Sewon Min
Assistant Professor
Studies large language models, including retrieval-augmented and nonparametric language models.
Sewon Min's lab at UC Berkeley works on large language models and knowledge, QA & retrieval. 37 papers since 2023, 27 at top venues, mostly ICLR and NeurIPS. 4 PhD students. Applications close .
At a glance
- papers, 2023–2026
- 10/ year
- at T1 venues
- 73%
- PhD students
- 4
- MS students
- 0
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 17 papers2026 · T1 14 · collecting
- 5 papers2025 · T1 3
- 8 papers2024 · T1 6
- 7 papers2023 · T1 4
T1 venuesOtherVenues: ICLR, NeurIPS, ICML, EMNLP, ACL
Research focuspaper titles that use each topic's words
- Large language models17 papers
- Knowledge, QA & retrieval7 papers
- nonparametric2 papers
- retrieval-augmented1 paper
- studies large0 papers
Paper list
From the lab homepage.
202617 papers
- NeurIPS (T1)Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale (opens in a new tab)
- ICLR (T1)Constantly Improving Image Models Need Constantly Improving Benchmarks (opens in a new tab)
- ICML (T1)DR Tulu: Reinforcement Learning with Evolving Rubrics for Deep Research (opens in a new tab)
- Proceedings of AAAI DemoDS SERVE: A Framework for Efficient and Scalable Neural Retrieval. (opens in a new tab)
- NeurIPS (T1)EMO: Pretraining Mixture of Experts for Emergent Modularity (opens in a new tab)
- ICML (T1)FrontierCS: Evolving Challenges for Evolving Intelligence (opens in a new tab)
- ICLR (T1)Frustratingly Simple Retrieval Improves Challenging, Reasoning-Intensive Benchmarks (opens in a new tab)
- Proceedings of MLSys Best Paper AwardLEANN: A Low-Storage Vector Index (opens in a new tab)
- NeurIPS (T1)No More Free Lunch: Corpus Task Complexity Matters as Corpora Grow (opens in a new tab)
- EMNLP (T1)RAG over Thinking Traces Can Improve Reasoning Tasks (opens in a new tab)
- ICLR (T1)Reliable Fine-Grained Evaluation of Natural Language Math Proofs (opens in a new tab)
- ICML (T1)Residual Context Diffusion Language Models (opens in a new tab)
20255 papers
- NeurIPS (T1)FlexOlmo: Open Language Models for Flexible Data Use (opens in a new tab)
- ICLR (T1)OLMoE: Open Mixture-of-Experts Language Models (opens in a new tab)
- Proceedings of ACL demo Best Demo PaperOLMoTrace: Tracing Language Model Outputs Back to Trillions of Training Tokens (opens in a new tab)
- ICML (T1)Organize the Web: Constructing Domains Enhances Pre-Training Data Curation (opens in a new tab)
- Proceedings of COLMReasonIR: Training Retrievers for Reasoning Tasks (opens in a new tab)
20248 papers
- ICLR (T1)BTR: Binary Token Representations for Efficient Retrieval Augmented Language Models (opens in a new tab)
- EMNLP (T1)CopyBench: Measuring Literal and Non-Literal Reproduction of Copyright-Protected Text in Language Model Generation (opens in a new tab)
- Proceedings of COLMDo Membership Inference Attacks Work on Large Language Models? (opens in a new tab)
- ICLR (T1)In-Context Pretraining: Language Modeling Beyond Document Boundaries (opens in a new tab)
- Proceedings of COLMInfini-gram: Scaling Unbounded n-gram Language Models to a Trillion Tokens (opens in a new tab)
- NAACL (T1)REPLUG: Retrieval-Augmented Black-Box Language Models (opens in a new tab)
- NeurIPS (T1)Scaling Retrieval-Based Language Models with a Trillion-Token Datastore (opens in a new tab)
- ICLR (T1)SILO Language Models: Isolating Legal Risk in a Nonparametric Datastore (opens in a new tab)
20237 papers
- ACL (T1)CREPE: Open-Domain Question Answering with False Presuppositions (opens in a new tab)
- EMNLP (T1)FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation (opens in a new tab)
- TACL (T1)INSCIT: Information-Seeking Conversations with Mixed-Initiative Interactions
- Findings of EMNLPMeasuring and Narrowing the Compositionality Gap in Language Models (opens in a new tab)
- Findings of ACLNonparametric Masked Language Modeling (opens in a new tab)
- Proceedings of ACL (long) Best Paper Honorable MentionTowards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters (opens in a new tab)
- ACL (T1)Z-ICL: Zero-Shot In-Context Learning with Pseudo-Demonstrations (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to UC BerkeleyFall 2027
UC Berkeley EECS Computer Science PhD
- Round
- Single round
- Opens
- —
- GRE
- not required
- English
- TOEFL/IELTS (non-English-country degrees)
- Fee
- See program page
Confirmed on the program page.
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