperform 15 LCMs with less than or equal to 16B parameters. Notably, SodaCoder-DS-6.7B, built on DeepseekCoder-6.7B, even surpasses the prominent ChatGPT on average Pass@1.

cs.SE↗

arXiv2025

Towards Mitigating API Hallucination in Code Generated by LLMs with Hierarchical Dependency Aware

Application Programming Interfaces (APIs) are crucial in modern software development. Large Language Models (LLMs) assist in automated code generation but often struggle with API hallucination, including invoking non-existent APIs and misusing existing ones in practical development scenarios. Existing studies resort to Retrieval-Augmented Generation (RAG) methods for mitigating the hallucination issue, but tend to fail since they generally ignore the structural dependencies in practical projects and do not indeed validate whether the generated APIs are available or not. To address these limitations, we propose MARIN, a framework for mitigating API hallucination in code generated by LLMs with hierarchical dependency aware. MARIN consists of two phases: Hierarchical Dependency Mining, which analyzes local and global dependencies of the current function, aiming to supplement comprehensive project context in LLMs input, and Dependency Constrained Decoding, which utilizes mined dependencies to adaptively constrain the generation process, aiming to ensure the generated APIs align with the projects specifications. To facilitate the evaluation of the degree of API hallucination, we introduce a new benchmark APIHulBench and two new metrics including Micro Hallucination Number (MiHN) and Macro Hallucination Rate (MaHR). Experiments on six state-of-the-art LLMs demonstrate that MARIN effectively reduces API hallucinations, achieving an average decrease of 67.52% in MiHN and 73.56% in MaHR compared to the RAG approach. Applied to Huaweis internal projects and two proprietary LLMs, MARIN achieves average decreases of 57.33% in MiHN and 59.41% in MaHR.

cs.SE↗

arXiv2024

FastFixer: An Efficient and Effective Approach for Repairing Programming Assignments

Providing personalized and timely feedback for student's programming assignments is useful for programming education. Automated program repair (APR) techniques have been used to fix the bugs in programming assignments, where the Large Language Models (LLMs) based approaches have shown promising results. Given the growing complexity of identifying and fixing bugs in advanced programming assignments, current fine-tuning strategies for APR are inadequate in guiding the LLM to identify bugs and make accurate edits during the generative repair process. Furthermore, the autoregressive decoding approach employed by the LLM could potentially impede the efficiency of the repair, thereby hindering the ability to provide timely feedback. To tackle these challenges, we propose FastFixer, an efficient and effective approach for programming assignment repair. To assist the LLM in accurately identifying and repairing bugs, we first propose a novel repair-oriented fine-tuning strategy, aiming to enhance the LLM's attention towards learning how to generate the necessary patch and its associated context. Furthermore, to speed up the patch generation, we propose an inference acceleration approach that is specifically tailored for the program repair task. The evaluation results demonstrate that FastFixer obtains an overall improvement of 20.46% in assignment fixing when compared to the state-of-the-art baseline. Considering the repair efficiency, FastFixer achieves a remarkable inference speedup of 16.67 times compared to the autoregressive decoding algorithm.

cs.CY↗

arXiv2022

PanGu-Coder: Program Synthesis with Function-Level Language Modeling

We present PanGu-Coder, a pretrained decoder-only language model adopting the PanGu-Alpha architecture for text-to-code generation, i.e. the synthesis of programming language solutions given a natural language problem description. We train PanGu-Coder using a two-stage strategy: the first stage employs Causal Language Modelling (CLM) to pre-train on raw programming language data, while the second stage uses a combination of Causal Language Modelling and Masked Language Modelling (MLM) training objectives that focus on the downstream task of text-to-code generation and train on loosely curated pairs of natural language program definitions and code functions. Finally, we discuss PanGu-Coder-FT, which is fine-tuned on a combination of competitive programming problems and code with continuous integration tests. We evaluate PanGu-Coder with a focus on whether it generates functionally correct programs and demonstrate that it achieves equivalent or better performance than similarly sized models, such as CodeX, while attending a smaller context window and training on less data.

cs.LG↗