Finally: Some Truth Serum for Lying GenAI Chatbots
The evolution of large language model (LLM)-based generative AI (genAI) chatbots, like ChatGPT, has been nothing short of remarkable since OpenAI made ChatGPT available to the public in late 2022. However, despite the rapid advancement in the genAI revolution, several fundamental issues have hindered the full potential of these chatbots in business settings.
One major concern is the generic output often generated by genAI chatbots. These responses lack nuance, creativity, and personalization, primarily due to their reliance on large-scale training data. Critics warn of “model collapse,” where repeated training on AI-generated data diminishes variability and originality over time, leading to homogenized content.
Another prevalent problem lies in the hallucinatory output produced by AI chatbots. These responses can be factually inaccurate or nonsensical, yet presented with unwavering confidence. The lack of real-world understanding by LLMs, coupled with biases and inaccuracies in training data, can result in embarrassing situations, such as chatbots fabricating entire legal cases.
Moreover, deliberate sabotage of chatbot outputs poses a serious threat. Instances like the “Pravda” network orchestrated by the Russian government showcase how malicious actors can manipulate training data to push false narratives and biases. This data poisoning can significantly impact the reliability, accuracy, and ethical integrity of genAI chatbots, leading to biased responses and misinformation.
To combat these flaws, the industry is shifting towards customized, special-purpose tools to enhance the value and efficiency of genAI in business settings. Techniques like retrieval-augmented generation (RAG) and prompt engineering are being employed to improve model outputs. However, challenges around data privacy and security in customizing LLM use remain a concern for companies.
In the quest for quality output, companies like Contextual AI have introduced groundbreaking solutions such as the Grounded Language Model (GLM). The GLM prioritizes factual accuracy by adhering strictly to provided knowledge sources, avoiding reliance on generic or compromised training data. By suppressing pretraining biases and emphasizing user-supplied information, the GLM aims to provide responses with embedded quality sourcing for easy fact-checking.
As the industry continues to address the shortcomings of genAI chatbots, users must prioritize quality output over flashy features. By opting for chatbots optimized for specific industries and emphasizing customization, users can ensure more truthful and reliable interactions. The future of genAI lies in leveraging advancements like the GLM to enhance user satisfaction and credibility.