LLM Contradictions

1. Brief critique and context: The conjecture focuses on the limitations of self-supervised transformer language models, specifically their ability to detect logical contradictions in formal proofs. The challenge lies in the models' training on next-token prediction, which might not adequately capture complex logical structures. Transformer models excel at language tasks but may struggle with formal reasoning without additional logic-specific training or architectures.

2. Recent research: Recent advancements in transformer models have shown improvements in reasoning tasks by incorporating logic-specific training or fine-tuning strategies. For example, OpenAI's work on GPT-4 has explored logical reasoning, but often involves additional training beyond simple next-token prediction. See research on reasoning capabilities: https://arxiv.org/abs/2303.08774

3. Bayesian likelihood of falsification (with reasoning): 30% likelihood of being falsified within 5 years. While there is significant progress in enhancing LLMs' reasoning abilities, achieving 95% accuracy on logical contradictions purely from next-token prediction is challenging. Current strategies often require additional training paradigms or architectures, and it's uncertain if next-token prediction alone can achieve the necessary logical comprehension without such enhancements.