Personality-Guided Code Generation Using Large Language Models

• URL: http://arxiv.org/abs/2411.00006v2
• Date: Thu, 29 May 2025 11:26:39 GMT
• Title: Personality-Guided Code Generation Using Large Language Models
• Authors: Yaoqi Guo, Zhenpeng Chen, Jie M. Zhang, Yang Liu, Yun Ma,
• Abstract summary: We conduct an empirical study on personality-guided code generation using large language models (LLMs)<n>Our results show that personality guidance significantly enhances code generation accuracy, with improved pass rates in 23 out of 28 LLM-dataset combinations.
• Score: 14.665759212676488
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Code generation, the automatic creation of source code from natural language descriptions, has garnered significant attention due to its potential to streamline software development. Inspired by research that links task-personality alignment with improved development outcomes, we conduct an empirical study on personality-guided code generation using large language models (LLMs). Specifically, we investigate how emulating personality traits appropriate to the coding tasks affects LLM performance. We extensively evaluate this approach using seven widely adopted LLMs across four representative datasets. Our results show that personality guidance significantly enhances code generation accuracy, with improved pass rates in 23 out of 28 LLM-dataset combinations. Notably, in 11 cases, the improvement exceeds 5%, and in 5 instances, it surpasses 10%, with the highest gain reaching 12.9%. Additionally, personality guidance can be easily integrated with other prompting strategies to further boost performance. We open-source our code and data at https://github.com/IanWalls/Persona-Code.

