How Personality Traits Impact Code Generation Accuracy

Code Generation is the process of automatically creating source code from plain language descriptions. Think of it as telling a robot how to write a recipe; instead of food, it creates code that does something. This area has become a hot topic because it can really speed up the way we develop software. With the rise of large language models (LLMs), we can produce nearly complete and working code. Some special LLMs focus specifically on coding tasks, making them even smarter at creating code.

Studies have shown that matching a person’s work tasks with their personality can lead to better results. Imagine a developer who likes working alone being given a project that requires long meetings. Not ideal, right? Likewise, teams that have a mix of different personalities often produce better software.

In the world of code generation, LLMs are often asked to act like programmers. But here's the catch: does giving these “programmers” the right Personality Traits improve the quality of the code they create?

To investigate this, a study was carried out on personality-guided code generation using LLMs. The researchers used an advanced LLM to generate a programmer personality for coding tasks. They then evaluated how taking on these different personalities impacted the Accuracy of code generation.

#Testing the Waters

The researchers evaluated seven popular LLMs across four well-known datasets. These models are like the celebrities of the coding world, developed by companies like OpenAI, Meta, and Alibaba. One tool used for personality assessment was the Myers-Briggs Type Indicator (MBTI), which sorts people into 16 different personality types. This framework is easy to use and helps teams work better together by matching tasks to the right personalities.

The results were pretty interesting. The study found that guiding LLMs with personality traits improved code generation accuracy. Out of 28 tests using different LLMs and datasets, 23 saw improvements. In 11 instances, the accuracy increased by more than 5%, and in five instances, it jumped by over 10%. One model even showed a whopping gain of 12.9%!

#Why Does Personality Matter?

So, why would giving a robot a “personality” make it write better code? Well, it turns out that matching personalities to tasks is a real thing in the software world as well. If you’ve ever worked with a team, you know that a mix of personalities can drive creativity and problem-solving. If you have an introverted coder who thrives on solitude, giving them an extroverted task might not yield the best results.

The LLMs evaluated can be seen as acting like team members of varying skill levels. It’s suggested that mediocre performers benefit the most from personality guidance. Strong performers may already be doing well, while weaker models might need more fundamental changes to improve.

#The Datasets

For this research, four datasets were chosen for evaluation:

1. MBPP Sanitized: This dataset contains 427 Python problems that are perfect for beginners. Each problem comes with a description, a solution, and tests to see if the code works.

2. MBPP+: This is a more refined version of the previous dataset, with improvements made to existing problems and a larger set of test cases for better evaluation.

3. HumanEval+: This dataset has 164 handpicked Python problems, including function signatures and unit tests to catch common mistakes that LLMs might overlook.

4. APPS: This is a large benchmark of 10,000 Python problems with varying difficulty levels. To manage resources, the researchers randomly picked 500 problems from the interview-level set.

#The LLMs Used

The researchers utilized seven LLMs for the study:

• GPT-4o and GPT-4o mini: These are versatile general-purpose models.
• Llama3.1: Another general-purpose LLM.
• Qwen-Long: A promising general-purpose model.
• DeepSeek-Coder: Designed specifically for code tasks.
• Codestral: Another code-specific LLM.
• CodeLlama: A specialized LLM for coding tasks.

#Understanding Code Generation Accuracy

Accuracy was measured by calculating the pass rate of LLMs on coding tasks. If a model generated code that passed all tests, it was deemed successful. Each model was run multiple times to ensure the results were reliable.

#The Itch to Combine Personalities with Other Strategies

Next, the researchers asked, “Can we do better?” They looked into combining personality guidance with existing strategies like few-shot learning and Chain of Thought (CoT). Few-shot learning gives the model examples to help with understanding, while CoT encourages the model to think step-by-step through problems.

The results indicated that personality-guided code generation generally outperformed both strategies alone. However, when combined with CoT, the improvements were even bigger. This combination resulted in better accuracy for all LLMs involved.

#How Prompt Design Plays a Role

The researchers also examined how the way they introduced personalities affected the model's performance. Did longer prompts with full personality descriptions help? Or did shorter prompts indicating just the MBTI type do just as well? To find out, they ran tests and concluded that using the full MBTI description consistently performed better. On average, it improved results by nearly 4%.

#Personality Generation Matters

An interesting aspect of the study was how personalities were generated. They primarily used GPT-4o for this task. When they tested the idea of letting each LLM generate its own personality, they found that this approach was less effective. Using a consistent personality generator like GPT-4o gave better results. Models designed for coding tasks struggle with generating their personalities based on the coding descriptions, reinforcing the idea of using a dedicated personality generator.

#Conclusions and Limitations

This study provides important insights into how guiding LLMs with personality traits can enhance code generation. The results show that personality plays a significant role in boosting accuracy. However, it’s important to note the limitations of the research.

The only personality framework looked at was MBTI. While this model is popular, it may not cover the full range of personality traits that could affect performance. Plus, even though they tested seven LLMs, the findings may not apply broadly to other models out there. They also focused only on function-level code generation, leaving room for further studies that look at more complex coding tasks.

In conclusion, this research opens new doors in the field of code generation, showing that personality traits in LLMs can lead to better software outcomes. By combining personality guidance with existing strategies, developers could find new, more effective ways to leverage these technology improvements, making coding a little less robotic and a bit more human-like.

So, next time you ask your LLM to write code, maybe remind it to “be itself” a little more; it could just write some cleaner code-and save you from debugging!