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Chaitanya Malaviya
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Chaitanya Malaviya
@cmalaviya11
Senior research scientist @GoogleDeepMind | benchmarking and evaluation | prev @upennnlp @allen_ai @GoogleDeepMind and @LTIatCMU
Seattle, WA
chaitanyamalaviya.github.io
Joined September 2023
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    Chaitanya Malaviya
    @cmalaviya11
    Jun 6, 2025
    Ever wondered what makes language models generate overly verbose, vague, or sycophantic responses? Our new paper investigates these and other idiosyncratic biases in preference models, and presents a simple post-training recipe to mitigate them! Thread below 🧵↓
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    Chaitanya Malaviya
    @cmalaviya11
    Jul 30, 2025
    People at #ACL2025, come drop by our poster today & chat with me about how context matters for reliable language model evaluations! Jul 30, 11:00-12:30 at Hall 4X, board 424.
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    Chaitanya Malaviya
    @cmalaviya11
    Nov 13, 2024
    Excited to share ✨ Contextualized Evaluations ✨! Benchmarks like Chatbot Arena contain underspecified queries, which can lead to arbitrary eval judgments. What happens if we provide evaluators with context (e.g who's the user, what's their intent) when judging LM outputs? 🧵↓
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    Chaitanya Malaviya
    @cmalaviya11
    Jul 22, 2025
    Context is an overlooked aspect of language model evaluations. Check out how to incorporate context into evaluations in our TACL paper, how it changes evaluation conclusions and makes evaluation more reliable!
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    Ai2
    @allen_ai
    Jul 22, 2025
    In our new paper, “Contextualized Evaluations: Judging Language Model Responses to Underspecified Queries,” we find that adding just a bit of missing context can reorder model leaderboards—and surface hidden biases. 🧵👇
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    Chaitanya Malaviya
    @cmalaviya11
    Jun 9, 2025
    Thanks for the mention @natolambert :) shoutout to the amazing undergrad @abharadwaj123 who led this work!
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    Nathan Lambert
    @natolambert
    Jun 9, 2025
    Nice to see folks studying biases in RLHF / preference tuning all the way down to the datasets. I think many of the biases are mostly irreducible human biases that can't be solved within current training regimes, just mitigated.
  • user avatar
    Chaitanya Malaviya
    @cmalaviya11
    Nov 13, 2024
    Excited to share ✨ Contextualized Evaluations ✨! Benchmarks like Chatbot Arena contain underspecified queries, which can lead to arbitrary eval judgments. What happens if we provide evaluators with context (e.g who's the user, what's their intent) when judging LM outputs? 🧵↓
    Image

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