# Comparative Analysis of Chatbots Using Large Language Models for Web Development Tasks
## 5 Discussion  
Benchmarking and evaluating large language models is crucial, but existing methods have limitations. Traditional fixed benchmarks are limited in scope due to costly human annotations and risk overfitting. Using LLMs as evaluators provides human-like assessments but may suffer from biases and unreliability in certain domains. Human evaluation is considered the gold standard but is time-consuming and labor-intensive for large test sets, while small sets introduce sampling bias. Some platforms like Chatbot Arena [60] or Dynabench [61] facilitate human evaluation through crowdsourcing, but the process remains manual and costly. Another issue is the breakneck pace at which LLMs are improving, with major upgrades happening within just months. This makes it extremely difficult to establish stable benchmarks before they become outdated by the next generation of more powerful models.  
Adding to the complexity is the uneven global availability of LLM chatbots due to factors like regulatory differences, linguistic barriers, and infrastructure constraints across countries. Some markets have greater access than others, limiting the representativeness of any benchmarks [62; 63].  
There is also a lack of standardization in the pricing and usage models offered by commercial chatbot providers. Subscription, pay-per-use, freemium, and enterprise plans make it challenging to directly compare cost-effectiveness across different real-world applications [64; 65].  
While the focus of this study was on generating basic elements such as HTML and PHP forms, it is important to consider the chatbots' ability to handle larger, more complex projects. Such projects often require maintaining state across multiple interactions and managing dependencies between different files and technologies. General-purpose language models possess conversational memory capabilities that allow them to maintain context over multiple interactions, which can be beneficial in managing stateful applications [66]. However, complex dependency management across multiple files and technologies--such as coordinating frontend, backend, and database interactions--remains a significant challenge. Current chatbots may struggle without specific prompts and user intervention, as they are optimized for simpler tasks. Programming-specific models are more focused on  
Figure 3: The bar chart depicts the average evaluation scores of five chatbots as assessed by three human testers.  
code accuracy and syntax adherence and also face challenges in handling multi-component projects that require seamless integration between technologies.  
A critical area requiring improvement is enhancing the factual accuracy and reliability of chatbot outputs. Integrating automated fact-checking capabilities could reduce issues like hallucinations, but implementing robust self-verification remains an open technical hurdle [67, 68].  
Furthermore, despite their fluency, modern chatbots can struggle with precisely comprehending natural language prompts and maintaining consistency across long conversations. Better prompt engineering techniques are needed to optimize chatbot performance and response quality [69].  
Finally, while foundational LLMs trained on broad data are impressively capable, domain-specific customization through techniques like fine-tuning can unlock significant additional performance gains. However, accounting for such specialized customizations in benchmarking presents its own difficulties.