Overcoming Limitations of Large Language Models in AI Content Generation

Tony Yan

·July 31, 2023

·6 min read

Introduction

The past six months have undoubtedly been an explosive period for AIGC (AI Generated Content) applications, thanks to the breakthrough progress made by large language models. Our team has also built Quick Creator, an AI tool to generate blogs and landing pages, by leveraging AI to solve our own problems.

Building an internal tool versus a customer-facing product comes with different challenges. Our product has been live for 4 months with nearly 3000 registered users and 50 paying customers, all through organic traffic. Serving these customers, especially paid ones, has allowed us to experience the powerful capabilities of large models, as well as uncover more limitations due to how they work. Here I will summarize the key limitations based on our experience using AI to generate SEO-optimized blogs.

Hallucination in Large Language Models

Large language models have revolutionized the field of AI content generation, allowing for the creation of high-quality content at scale. However, these models still suffer from certain limitations that can impact their accuracy and effectiveness. One common issue is hallucination, where the model generates incorrect or irrelevant information based on incomplete or inadequate input.

Causes of Hallucination

There are several factors that can contribute to hallucination in large language models. One key cause is the lack of context or specificity in the input prompt. When a prompt is too vague or general, the model may generate responses that are tangential or unrelated to the intended topic. Similarly, when a prompt asks about out-of-domain knowledge that was not included in the model's training data, the model may generate inaccurate or nonsensical responses.

Another factor that can contribute to hallucination is bias in the training data. If a model is trained on data that contains biased or inaccurate information, it may produce similarly biased or inaccurate output. This can be particularly problematic when generating content related to sensitive topics such as race, gender, or politics.

Mitigating Hallucination

Fortunately, there are several strategies that can be used to mitigate hallucination in large language models. One approach is to use more specific and detailed prompts that provide additional context and constraints for the model. By providing more guidance and structure for the model's output, it may be less likely to generate incorrect or irrelevant information.

Another strategy is to fine-tune the model on in-domain data related to the specific topic being generated. By training on data that is more closely aligned with the target domain, the model may be better able to understand and generate accurate information related to that domain.

Finally, querying knowledge databases such as Wikipedia or Google Knowledge Graph can also help mitigate hallucination by providing additional context and information for the model. By incorporating external knowledge sources into its output generation process, a large language model may be better able to produce accurate and relevant content.

Lack of Personality in Content Generated from Large Language Models

Large language models are capable of generating vast amounts of content with impressive accuracy. However, there is a significant issue with the lack of personality in the content they generate. The problem is that the generated content tends to be generic and templatized, lacking any real character or voice. This can make it difficult for businesses to differentiate themselves from their competitors and can lead to disengaged audiences.

The Issue of Lack of Personality

The lack of personality in AI-generated content is a result of the way these models are trained. They are fed vast amounts of text data and learn to predict what words should come next based on statistical patterns in the data. While this approach is highly effective at generating coherent and grammatically correct sentences, it does not capture the nuances of human communication.

Humans communicate using more than just words; we use tone, body language, and context to convey meaning. These elements are difficult for large language models to replicate because they require a deep understanding of human psychology and culture. As a result, AI-generated content can feel robotic and impersonal.

Tapping into Brand Assets and History

To overcome this limitation, businesses can tap into their unique brand assets and history when creating AI-generated content. By providing the model with information about their brand's values, tone, and history, they can help it create content that better reflects their identity.

For example, if a business has a playful brand personality, they could provide examples of previous marketing campaigns that showcase this personality. The model could then use this information to generate new content that aligns with the brand's established voice.

Similarly, if a business has a long history or unique origin story, they could provide this information to the model as well. This could help it create content that highlights the brand's heritage or sets it apart from its competitors.

By tapping into their unique brand assets and history, businesses can help AI-generated content feel more personalized and engaging. This can lead to increased customer engagement and loyalty over time.

Balancing Efficiency and Quality in Content Generation

Practitioners in the field of AI content generation are constantly refining techniques to balance efficiency and quality. With the rise of customer expectations, it is crucial to produce content that is not only accurate but also engaging and valuable. One approach to achieving this balance is through fine-tuning language models on in-domain data. This involves training a model on specific data related to a particular industry or topic, which allows it to generate more relevant and accurate content.

Another technique for balancing efficiency and quality is by using knowledge databases to supplement language models. For instance, if a prompt requires information that is not present in the model's training data, the system can query external knowledge sources such as Wikipedia or specialized databases like PubMed. This approach can help reduce hallucination by providing additional context for generating content.

In addition to these technical solutions, practitioners must also consider the importance of brand assets and history when generating content. A company's unique personality and voice are essential components of its brand identity, and customers expect content that reflects these qualities. To achieve this level of personalization, practitioners need to work closely with clients to understand their brand values and develop strategies for incorporating them into generated content.

One way to achieve this level of personalization is through augmentation techniques such as template-based generation or human-in-the-loop systems. Template-based generation involves creating pre-defined templates for specific types of content (e.g., product descriptions) that can be easily customized by adding relevant information. Human-in-the-loop systems involve having humans review and edit generated content before publication, ensuring that it meets quality standards while still being efficient.

Conclusion

In conclusion, the limitations of large language models in AI content generation are significant but not insurmountable. By understanding the challenges of hallucination and lack of personality, content creators can take steps to mitigate these issues and produce more engaging and valuable content. Balancing efficiency and quality is also a crucial consideration, as AI-generated content becomes increasingly important in meeting customer expectations. Despite the challenges, the potential for large language models in content creation is vast, with applications ranging from personalized marketing to automated news articles. As these models continue to improve and evolve, it is essential that practitioners prioritize ethical considerations such as bias and transparency. Ultimately, by harnessing the power of AI in content creation while remaining mindful of its limitations and risks, we can unlock new opportunities for innovation and growth in this rapidly evolving industry.