Unlocking the Power of Large Language Models: Understanding How They Work and Don’t Work
Large Language Models (LLMs) have revolutionized the field of artificial intelligence, offering impressive capabilities that can fuel productivity. However, many professionals have avoided using them due to the risks associated with their use. Understanding the inherent hurdles is essential to successfully navigating them and maximizing the value you get from generative AI.
Predictions and Hallucinations
The first analogy relates to a technology you probably use every day. Your phone has a “predictive text” feature that, given some text you’ve written, predicts three possibilities for the next word. This feature is like a smaller version of modern LLMs, which can generate entire pages of coherent and relevant content.
Consider a Large Language Model predicting a word to follow the phrase “the students opened their.” Based on its training, the LLM determines that “books” is the most likely next word. The important concept to understand here is that the LLM is not looking up data in a database, it is not searching the web, and it is not “understanding” the text. Rather, it is using statistical correlations or patterns that it has learned from the large datasets of text that it was trained on.
Predictive text in action
Language Comprehension and the Chinese Room Argument
A second analogy underscores the point that LLMs do not “understand” language. In 1980, philosopher John Searle introduced The Chinese Room argument to challenge the notion that computers with conversational abilities are actually understanding that conversation.
The Chinese Room argument
Data as Trainer vs. Database
Our final analogy helps explain how Large Language Models use their training data and why they can’t use it as reference data. Assume the individual pieces of training data that feed an LLM are like individual pieces of fruit being fed into a blender. Once the model has been trained, what is left is like a fruit smoothie. You no longer have access to the individual pieces of fruit.
Data as trainer vs. database
Beyond the Reference Model: SimPO Unlocks Efficient and Scalable RLHF for Large Language Models
Artificial intelligence is continually evolving, focusing on optimizing algorithms to improve the performance and efficiency of large language models (LLMs). Reinforcement learning from human feedback (RLHF) is a significant area within this field, aiming to align AI models with human values and intentions to ensure they are helpful, honest, and safe.
SimPO, a simpler and more effective approach to preference optimization, utilizes the average log probability of a sequence as the implicit reward, aligning better with model generation and removing the need for a reference model. This makes SimPO more compute and memory efficient.
SimPO in action
DeepSeek-Prover: Boosting Theorem Proving in LLMs
Large language models (LLMs) have shown impressive capabilities in mathematical reasoning, but their application in formal theorem proving has been limited by the lack of training data. To address this challenge, researchers have developed a novel approach to generate large datasets of synthetic proof data.
DeepSeek-Prover in action
Conclusion
Generative AI can help organizations increase productivity, enhance client and employee experiences, and accelerate business priorities. Simply having overall awareness and a better understanding of how Large Language Models do and do not work will make you a more effective, safer user.
