The rise of LLMs like ChatGPT and GPT-3 has generated buzz across various industries, from email composition to software code generation. However, with their growing popularity, concerns about their limitations also surface. Here's a look at some challenges LLMs face and the ongoing research to mitigate them:
LLMs, while adept at producing plausible text, often stray from facts. The solution? Knowledge retrieval techniques. By tapping into external sources like Wikipedia, the models can stay rooted in reality. Innovations like Google's REALM in 2020, AI21 Labs' in-context retrieval, and You.com's integration in ChatGPT are leading this charge. In any production model we always have a supervisor AI agent who's sole job is to protect customer data and mitigate any hallucinations.
LLMs don't comprehend language as humans do, leading to occasional missteps. Enter prompt engineering, which helps steer their responses. Techniques range from few-shot learning, where models are provided with context, to chain-of-thought (CoT) prompting, shedding light on the model's reasoning process. As AI continues to evolve, these methods can bridge the gap between machine-generated and human-like responses.
For specialized domains, fine-tuning LLMs is crucial. Techniques like "reinforcement learning from human feedback" (RLHF) have proven effective, as seen with ChatGPT's superior user instruction following. The future could see tech giants like OpenAI and Microsoft offering tools for companies to create their RLHF pipelines.
LLMs come with a hefty price tag, limiting their reach. Solutions include creating efficient AI processors and developing smaller yet powerful models. Take Facebook's LLaMa, for instance, which offers comparable performance to GPT-3 but with fewer parameters, making it accessible to a broader audience.
While LLMs have hurdles ahead, the continuous advancements in the field hint at a promising future
