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Amanda Bertsch

@abertsch.bsky.social
2.2K followers 546 following 20 posts

PhD student @ CMU LTI. working on text generation + long context www.cs.cmu.edu/~abertsch

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Reposted by Amanda Bertsch
Ari @ari-holtzman.bsky.social · 09/11/2025
LLMs don't accumulate information over the course of a text the way you'd hope! I think this is why LLMs often feel 'fixated on the wrong thing' or 'overly literal'—they are usually responding using the most relevant single thing they remember, not the aggregate of what was said
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Amanda Bertsch @abertsch.bsky.social · 07/11/2025
Can LLMs accurately aggregate information over long, information-dense texts? Not yet… We introduce Oolong, a dataset of simple-to-verify information aggregation questions over long inputs. No model achieves >50% accuracy at 128K on Oolong!
Performance of a sweep of models on Oolong-synth and Oolong-real. Performance decreases with increasing context length, sometimes steeply.
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Kyle Lo @ ICML2026 🇰🇷 @kylelo.bsky.social · 05/11/2025
why intern at Ai2? 🐟interns own major parts of our model development, sometimes even leading whole projects 🐡we're committed to open science & actively help our interns publish their work reach out if u wanna build open language models together 🤝 links 👇
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Sung Kim @sungkim.bsky.social · 05/11/2025
DeltaNet Explained by Sonlin Yang A gentle and comprehensive introduction to the DeltaNet Part 1: sustcsonglin.github.io/blog/2024/de... Part 2: sustcsonglin.github.io/blog/2024/de... Part 3: sustcsonglin.github.io/blog/2024/de...
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Natasha Johnson @natashamarie330.bsky.social · 05/11/2025
I’ll be presenting this work in **2 hours** at EMNLP’s Gather Session 3. Come by to chat about fanfiction, literary notions of similarity, long-context modeling, and consent-focused data collection!
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Natasha Johnson @natashamarie330.bsky.social · 05/11/2025
Digital humanities researchers often care about fine-grained similarity based on narrative elements like plot or tone, which don’t necessarily correlate with surface-level textual features. Can embedding models capture this? We study this in the context of fanfiction!
Figure showing a similarity comparison between three stories. Story A and story B have the same author, and story A and story C have the same tone. A human might care about which stories are tonally the most similar, but a language model's notion of similarity is strongly informed by surface-level features like small differences in writing style across authors.
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Amanda Bertsch @abertsch.bsky.social · 12/09/2025
. @gneubig.bsky.social and I are co-teaching a new class on LM inference this fall! We designed this class to give a broad view on the space, from more classical decoding algorithms to recent methods for LLMs, plus a wide range of efficiency-focused work. website: phontron.com/class/lminfe...
phontron.com
11-664/763 LM Inference
A class at Carnegie Mellon University on language model inference algorithms.
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Lindia Tjuatja @lindiatjuatja.bsky.social · 09/06/2025
When it comes to text prediction, where does one LM outperform another? If you've ever worked on LM evals, you know this question is a lot more complex than it seems. In our new #acl2025 paper, we developed a method to find fine-grained differences between LMs: 🧵1/9
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Amanda Bertsch @abertsch.bsky.social · 30/04/2025
super excited to see folks at #NAACL25 this week! I'll be presenting our work on long-context ICL Wednesday in the 2pm poster session in Hall 3-- would love to chat with folks there or at the rest of the conference about long context data, ICL, inference time methods, New Mexican food, etc :)
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Alicia DeVrio @uhleeeeeeeshuh.bsky.social · 06/03/2025
How can we better think and talk about human-like qualities attributed to language technologies like LLMs? In our #CHI2025 paper, we taxonomize how text outputs from cases of user interactions with language technologies can contribute to anthropomorphism. arxiv.org/abs/2502.09870 1/n
Image of the first page of the CHI 2025 paper titled "A Taxonomy of Linguistic Expressions That Contribute To Anthropomorphism of Language Technologies" by authors Alicia DeVrio, Myra Cheng, Lisa Egede, Alexandra Olteanu, & Su Lin Blodgett
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Max Müller-Eberstein @mxij.me · 23/12/2024
9.6 million seconds = 1 PhD 🔥 Finally analyzed my PhD time tracking data so you can plan your own research journey more effectively: mxij.me/x/phd-learning-dynamics For current students: I hope this helps put your journey into perspective. Wishing you all the best!
mxij.me
The Learning Dynamics of a PhD
This is what a PhD looks like: 9.6 million seconds of research.
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Sireesh Gururaja @siree.sh · 17/12/2024
When I started on ARL project that funds my PhD, the thing we were supposed to build was a "MaterialsGPT". What is a MaterialsGPT? Where does that idea come from? I got to spend a lot of time thinking about that second question with @davidthewid.bsky.social and Lucy Suchman (!) working on this:
The abstract of a paper titled "Basic Research, Lethal Effects: Military AI Research Funding as Enlistment".

