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Are LLMs Good at Causal Reasoning? with Robert Osazuwa Ness - #638

2023-07-17 · 48 min · episode 638 · 13 entities

Asserted relationships

  • evidence rules-v4
    Are LLMs Good at Causal Reasoning? with Robert Osazuwa Ness - #638
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    Feed author/publisher: Sam Charrington
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  • → discusses Technology company
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  • → hosted by TWIML company
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    Feed author/publisher: TWIML

Entities found in this episode

concepts 5

  • mentioned Science concept
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  • mentioned Tech News concept
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    evidence rules-v4
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  • discusses Science concept
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    evidence rules-v4
    Feed category: Science
  • discusses News concept
    0.40 · ×2
    evidence rules-v4
    Feed category: News
  • discusses Tech News concept
    0.40 · ×2
    evidence rules-v4
    Feed category: Tech News

companys 4

  • mentioned TWIML company
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    Feed author/publisher: TWIML
  • mentioned Technology company
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  • discusses Technology company
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  • hosted by TWIML company
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    evidence rules-v4
    Feed author/publisher: TWIML

persons 3

  • mentioned Robert Osazuwa Ness person
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    Are LLMs Good at Causal Reasoning? with Robert Osazuwa Ness - #638
  • mentioned Sam Charrington person
    0.70 · ×2
    evidence rules-v4
    Feed author/publisher: Sam Charrington
  • hosted by Sam Charrington person
    0.55 · ×2
    evidence rules-v4
    Feed author/publisher: Sam Charrington

podcasts 1

Episode description as stored
Today we’re joined by Robert Osazuwa Ness, a senior researcher at Microsoft Research, Professor at Northeastern University, and Founder of Altdeep.ai. In our conversation with Robert, we explore whether large language models, specifically GPT-3, 3.5, and 4, are good at causal reasoning. We discuss the benchmarks used to evaluate these models and the limitations they have in answering specific causal reasoning questions, while Robert highlights the need for access to weights, training data, and architecture to correctly answer these questions. The episode discusses the challenge of generalization in causal relationships and the importance of incorporating inductive biases, explores the model's ability to generalize beyond the provided benchmarks, and the importance of considering causal factors in decision-making processes. The complete show notes for this episode can be found at twimlai.com/go/638.