Where We See the Biggest Opportunities in Generative AI

As we continue to push boundaries, it's crucial to prioritize transparency, reliability, and efficiency in generative AI development.

ChatGPT for coding

From self-driving cars to AI-powered robots, the future is here… and it’s busy generating its own content. Generative AI has been at the forefront of innovation in the past few years. With its ability to generate various forms of content, it has witnessed a remarkable surge in applications, marked by exciting advancements across various domains. While the landscape is evolving rapidly, there are still opportunities for improvement that developers can explore to enhance its capabilities further.

Room for Growth

Hallucinations in Generative AI refer to the phenomenon where models produce inaccurate or irrelevant outputs that do not align with the desired context or task. This issue is widely recognized and discussed within the community due to its significant implications. Hallucinations can occur in both text and image generation tasks, leading to misleading or erroneous outputs that undermine the reliability and trustworthiness. The prevalence of hallucinations is of significant concern, particularly in domains where precision and accuracy are critical, such as healthcare, legal, or financial applications. For instance, last year, a lawyer who used ChatGPT in court with fake citations faced consequences.

Interpretability and explainability are critical aspects of Generative AI systems, particularly in domains where transparency and trust are paramount. However, current models often lack interpretability, making it challenging to understand the underlying reasoning processes or decision-making mechanisms behind generated outputs. This lack of transparency hindering trust and reliance on Generative AI systems for critical tasks.

The training of models is resource-intensive, requiring large-scale datasets and significant computational resources. Correcting misinformation or updating outdated information within these models can be challenging and expensive, leading to higher training costs and limited accessibility for developers. This poses a barrier to entry for developers and organizations seeking to leverage Generative AI in their applications. Although smaller models, like Mistral, are getting attention and praise, training costs still remain high.

LLMs often struggle to maintain coherence and context in longer conversations, primarily due to memory limitations. Maharana et al. (2024) outlined this problem in their research paper. As conversations progress, LLMs may encounter difficulties in recalling relevant information from earlier parts of the dialogue, leading to disjointed or inconsistent responses. This limitation hinders the model’s ability to engage in sustained interactions or understand nuanced conversational dynamics effectively. As conversations get longer, the engagement level needs to be sustained to be able to maintain coherence.

Turning Them Into Opportunities

To address hallucinations, developers can leverage knowledge graphs to provide grounded context and structure for the models. Knowledge graphs offer a structured representation of domain-specific knowledge and relationships. By integrating structured data from knowledge graphs, developers can mitigate hallucinations by ensuring that the model’s outputs align with accurate and contextually relevant information. Denny Vrandečić highlights the lack of consistency with LLMs in his keynote speech and states that knowledge graph can provide that consistency with ground truth.

Vrandečić  highlights in his speech how integrating knowledge graphs can enhance interpretability and explainability by providing structured and explainable representations of knowledge. Knowledge graphs offer a graph-based representation of entities and their relationships, enabling developers to trace the model’s reasoning process and understand the underlying logic behind generated outputs. This facilitates a better decision-making and accountability. By prioritizing interpretability and explainability in Generative AI development, developers can foster trust and transparency.

Knowledge graphs also offer a cost-efficient alternative for training Generative AI models, mitigating the high operational costs associated with traditional approaches. It  allows for faster and more cost-effective query processing. By leveraging graph-based querying techniques, developers can reduce computational overhead compared to LLMs. They also require fewer computational resources for model training and inference compared to LLMs. LLMs, particularly large-scale models, demand substantial computational resources for training and fine-tuning, resulting in high operational costs. Knowledge graphs also have its disadvantages but it is a promising opportunity for improving performance.

Addressing long-term context and memory limitations requires innovative approaches to augmenting model architectures. Maharana et al. (2024)’s one potential solution is the integration of memory-augmented architectures, which provide mechanisms for storing and retrieving relevant information over extended dialogue sessions. Additionally, hierarchical attention mechanisms can help LLMs focus on salient aspects of conversation, enabling more effective retention and utilization of contextually important information. By enhancing the model’s ability to retain and recall long-term context, developers can improve the coherence and relevance of LLM-generated responses in extended conversations.

As we continue to push the boundaries of Generative AI, it’s crucial to prioritize transparency, reliability, and efficiency in Generative AI development. By addressing these challenges head-on and embracing emerging technologies and methodologies, we can usher in a new era of Generative AI that is more trustworthy, adaptable, and capable of meeting the diverse needs of our rapidly evolving world.

Ziba Atak

Ziba Atak is a passionate Data Scientist and Machine Learning Engineer with a keen interest in the fascinating realms of Generative AI and Natural Language Processing (NLP). With a background in leveraging data-driven insights to tackle complex problems, Ziba is dedicated to exploring the frontiers of AI technology and its applications. Her professional pursuits are centered on unraveling the complexities of artificial intelligence and leveraging its potential for transformative impact in various domains.


Ziba Atak
Ziba Atak is a passionate Data Scientist and Machine Learning Engineer with a keen interest in the fascinating realms of Generative AI and Natural Language Processing (NLP). With a background in leveraging data-driven insights to tackle complex problems, Ziba is dedicated to exploring the frontiers of AI technology and its applications. Her professional pursuits are centered on unraveling the complexities of artificial intelligence and leveraging its potential for transformative impact in various domains.