In the context of unprecedented U.S. Department of Defense (DoD) budgets, this paper examines the recent history of DoD funding for academic research in algorithmically based warfighting. We draw from a corpus of DoD grant solicitations from 2007 to 2023, focusing on those addressed to researchers in the field of artificial intelligence (AI). Considering the implications of DoD funding for academic research, the paper proceeds through three analytic sections. In the first, we offer a critical examination of the distinction between basic and applied research, showing how funding calls framed as basic research nonetheless enlist researchers in a war fighting agenda. In the second, we offer a diachronic analysis of the corpus, showing how a 'one small problem' caveat, in which affirmation of progress in military technologies is qualified by acknowledgement of outstanding problems, becomes justification for additional investments in research. We close with an analysis of DoD aspirations based on a subset of Defense Advanced Research Projects Agency (DARPA) grant solicitations for the use of AI in battlefield applications. Taken together, we argue that grant solicitations work as a vehicle for the mutual enlistment of DoD funding agencies and the academic AI research community in setting research agendas. The trope of basic research in this context offers shelter from significant moral questions that military applications of one's research would raise, by obscuring the connections that implicate researchers in U.S. militarism.
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🌶 David Gray Widder @davidthewid.bsky.social · 09/12/2024
📢 NEW Paper! @siree.sh, Lucy Suchman, and I examine a corpus of 7,000 US Military grant solicitations to ask what the world’s largest military wants with to do with AI, by looking at what it seeks to fund. #STS 📄: arxiv.org/pdf/2411.17840 We find…
Basic Research, Lethal Effects: Military AI Research Funding as Enlistment David Gray Widder Digital Life Initiative, Cornell University Sireesh Gururaja School of Computer Science, Carnegie Mellon University Lucy Suchman Department of Sociology, Lancaster University Abstract In the context of unprecedented U.S. Department of Defense (DoD) budgets, this paper examines the recent history of DoD funding for academic research in algorithmically based warfighting. We draw from a corpus of DoD grant solicitations from 2007 to 2023, focusing on those addressed to researchers in the field of artificial intelligence (AI). Considering the implications of DoD funding for academic research, the paper proceeds through three analytic sections. In the first, we offer a critical examination of the distinction between basic and applied research, showing how funding calls framed as basic research nonetheless enlist researchers in a war fighting agenda. In the second, we offer a diachronic analysis of the corpus, showing how a ‘one small problem’ caveat, in which affirmation of progress in military technologies is qualified by acknowledgement of outstanding problems, becomes justification for additional investments in research. We close with an analysis of DoD aspirations based on a subset of Defense Advanced Research Projects Agency (DARPA) grant solicitations for the use of AI in battlefield applications. Taken together, we argue that grant solicitations work as a vehicle for the mutual enlistment of DoD funding agencies and the academic AI research community in setting research agendas. The trope of basic research in this context offers shelter from significant moral questions that military applications of one’s research would raise, by obscuring the connections that implicate researchers in U.S. militarism. Keywords: artificial intelligence; US Department of Defense; military; funding; investment, war
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Vicki @vickiboykis.com · 20/11/2024
when you try to convert your text into smaller pieces but all it gives you is Elvish, that’s a tolkienizer
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Charles Sutton @randomlywalking.bsky.social · 21/11/2024
That’s right. You might think that all successful CS academics are good at running. But that’s only because the ones who weren’t, have been eaten by bears.
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Lindia Tjuatja @lindiatjuatja.bsky.social · 20/11/2024
💬 Have you or a loved one compared LM probabilities to human linguistic acceptability judgments? You may be overcompensating for the effect of frequency and length! 🌟 In our new paper, we rethink how we should be controlling for these factors 🧵:
Screenshot of the paper title "What Goes Into a LM Acceptability Judgment? Rethinking the Impact of Frequency and Length"
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Sireesh Gururaja @siree.sh · 12/11/2024
I'm keeping track of people at the CMU Language Technologies Institute here: go.bsky.app/NhTwCVb. Follow along!
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Shaily @shaily99.bsky.social · 14/11/2024
Today is the day!! Find me at 2 PM in the Jasmine Hall (the one on the floor near food).
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Lindia Tjuatja @lindiatjuatja.bsky.social · 08/11/2024
(Hehe first bsky post!) I'll be at #EMNLP2024 💃🌴! Happy to chat about (among other things): ✨linguistically+cognitively motivated evaluation ✨NLP for low-resource+endangered languages ✨figuring out what features of language data LMs are *actually* learning I'll be presenting two posters 🧵:
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Naomi Saphra @nsaphra.bsky.social · 08/11/2024
Taking a stand that we aren’t doing the #nlproc tag here. It’s #nlp. We used #nlproc because a decade ago the #nlp tag was full of sleazy scammers selling guides for hypnotizing women into sleeping with you. But guess what? We won. All the sleazy scammers are doing natural language processing now.
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Clara Na @clarana.bsky.social · 05/11/2024
Building/customizing your own LLM? You'll want to curate training data for it, but how do you know what makes the data good? You can try out recipes👩‍🍳 iterate on ✨vibes✨ but we can't actually test all possible combos of tweaks,,, right?? 🙅‍♂️WRONG! arxiv.org/abs/2410.15661 (1/n) 🧵
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Clara Na @clarana.bsky.social · 12/10/2023
bsky.app/profile/sire... it was so interesting to see participants' perceptions of "paradigm shifts" paralleled across eras and cycles of NLP, and at the same time nothing until recent years had reached quite the level of *47%* of ACL papers in 2021 citing BERT
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Sireesh Gururaja @siree.sh · 12/10/2023
We all know that “recently large language models have”, “large language models are”, and “large language models can.” But *why* LLMs? How did we get here? (where is “here”?) What forces are shaping NLP, and how recent are they, actually? To appear at EMNLP 2023: arxiv.org/abs/2310.07715
Screenshot of paper title: "To Build Our Future, We Must Know Our Past: Contextualizing Paradigm Shifts in Natural Language Processing"
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Gretchen McCulloch @gretchenmcculloch.com · 11/10/2023
Talking to the youths in 2023: did you know that "podcast" comes from a pun on "broadcast" plus the Apple iPod, a precursor to the iPhone that only played music Talking to the youths in 2043: did you know that "tweet" comes from a pun on Twitter, a precursor to various shortform social media
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