Alane Suhr
Assistant Professor
Builds systems that use language to interact with people and learn language through interaction.
Alane Suhr's lab at UC Berkeley works in Natural Language Processing. 26 papers since 2023, 20 at top venues, mostly ICML and EMNLP. Applications close .
At a glance
- papers, 2023–2026
- 7/ year
- at T1 venues
- 77%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 8 papers2026 · T1 5 · collecting
- 5 papers2025 · T1 3
- 8 papers2024 · T1 7
- 5 papers2023 · T1 5
T1 venuesOtherVenues: ICML, EMNLP, NeurIPS, ICLR, CVPR
Research focuspaper titles that use each topic's words
- use1 paper
- interact0 papers
- builds systems0 papers
- learn0 papers
- people0 papers
Paper list
From the lab homepage.
20268 papers
- COLMDcoupling planning and control for instructable agents.
- EMNLP (T1)DigitalCoach: Communication and grounding gaps in human and agentic computer use coaching. (opens in a new tab)
- COLMLearning to refer from estimated listener gaze.
- Findings of ACLLinear script representations in speech foundation models enable zero-shot transliteration. (opens in a new tab)
- ICLR (T1)Long chain-of-thought reasoning across languages. (opens in a new tab)
- ICML (T1)Representational similarity and model behavior in multi-agent interaction. (opens in a new tab)
- ICML (T1)ThreadWeaver: Adaptive threading for efficient parallel reasoning in language models. (opens in a new tab)
- ICML (T1)V1: Unifying generation and self-verification for parallel reasoners. (opens in a new tab)
20255 papers
- CVPR (T1)AutoPresent: Designing structured visuals from scratch. (opens in a new tab)
- CVPR (T1)Evaluating model perception of color illusions in photorealistic scenes. (opens in a new tab)
- COLMLearning adaptive parallel reasoning with language models. (opens in a new tab)
- ICML (T1)Training software engineering agents and verifiers with SWE-Gym. (opens in a new tab)
- Workshop on What is Next in Multimodal Foundation Models? (MMFM4) at ICCVTULIP: Towards unified language-image pretraining. (opens in a new tab)
20248 papers
- COLMAutonomous evaluation and refinement of digital agents (opens in a new tab)
- NeurIPS (T1)DigiRL: Training in-the-wild device-control agents with autonomous reinforcement learning (opens in a new tab)
- NeurIPS (T1)Fine-tuning large vision-language models as decision-making agents via reinforcement learning (opens in a new tab)
- EMNLP (T1)Grounding language in multi-perspective referential communication. (opens in a new tab)
- ICLR (T1)Quantifying language models' sensitivity to spurious features in prompt design or: How I learned to start worrying about prompt formatting (opens in a new tab)
- NAACL (T1)UNcommonsense reasoning: Abductive reasoning about uncommon situations (opens in a new tab)
- EMNLP (T1)Using language models to disambiguate lexical choices in translation. (opens in a new tab)
- ICLR (T1)What's in my big data? (opens in a new tab)
20235 papers
- NeurIPS (T1)Continual learning for instruction following from realtime feedback. (opens in a new tab)
- ICML (T1)Do embodied agents dream of pixelated sheep?: Embodied decision making using language guided world modelling. (opens in a new tab)
- NeurIPS (T1)Fine-grained human feedback gives better rewards for language model training. (opens in a new tab)
- ACL (T1)Minding language models' (lack of) theory of mind: A plug-and-play multi-character belief tracker. (opens in a new tab)
- EMNLP (T1)We're afraid language models aren't modeling ambiguity. (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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Your notes
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Data
Some numbers could not be verified:
- The member list could not be found on the lab site.
- The alumni list could not be found on the lab site.
- Collected
- Verified
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- The lab homepage
- Members
- Not collected
- Alumni
- Not collected
Tiers are venue classifications, not lab rankings.
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