In episode 65 of The Gradient Podcast, Daniel Bashir speaks to Sewon Min.

Sewon is a fifth-year PhD student in the NLP group at the University of Washington, advised by Hannaneh Hajishirzi and Luke Zettlemoyer. She is a part-time visiting researcher at Meta AI and a recipient of the JP Morgan PhD Fellowship. She has previously spent time at Google Research and Salesforce research.

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Outline:

* (00:00) Intro

* (03:00) Origin Story

* (04:20) Evolution of Sewon’s interests, question-answering and practical NLP

* (07:00) Methodology concerns about benchmarks

* (07:30) Multi-hop reading comprehension

* (09:30) Do multi-hop QA benchmarks actually measure multi-hop reasoning?

* (12:00) How models can “cheat” multi-hop benchmarks

* (13:15) Explicit compositionality

* (16:05) Commonsense reasoning and background information

* (17:30) On constructing good benchmarks

* (18:40) AmbigQA and ambiguity

* (22:20) Types of ambiguity

* (24:20) Practical possibilities for models that can handle ambiguity

* (25:45) FaVIQ and fact-checking benchmarks

* (28:45) External knowledge

* (29:45) Fact verification and “complete understanding of evidence”

* (31:30) Do models do what we expect/intuit in reading comprehension?

* (34:40) Applications for fact-checking systems

* (36:40) Intro to in-context learning (ICL)

* (38:55) Example of an ICL demonstration

* (40:45) Rethinking the Role of Demonstrations and what matters for successful ICL

* (43:00) Evidence for a Bayesian inference perspective on ICL

* (45:00) ICL + gradient descent and what it means to “learn”

* (47:00) MetaICL and efficient ICL

* (49:30) Distance between tasks and MetaICL task transfer

* (53:00) Compositional tasks for language models, compositional generalization

* (55:00) The number and diversity of meta-training tasks

* (58:30) MetaICL and Bayesian inference

* (1:00:30) Z-ICL: Zero-Shot In-Context Learning with Pseudo-Demonstrations

* (1:02:00) The copying effect

* (1:03:30) Copying effect for non-identical examples

* (1:06:00) More thoughts on ICL

* (1:08:00) Understanding Chain-of-Thought Prompting

* (1:11:30) Bayes strikes again

* (1:12:30) Intro to Sewon’s text retrieval research

* (1:15:30) Dense Passage Retrieval (DPR)

* (1:18:40) Similarity in QA and retrieval

* (1:20:00) Improvements for DPR

* (1:21:50) Nonparametric Masked Language Modeling (NPM)

* (1:24:30) Difficulties in training NPM and solutions

* (1:26:45) Follow-on work

* (1:29:00) Important fundamental limitations of language models

* (1:31:30) Sewon’s experience doing a PhD

* (1:34:00) Research challenges suited for academics

* (1:35:00) Joys and difficulties of the PhD

* (1:36:30) Sewon’s advice for aspiring PhDs

* (1:38:30) Incentives in academia, production of knowledge

* (1:41:50) Outro

Links:

* Sewon’s homepage and Twitter

* Papers

* Solving and re-thinking benchmarks

* Multi-hop Reading Comprehension through Question Decomposition and Rescoring / Compositional Questions Do Not Necessitate Multi-hop Reasoning

* AmbigQA: Answering Ambiguous Open-domain Questions

* FaVIQ: FAct Verification from Information-seeking Questions

* Language Modeling

* Rethinking the Role of Demonstrations

* MetaICL: Learning to Learn In Context

* Towards Understanding CoT Prompting

* Z-ICL: Zero-Shot In-Context Learning with Pseudo-Demonstrations

* Text representation/retrieval

* Dense Passage Retrieval

* Nonparametric Masked Language Modeling

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