Can LLMs reason? | Yann LeCun and Lex Fridman

Summary

The transcript discusses the limitations of large language models (LLMs) in reasoning and their constant computation per token produced. It suggests that future dialogue systems will require a more sophisticated approach, involving planning and optimization before generating a response. The conversation touches on the potential for systems to build upon a foundational world model, using processes akin to probabilistic models to infer latent variables. This could lead to more efficient and deep reasoning capabilities, moving beyond the current auto-regressive prediction methods.

Takeaways

• 🧠 The reasoning in large language models (LLMs) is considered primitive due to the constant amount of computation spent per token produced.
• 🔄 The computation does not adjust based on the complexity of the question, whether it's simple, complicated, or impossible to answer.
• 🚀 Human reasoning involves spending more time on complex problems, with an iterative and hierarchical approach, unlike the constant computation model of LLMs.
• 🌐 The future of dialogue systems may involve building upon a well-constructed world model with mechanisms like persistent long-term memory and reasoning.
• 🛠️ There's a need for systems that can plan and reason, devoting more resources to complex problems, moving beyond auto-regressive prediction of tokens.
• 🎯 The concept of an energy-based model is introduced, where the model output is a scalar number representing the 'goodness' of an answer for a given prompt.
• 📈 Optimization processes are key in future dialog systems, with the system planning and optimizing the answer before converting it into text.
• 🌟 The optimization process involves abstract representation and is more efficient than generating numerous sequences and selecting the best ones.
• 🔄 The training of an energy-based model involves showing it compatible pairs of inputs and outputs, using methods like contrastive training and regularizers.
• 🔢 The energy function is trained to have low energy for compatible XY pairs and higher energy elsewhere, ensuring the model can distinguish between good and bad answers.
• 📚 The transcript discusses the indirect nature of training LLMs, where high probability for one word results in low probability for others, and how this could be adapted for more complex reasoning tasks.

Q & A

What is the main limitation of the reasoning process in large language models (LLMs)?

The main limitation is that the amount of computation spent per token produced is constant, meaning that the system does not adjust the computational resources based on the complexity of the question or problem at hand.

How does human reasoning differ from the reasoning process in LLMs?

Human reasoning involves spending more time on complex problems, with an iterative and hierarchical approach, while LLMs allocate a fixed amount of computation regardless of the question's complexity.

What is the significance of a persistent long-term memory in dialogue systems?

A persistent long-term memory allows dialogue systems to build upon previous information and context, leading to more coherent and informed responses in a conversation.

How does the concept of 'system one' and 'system two' in psychology relate to LLMs?

System one corresponds to tasks that can be done without conscious thought, similar to how LLMs operate on instinctive language patterns. System two involves deliberate planning and thinking, which is something LLMs currently lack but could potentially develop.

What is the proposed blueprint for future dialogue systems?

The proposed blueprint involves a system that thinks about and plans its answer through optimization before converting it into text, moving away from the auto-regressive prediction of tokens.

How does the energy-based model work in the context of dialogue systems?

The energy-based model is a function that outputs a scalar number indicating how good an answer is for a given prompt. The system searches for an answer that minimizes this number, representing a good response.

What is the difference between contrastive and non-contrastive methods in training an energy-based model?

Contrastive methods train the model by showing it pairs of compatible and incompatible inputs and outputs, adjusting the weights to increase the energy for incompatible pairs. Non-contrastive methods, on the other hand, use a regularizer to ensure that the energy is higher for incompatible pairs by minimizing the volume of space that can take low energy.

How does the concept of latent variables play a role in the optimization process of dialogue systems?

Latent variables, or Z in the context of the script, represent an abstract form of a good answer that the system can manipulate to minimize the output energy. This allows for optimization in an abstract representation space rather than directly in text.

What is the main inefficiency in the current auto-regressive language model training?

The main inefficiency is that it involves generating a large number of hypothesis sequences and then selecting the best ones, which is computationally wasteful compared to optimizing in continuous, differentiable spaces.

How does the energy function ensure that a good answer has low energy and a bad answer has high energy?

The energy function is trained to produce low energy for pairs of inputs and outputs (X and Y) that are compatible, based on the training set. A regularizer in the cost function ensures that the energy is higher for incompatible pairs, effectively pushing the energy function down in regions of compatible XY pairs and up elsewhere.

How is the concept of energy-based models applied in visual data processing?

In visual data processing, the energy of the system is represented by the prediction error of the representation when comparing a corrupted version of an image or video to the actual, uncorrupted version. A low energy indicates a good match, while a high energy indicates significant differences.