Large language models (LLMs) have taken the tech industry by storm, revolutionizing various fields like copywriting and coding. These models, powered by clusters of thousands of GPUs and trained on trillions of tokens of data, possess remarkable natural language understanding. However, like any emerging technology, generative AI has faced criticism.
Critics argue that LLMs have limitations, such as hallucinating and reproducing bias. To address these concerns, leading model companies are working on improved steering, which aims to provide better controls on LLM outputs. Noam Shazeer compares this steering to directing small children, emphasizing the need for clear instructions to guide the models. Tools like Guardrails and LMQL have been developed to enhance steerability, but researchers continue to make advancements to improve LLMs’ productization.
Enhanced steering is particularly significant for enterprise companies, where unpredictable LLM behavior can be costly. By refining LLM outputs, founders can have greater confidence in the model’s performance aligning with customer demands. Moreover, improved steering opens opportunities for adoption in industries such as advertising, where accuracy and reliability are crucial. LLMs with better steering can also handle complex tasks with less prompt engineering, as they grasp the overall intent more effectively.
Additionally, advances in LLM steering can unlock new possibilities in sensitive consumer applications. While users might tolerate less accurate outputs from LLMs for conversational or creative purposes, they expect tailored and accurate responses when using LLMs for daily tasks, major decisions, or assistance from professionals like life coaches, therapists, and doctors. However, better steering is necessary to build user trust before LLMs can replace established consumer applications like search.
Another key innovation for LLMs is improved memory capabilities. Although LLMs have been successful in generating copywriting and ads, their outputs are generally generalized, making personalization and contextual understanding challenging. Prompt engineering and fine-tuning offer some level of personalization, but they are less scalable and often costly. In-context learning, which allows LLMs to draw from a company’s specific content, jargon, and context, is the ultimate goal for achieving refined and tailored outputs.
Enhanced memory requires two main components: context windows and retrieval. Context windows refer to the text that LLMs can process and use to inform their outputs, beyond their training data. Retrieval involves referencing relevant information from external data sources. Currently, LLMs have limited context windows and lack native retrieval capabilities, resulting in less personalized outputs. With expanded context windows and improved retrieval mechanisms, LLMs can provide more refined outputs specialized for individual use cases.
Expanded context windows enable models to process larger amounts of text and maintain continuity through conversations. This enhances their ability to summarize lengthy articles or generate coherent and contextually accurate responses in extended discussions. Noteworthy progress has been made with context windows in models like GPT-4, ChatGPT, and Claude, which have increased their token context window sizes. For example, GPT-4 has both an 8k and 32k token context window. Retrieval mechanisms further enhance memory by allowing access to additional information sources and enabling the focus on task-relevant information.
In conclusion, improved steering and memory capabilities are key innovations that will drive the evolution of LLMs in the next 6 to 12 months. Founders interested in integrating AI into their businesses should pay attention to these advancements. By leveraging these innovations, businesses can harness the power of LLMs while addressing concerns related to bias, accuracy, and reliability. As researchers continue to push the boundaries of generative AI, we can expect even more breakthroughs that will shape the future of this exciting field.
**Editor Notes:**
Large language models (LLMs) are revolutionizing various industries, bringing forth a new era of AI capabilities. However, they also face challenges related to issues like bias and accuracy. In this article, we explore the innovations of improved steering and memory capabilities for LLMs. These advancements will not only address concerns but also open new possibilities and opportunities for businesses. Stay up-to-date with the latest AI developments by visiting [GPT News Room](https://gptnewsroom.com).
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