Mayee Chen (@MayeeChen) / X

Mayee Chen

677 posts

Mayee Chen

@MayeeChen

Research @EngramLab. CS PhD @StanfordAILab @HazyResearch, undergrad @princeton. Working on all things data! she/her 🎃

Stanford, CA

Joined February 2020

Pinned
Mayee Chen
@MayeeChen
Jun 23
I've spent my PhD figuring out how a model learns from data, digging through piles of Spanish math problems, fanfiction, and the depths of StackExchange. Engram opens a new dimension: how a model learns from you - your world, how it evolves, and the threads that tie it all
Engram
@EngramLab
Jun 23
Article
Introducing Engram: Scaling compute on your context
We’re Engram. We’re building AI that learns from you and deeply understands your work. Today’s AI models don’t understand what you do. Not really. Everything models know comes from their training –...
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Mayee Chen
@MayeeChen
Jul 29, 2023
Large language models (LMs) rely heavily on training data quality. How do we best select training data for good downstream model performance across tasks? Introducing 🍳Skill-It: a data-driven framework for understanding and training LMs! Paper: arxiv.org/abs/2307.14430 1/13
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Mayee Chen
@MayeeChen
Oct 15, 2023
Stanford CS graduate students are running an application support program for diversity students (broadly defined); if you are applying to our CS PhD program, we'll give one round of feedback on your application. SPACE IS LIMITED. APPLY BY OCTOBER 27: Link: tinyurl.com/3x5ke5uz
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Mayee Chen
@MayeeChen
Apr 19, 2022
New preprint alert! 📣 How do we produce transferable and robust representations with supervised contrastive learning? We need *geometric spread* and an inductive bias towards *latent subclass clustering* in representation space. 📜 arxiv.org/abs/2204.07596 👇 (1/n)
Mayee Chen
@MayeeChen
Jun 24, 2025
LLMs often generate correct answers but struggle to select them. Weaver tackles this by combining many weak verifiers (reward models, LM judges) into a stronger signal using statistical tools from Weak Supervision—matching o3-mini-level accuracy with much cheaper models! 📊
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Mayee Chen
@MayeeChen
Nov 12, 2024
There are many algorithms for constructing pre-training data mixtures—which one should we use? Turns out: many of them fall under one framework, have similar issues, and can be improved with a straightforward modification. Introducing Aioli! 🧄 1/9
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Mayee Chen
@MayeeChen
Apr 22, 2025
!!! I'm at #ICLR2025 to present 🧄Aioli🧄 a unified framework for data mixing on Thursday afternoon! 🔗 arxiv.org/abs/2411.05735 Message me to chat about pre/post training data (mixing, curriculum, understanding); test-time compute/verification; or to try new food 🇸🇬
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Mayee Chen
@MayeeChen
Jul 2, 2021
New paper appearing in #ICML2021! Mandoline: Model Evaluation under Distribution Shift: Paper: arxiv.org/abs/2107.00643 Code: github.com/HazyResearch/m… work done w/ equal contribution from @krandiash and @nimit_sohoni , as well as @faitpoms, @kayvonf, and @HazyResearch 1/6
Mayee Chen
@MayeeChen
Oct 31, 2023
Skill-It has been accepted to #NeurIPS2023 as a spotlight! Want to understand how skills in your training data can give way to better data selection methods? Our code is available here: github.com/HazyResearch/s…
Mayee Chen
@MayeeChen
Jul 29, 2023
Large language models (LMs) rely heavily on training data quality. How do we best select training data for good downstream model performance across tasks? Introducing 🍳Skill-It: a data-driven framework for understanding and training LMs! Paper: arxiv.org/abs/2307.14430 1/13
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Mayee Chen
@MayeeChen
Dec 10, 2024
Given open-ended generations from K different LLMs for an input, can we learn to select the best generation, without needing any labeled data? Introducing our #NeurIPS2024 paper, Smoothie, a label-free test-time LLM routing algorithm! 🥤 1/4
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Mayee Chen
@MayeeChen
Oct 3, 2024
honored to have contributed to this incredibly exciting work! Archon shows how intelligently blending concepts like ensembling, fusing, and repeated sampling can create very strong LLM inference systems, using 70B+ open models to outperform GPT-4o and Claude 3.5 sonnet!
Jon Saad-Falcon
@JonSaadFalcon
Sep 30, 2024
What is the best way to spend your inference compute budget to create LLM systems greater than the sum of their parts? In our latest paper, we present Archon, an architecture search framework for inference-time techniques! Archon is enabled by inference-time architecture search
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Mayee Chen
@MayeeChen
Aug 15, 2023
Embroid's ability to improve LLM performance across 95 classification tasks is quite impressive, and it does so just by exploiting smoothness of other pre-trained model embeddings via knn (with theoretical guarantees too). Amazing work by @NeelGuha and honored to have helped out!
Neel Guha
@NeelGuha
Aug 14, 2023
We’re excited to share Embroid: a method for “stitching” together an LLM with embedding information from multiple smaller models (e.g., BERT), allowing us to automatically correct LLM predictions without supervision. ✍️: hazyresearch.stanford.edu/blog/2023-08-1… 📜: arxiv.org/abs/2307.11031
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Mayee Chen
@MayeeChen
Dec 10, 2024
Omw to #NeurIPS2024! I have 2 papers: - Smoothie (w @NeelGuha): a test-time LLM routing approach that requires no labels/training (Thu 4:30 East) - DCLM: a benchmark for LLM data curation (Fri 4:30 West) Excited to meet new ppl & chat about data-centric AI/ test-time approaches!
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Mayee Chen
@MayeeChen
Dec 12, 2023
I'm at #NeurIPS2023 from now to Sat and will be around at these posters: - Embroid Tues 5:15 - Skill-It Wed 10:45 - Segmentation for classification Wed 5:00 Let's chat about training data and data-centric frameworks for understanding/aligning LLMs! DM/email me 🍤 Paper details 👇
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