Mukund Srinath @ EMNLP
@MukundSrinath3
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Machine Learning Scientist @ Expedia Group | NLP, IR and Trustworthy AI
Joined September 2020
Who knew that writing a paper saying that the field of sentiment analysis in #NLProc is broken, can land you an OUTSTANDING PAPER AWARD!! 🎉🎉🎉 in the the sentiment analysis track at #EMNLP2023!!
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RT @vipul_1011: Wohhoo, our work got accepted at NAACL 2025! 🥳 TLDR: we built a methodology to improve reliability of any dataset by selec…
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RT @PranavVenkit: That's a wrap for paper #1! 🎉 Thank you @MukundSrinath3 and @koustavagoswami for co-presenting. Loved all the interaction…
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RT @Sanjana08395511: For all Future PhDs @ #CSCW2024! 🎉 For anyone considering a PhD, check out my latest blog with tips on starting your a…
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RT @Sanjana08395511: 👋🏻 Excited for Day 1 at #CSCW2024! Can’t wait to connect with friends old and new. If you’re interested in #HumanCente…
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RT @Sanjana08395511: While at #CSCW2024 , I'm also happy to chat about fun things to do around! Costa Rica has been beautiful so far! https…
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RT @ruchipanch: Excited to share that my first first-author full paper with @guo_freeman has been accepted to #chiplay2024! In this paper,…
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I had such a great time presenting at the #HCIplusNLPWorkshop at #NAACL2024! Huge thanks to @sulin_blodgett and @CurriedAmanda for putting together such a seamless and insightful workshop experience! @Sanjana08395511 #NLProc #HCI
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RT @vipul_1011: Finally in 5th (maybe 6th?) attempt over past 16 months, my paper has been accepted! It's been a marathon, but now I can p…
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RT @Sanjana08395511: It is really motivating to see that the work O am doing is relevant and useful to the community. Although citations do…
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TableLlama achieves comparable/better performance on in-domain tasks compared to other SOTA models. TableLlama archives a significant gain in performance on six out-of-domain datasets.
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Takeaways: - LLMs possess some degree of reasoning, but a lot of it comes from instance-level and task-level overfitting - In-context learning doesn’t fully bridge the gap either (Example on the addition task in a few-shot setting in different bases shown in the image)
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The approach is best explained using an example: - GPT-3 achieves perfect performance on two-digit addition - However, if the performance is not comparable in a less popular counterfactual setting (base-9 vs base-10), it might indicate memorization of the popular setting.
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LLMs seem to achieve super-human performance on several tasks but also fail on a number of basic problems. This paper probes whether LLM performance is simply a function of instance/task overfitting or if LLMs are reasoning over data.
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