The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) Are LLMs Good at Causal Reasoning? with Robert Osazuwa Ness - #638
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Jul 17, 2023 In this discussion, Robert Osazuwa Ness, a senior researcher at Microsoft Research, delves into the intriguing world of causal reasoning in large language models like GPT-3.5 and GPT-4. He examines their strengths and limitations, emphasizing the need for proper benchmarks and the importance of domain knowledge in causal analysis. Robert also highlights innovative methods for improving model performance through tailored reinforcement learning techniques and discusses the role of prompt engineering in enhancing causal inference tasks.
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Emergent Causal Reasoning
- LLMs show emergent causal reasoning abilities with increased size, absent in smaller models like GPT-2.
- This emergent behavior appears with models like GPT-3 and 4, marking a shift in LLM capabilities.
LLM-Based Hiring Concerns
- Robert Ness cautions against using LLMs for automated hiring decisions based on resumes.
- LLMs may provide seemingly logical explanations while still being influenced by biases, raising ethical concerns.
Memorization vs. Generalization
- Tübingen benchmark data was found within LLM training data, raising memorization concerns.
- Focus should shift to evaluating LLM generalization beyond benchmarks and handling novel causal relationships.