Related papers

• IFEvalCode: Controlled Code Generation [69.28317223249358]
  The paper introduces forward and backward constraints generation to improve the instruction-following capabilities of Code LLMs.<n>The authors present IFEvalCode, a multilingual benchmark comprising 1.6K test samples across seven programming languages.
  arXiv  Detail & Related papers  (2025-07-30T08:08:48Z)
• Personalized LLM Decoding via Contrasting Personal Preference [8.469329222500726]
  We propose CoPe, a novel decoding-time approach applied after performing parameter-efficient fine-tuning (PEFT) on user-specific data.<n>Our core idea is to leverage reward-guided decoding specifically for personalization by maximizing each user's implicit reward signal.<n>Our empirical results demonstrate that CoPe achieves strong performance, improving personalization by an average of 10.57% in ROUGE-L.
  arXiv  Detail & Related papers  (2025-06-13T09:12:44Z)
• OpenCodeInstruct: A Large-scale Instruction Tuning Dataset for Code LLMs [62.68905180014956]
  We introduce OpenCodeInstruct, the largest open-access instruction tuning dataset, comprising 5 million diverse samples.<n>Each sample includes a programming question, solution, test cases, execution feedback, and LLM-generated quality assessments.<n>We fine-tune various base models, including LLaMA and Qwen, across multiple scales (1B+, 3B+, and 7B+) using our dataset.
  arXiv  Detail & Related papers  (2025-04-05T02:52:16Z)
• Crystal: Illuminating LLM Abilities on Language and Code [58.5467653736537]
  We propose a pretraining strategy to enhance the integration of natural language and coding capabilities. The resulting model, Crystal, demonstrates remarkable capabilities in both domains.
  arXiv  Detail & Related papers  (2024-11-06T10:28:46Z)
• Large Language Models as Code Executors: An Exploratory Study [29.545321608864295]
  This paper pioneers the exploration of Large Language Models (LLMs) as code executors. We are the first to examine this feasibility across various LLMs, including OpenAI's o1, GPT-4o, GPT-3.5, DeepSeek, and Qwen-Coder. We introduce an Iterative Instruction Prompting (IIP) technique that processes code snippets line by line, enhancing the accuracy of weaker models by an average of 7.22%.
  arXiv  Detail & Related papers  (2024-10-09T08:23:22Z)
• SIaM: Self-Improving Code-Assisted Mathematical Reasoning of Large Language Models [54.78329741186446]
  We propose a novel paradigm that uses a code-based critic model to guide steps including question-code data construction, quality control, and complementary evaluation. Experiments across both in-domain and out-of-domain benchmarks in English and Chinese demonstrate the effectiveness of the proposed paradigm.
  arXiv  Detail & Related papers  (2024-08-28T06:33:03Z)
• Case2Code: Scalable Synthetic Data for Code Generation [105.89741089673575]
  Large Language Models (LLMs) have shown outstanding breakthroughs in code generation. Recent work improves code LLMs by training on synthetic data generated by some powerful LLMs. We propose a textbfCase2Code task by exploiting the expressiveness and correctness of programs.
  arXiv  Detail & Related papers  (2024-07-17T11:35:00Z)
• 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)
• From Effectiveness to Efficiency: Uncovering Linguistic Bias in Large Language Model-based Code Generation [30.914387085368734]
  Large Language Models (LLMs) have demonstrated promising capabilities for code generation. In this paper, we aim to investigate the potential linguistic bias through the lens of English and Chinese.
  arXiv  Detail & Related papers  (2024-06-02T03:22:30Z)
• Enhancing Reinforcement Learning with Label-Sensitive Reward for Natural Language Understanding [11.470005425117371]
  We propose a novel Reinforcement Learning framework enhanced with Label-sensitive Reward (RLLR) Our method aims to adeptly capture nuanced label-sensitive semantic features during RL, thereby enhancing natural language understanding. Experiments conducted on five diverse foundation models across eight tasks showcase promising results.
  arXiv  Detail & Related papers  (2024-05-30T07:19:31Z)
• AlchemistCoder: Harmonizing and Eliciting Code Capability by Hindsight Tuning on Multi-source Data [64.69872638349922]
  We present AlchemistCoder, a series of Code LLMs with enhanced code generation and generalization capabilities fine-tuned on multi-source data. We propose incorporating the data construction process into the fine-tuning data as code comprehension tasks, including instruction evolution, data filtering, and code review.
  arXiv  Detail & Related papers  (2024-05-29T16:57:33Z)
• LLM-Based Test-Driven Interactive Code Generation: User Study and Empirical Evaluation [13.800675921118348]
  We propose a novel interactive workflow TiCoder for guided intent clarification. We present an empirical evaluation of the effectiveness of the workflow to improve code generation accuracy. We observe an average absolute improvement of 45.97% in the pass@1 code generation accuracy for both datasets and across all LLMs within 5 user interactions.
  arXiv  Detail & Related papers  (2024-04-15T19:16:32Z)
• Code Needs Comments: Enhancing Code LLMs with Comment Augmentation [91.52444946362547]
  We introduce a novel data augmentation method that generates comments for existing code, coupled with a data filtering strategy that filters out code data poorly correlated with natural language. We conducted experiments on three code-focused Large Language Models and observed consistent improvements in performance on two widely-used programming skill benchmarks.
  arXiv  Detail & Related papers  (2024-02-20T13:56:38Z)
• LLM-Assisted Code Cleaning For Training Accurate Code Generators [53.087019724256606]
  We investigate data quality for code and find that making the code more structured and readable leads to improved code generation performance of the system. We build a novel data-cleaning pipeline that uses these principles to transform existing programs. We evaluate our approach on two challenging algorithmic code generation benchmarks and find that fine-tuning CodeLLaMa-7B improves the performance by up to 30% compared to fine-tuning on the original dataset.
  arXiv  Detail & Related papers  (2023-11-25T02:45:50Z)
• Testing LLMs on Code Generation with Varying Levels of Prompt Specificity [0.0]
  Large language models (LLMs) have demonstrated unparalleled prowess in mimicking human-like text generation and processing. The potential to transform natural language prompts into executable code promises a major shift in software development practices.
  arXiv  Detail & Related papers  (2023-11-10T23:41:41Z)
• Accelerating LLaMA Inference by Enabling Intermediate Layer Decoding via Instruction Tuning with LITE [62.13435256279566]
  Large Language Models (LLMs) have achieved remarkable performance across a wide variety of natural language tasks. However, their large size makes their inference slow and computationally expensive. We show that it enables these layers to acquire 'good' generation ability without affecting the generation ability of the final layer.
  arXiv  Detail & Related papers  (2023-10-28T04:07:58Z)
• Learning Performance-Improving Code Edits [107.21538852090208]
  We introduce a framework for adapting large language models (LLMs) to high-level program optimization. First, we curate a dataset of performance-improving edits made by human programmers of over 77,000 competitive C++ programming submission pairs. For prompting, we propose retrieval-based few-shot prompting and chain-of-thought, and for finetuning, these include performance-conditioned generation and synthetic data augmentation based on self-play.
  arXiv  Detail & Related papers  (2023-02-15T18:59:21Z)

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.