Stanford CS224N NLP with Deep Learning | Winter 2021 | Lecture 18 - Future of NLP + Deep Learning | Summary and Q&A

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October 29, 2021
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Stanford CS224N NLP with Deep Learning | Winter 2021 | Lecture 18 - Future of NLP + Deep Learning

TL;DR

This lecture discusses the recent advancements in neural NLP, including extremely large language models like GPT-3. It highlights the importance of compositionality and generalization in these models and explores the limitations in evaluating their task performance. The lecture concludes with a discussion on the need to move beyond text-based language learning and practical tips for neural NLP research.

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Questions & Answers

Q: How can extremely large language models like GPT-3 learn to perform non-trivial tasks with just a few demonstrations?

GPT-3's ability to perform non-trivial tasks with few demonstrations is a result of its training on massive amounts of unlabeled data. By converting data into sequences of integers, defining loss functions, and training on ample data, these models can learn complex tasks even with minimal input.

Q: What are the limitations of GPT-3 and similar models in terms of logical and mathematical reasoning?

GPT-3 struggles with logical and mathematical reasoning tasks that involve multiple steps of reasoning. It is not adept at arithmetic, solving work problems, or making analogies. These limitations stem from the models' lack of explicit logical reasoning and systematic generalization.

Q: How can we make neural language models exhibit human-like generalization and systematicity?

Achieving human-like generalization and systematicity in neural language models requires further research and exploration. Researchers can focus on refining evaluation benchmarks, introducing curriculum learning and reinforcement learning techniques, and incorporating other modalities in training to improve the models' understanding and generalization capabilities.

Q: Are there attempts to crowdsource dynamic benchmarks for evaluation and online learning in NLP?

Yes, dynamic benchmarks are being created by deploying models to platforms where humans can interact with them and provide feedback. Crowdsourcing is employed to generate new examples that the model fails to classify but humans can understand. Platforms like Dynabench facilitate the construction of ever-changing test sets for better evaluation of model performance.

Summary & Key Takeaways

  • The lecture begins with announcements regarding project milestones and reports. It then delves into the topic of extremely large language models and their popularity.

  • The speaker emphasizes the importance of compositionality and generalization of neural models and discusses the challenges in evaluating their real-world task performance.

  • The need to go beyond text-based language learning and explore other modalities is highlighted, along with practical tips for neural NLP research and the final project.

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