The Gradient: Perspectives on AI Sewon Min: The Science of Natural Language
Benchmarks Can Mislead Progress
- Benchmarks often incentivize solving the benchmark rather than the real-world problem they intended to measure.
- Sewon Min argues we should build benchmarks from real user problems to reveal meaningful challenges.
Multi-Hop Datasets Hide Shortcuts
- Many multi-hop QA datasets contain shortcuts so single-hop strategies succeed despite compositional questions.
- Min shows that dataset construction often lets models avoid true multi-step reasoning.
Design Benchmarks From Real Questions
- Build benchmarks from real user tasks to surface authentic challenges rather than synthetic ones.
- Examine real queries carefully to discover hidden difficulties before designing datasets.
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
* Nonparametric Masked Language Modeling
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