Bias Toward Simplicity in Code-Generating LLMs: An Empirical Evaluation of Algorithmic Reasoning Depth

1. Introduction

Large Language Models (LLMs) like GPT-4, Claude, and Gemini have emerged as powerful tools in software development, capable of generating code from natural language prompts with surprising fluency. From assisting developers with routine scripting to solving advanced algorithmic problems, these models are increasingly embedded into IDEs, interview platforms, and educational tools. However, as their adoption expands, critical questions arise about the quality and reasoning behind the code they produce.

While much of the existing literature focuses on correctness, style, or functional completeness, an often-overlooked aspect is how deeply these models reason about algorithmic efficiency. Consider a prompt asking for a solution to find the shortest path in a graph or compute the nth Fibonacci number — will the LLM provide a naive recursive approach or an optimal dynamic programming solution?

This paper explores a hypothesis we term simplicity bias: the idea that LLMs tend to prefer simpler, easier-to-learn algorithms, even when more complex or optimal alternatives are expected. Such a bias could stem from the frequency distribution of training data, prompt phrasing, or the lack of explicit reasoning chains within the model's architecture. This behavior has profound implications — not only for the correctness and performance of AI-generated code but also for how developers evaluate and trust the suggestions these systems produce.

Through empirical evaluation on a curated set of coding problems, this study investigates the extent to which LLMs default to simplicity, how this varies across tasks and prompts, and what this reveals about the current state of algorithmic reasoning in modern code-generating AI systems.

2. Methodology

To investigate whether large language models (LLMs) exhibit a systematic bias toward simpler, suboptimal algorithms in code generation, we designed a structured empirical study. Our methodology was guided by the goal of simulating realistic coding prompt conditions across a diverse set of algorithmic problems, allowing us to isolate and analyze model behavior in a controlled yet representative environment.

2.1 Benchmark Problem Set

We curated a benchmark suite consisting of ten algorithmically significant problems, each with well-established naive and optimal solutions. These problems were selected based on their representation across different categories of algorithmic reasoning—ranging from recursion and dynamic programming to graph traversal and string processing.

2.2 Language Models Evaluated

We selected two state-of-the-art LLMs commonly used for code generation tasks: OpenAI's GPT-3.5 Turbo and GPT-4. Both models were accessed via the OpenAI API to ensure consistency in versioning and querying. An optional third model (Claude 3 or Gemini Pro) was considered for broader comparison but is not included in the core evaluation due to limited access during the experiment window.

2.3 Prompting Styles

To examine the influence of prompt phrasing on model output, we tested three distinct prompting strategies for each problem:

1. Zero-shot: A basic instruction prompt without context — e.g., "Write a function to solve [problem]."
2. Few-shot: The same instruction as above, but preceded by 1–2 solved examples of unrelated problems to provide the model with general code-writing context.
3. Explicit Optimality Prompt: The original prompt modified to explicitly request performance — e.g., "Write the most efficient solution to [problem]."

Figure 1: Prompting Strategy Flow Layout

2.4 Evaluation Metrics

Each response was evaluated along four dimensions:

2.5 Bias Scoring Metric

To quantify the extent of simplicity bias across the dataset, we introduce a metric called the Simplicity Bias Score (SBS), defined as:

SBS = (Number of simple (naive) solutions) / (Total number of valid (correct) solutions generated)

An SBS close to 1.0 implies a strong tendency toward simpler or naive algorithms, while an SBS closer to 0.0 indicates a preference for optimal solutions. In this formulation, only correct (valid) code responses are considered in the denominator to ensure that the metric reflects algorithm choice rather than functional correctness.

This score was computed separately for each model (GPT-3.5 and GPT-4) and for each prompting strategy (zero-shot, few-shot, and explicit-optimal). Comparing SBS across these dimensions enables us to evaluate how different prompting styles influence algorithmic reasoning, and whether more advanced models demonstrate reduced simplicity bias in practice.

3. Results & Observations

To assess how large language models respond to different prompt styles across a range of algorithmic tasks, we conducted a quantitative analysis based on the outputs collected from GPT-3.5 and GPT-4. Each of the problems was posed using three distinct prompting styles: zero-shot, few-shot, and explicit optimality prompts. The solutions were evaluated based on algorithm type, efficiency, correctness, and code complexity.

3.1 Simplicity Bias Scores (SBS)

To quantify the tendency of LLMs to produce simpler solutions, we computed the Simplicity Bias Score (SBS), defined as the ratio of naive (simple) solutions to total valid outputs. A lower SBS indicates a stronger preference for optimal algorithms.

Figure 2: Simplicity Bias Scores Across Prompting Techniques

These scores indicate that zero-shot prompts consistently yielded the highest SBS values, suggesting that LLMs default to simpler solutions unless explicitly guided. GPT-4 consistently outperformed GPT-3.5 in selecting more optimal algorithms, particularly when efficiency was emphasized in the prompt.

3.2 Case Studies

To illustrate the SBS behavior more concretely, we highlight responses from selected problems:

Figure 3: Algorithm Type Distribution per Problem Across Models

3.3 Task Difficulty vs. Bias

We further categorized problem types by domain to study how task complexity affects LLM bias. Below are average SBS values for GPT-4 across different problem types:

LLMs showed a stronger preference for simpler solutions in math-heavy and dynamic programming problems. This suggests a lack of deep reasoning under default conditions, especially in domains that demand complexity awareness.

3.4 Prompt Style Impact

Prompting style played a significant role in mitigating simplicity bias. When asked explicitly for 'efficient' solutions, both GPT-3.5 and GPT-4 produced more optimal implementations. However, such specificity is rare in typical development workflows, raising concerns about default model behavior in production contexts.

4. Conclusion

This study investigated whether large language models (LLMs) such as GPT-3.5 and GPT-4 demonstrate a simplicity bias in algorithmic code generation. By evaluating model outputs across a curated set of algorithmic problems—each having both a naive and optimal implementation—we introduced and applied a Simplicity Bias Score (SBS) to quantify this tendency.

Our findings confirm the hypothesis: LLMs, especially in zero-shot conditions, tend to default to simpler algorithms such as recursive Fibonacci, bubble sort, or trial division. This bias persists even in models like GPT-4, though explicit prompting for optimality mitigates the effect significantly. Few-shot examples also improve performance but to a lesser extent than direct optimization requests.

Key Implications

1. Development Practices: Developers should be aware of this bias when using LLMs for algorithmic tasks, especially in performance-critical applications.

2. Prompt Engineering: Explicit requests for efficiency or optimization can significantly improve the quality of generated algorithms.

3. Model Training: Future LLM development should consider incorporating more sophisticated algorithmic reasoning capabilities.

4. Educational Impact: This bias could affect how students learn algorithms if they rely heavily on LLM-generated solutions without understanding the trade-offs.

Future Work

Further research should explore:

• The impact of this bias on real-world software development
• Methods to reduce simplicity bias during model training
• Comparative analysis across different LLM architectures
• Long-term effects on developer skills and algorithmic thinking

This research contributes to our understanding of LLM limitations and provides practical guidance for their effective use in software development contexts.

References

• Chen, M., et al. (2021). "Evaluating Large Language Models Trained on Code." arXiv preprint arXiv:2107.03374.
• Austin, J., et al. (2021). "Program Synthesis with Large Language Models." arXiv preprint arXiv:2108.07732.
• Li, Y., et al. (2022). "Competition-level code generation with AlphaCode." Science, 378(6624), 1092-1097.
• Nijkamp, E., et al. (2022). "CodeGen: An Open Large Language Model for Code Generation." arXiv preprint arXiv:2203.13474.