Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
AI Research
arXiv CS.AI·14d ago·5 min read
The Information Shadow: Measuring Structural Limits on What Language Models Can Learn
TL;DR
This research introduces the concept of the 'information shadow,' highlighting structural limits on what language models can learn from text. It identifies three types of knowledge gaps that persist regardless of data scale, emphasizing the inherent constraints of language-based learning.
✦ Why It Matters
Evaluate your language model's capabilities against these structural limits to optimize training strategies.
Key Takeaways
- Assess the structural limits of your language models.
- Use Language Compression Residuals to evaluate expressibility.
- Implement Counterfactual Distinction Tests to understand model biases.
- Consider data channel properties when training models.
How It Works
The concept of the information shadow is explored through specific probes that reveal the limitations of text-based learning. For Type I, Language Compression Residuals measure the gap between a text learner and a full-signal learner, demonstrating that the text learner's performance is constrained by the lossy nature of text encoding.
For Type II, the Counterfactual Distinction Test shows how models trained on conflicting rules can exhibit behavior dictated by their inductive biases, highlighting the importance of understanding model architecture.