Soham De @actuallysoham
PhD student @UW | algorithms & AI for pro-social objectives Ex- @AIatMeta @MSFTResearch, @umsi, @ashokauniv Also on https://t.co/7DkUJLjFgm sohamde.in Seattle, WA Joined June 2020-
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@PrernaRavi1 @MSFTResearch @halfak @agrima_seth Congratulations!! Would love to chat if you’re around UW sometime!
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Soham De retweeted
Could social media make us less polarized instead of more? We tested 5 algorithms on 3 platforms with 10,000 people for 6 months during the 2024 election, and found that the answer is yes. 🧵
Soham De retweeted
🚨🎉Excited to announce that our paper “Grok in the Wild: Characterizing the Roles and Uses of Large Language Models on Social Media” is accepted at @icwsm 2026! In this paper, we investigate how, when, and to what effect Grok is used on X. 🧵1/n thread
@agdhruv I had no idea you had made what we used to call MessCat haha 🤣 I remember maintaining it for a while and I wonder if it’s still in use?
All thanks to my amazing collaborators @Effeietsanders @ashishgoel @SmithaMilli @ArielProcaccia and Alice Siu!!! This was some work I did @AIatMeta this summer (more on the way!) Read more here: arxiv.org/abs/2511.04588 (7/7)
Our auditing tools are integrated into a deliberation platform @StanfordDDL used in 50+ countries, allowing moderators to measure and improve representation in real time (thanks, Harshvardhan Agarwal and others!) (6/7)
🚨 New paper! 🔍 “Question the Questions: Auditing Representation in Online Deliberative Processes” In deliberative polls, participants propose questions for experts but only a few make it to the panel. How representative are those chosen questions of everyone’s interests? 🧵👇
Soham De retweeted
(please reshare) I'm recruiting multiple PhD students and Postdocs @uwcse @uwnlp (bdata.uw.edu). Focus areas incl. psychosocial AI simulation and safety, Human-AI collaboration. PhD: cs.washington.edu/academics/phd/… Postdocs: docs.google.com/document/d/1h4…
Soham De retweeted
Today we're releasing Community Alignment - the largest open-source dataset of human preferences for LLMs, containing ~200k comparisons from >3000 annotators in 5 countries / languages! There was a lot of research that went into this... 🧵
@shahanmemon Hahaha the chances of me being employed by a YC startup were always slim but are finally 0 😂
interesting day on the internet for @actuallysoham 🙃
Without the YC community this guy would still be operating and would have maybe never been caught The startup guild of YC is a necessary invention to help founders be more successful than they would be alone
@Marco_Piani Your evaluation idea definitely makes sense to compare AI-assisted vs human written notes (we do something similar here: dl.acm.org/doi/10.1145/36…) but not sure if that directly solves the bottleneck issue?
@Marco_Piani Yea, I agree with rating being a bottleneck and I think there's a lot of interesting ways to address that issue. For instance, tools/algorithms to assist human raters, transfer ratings across similar notes etc.
Here’s our articulation of what the future of Community Notes in an LLM era could look like 👀 : Very interested to hear your thoughts!
Brand new paper on Arxiv with @Li_Haiwen_ @actuallysoham Manon Revel @andreas_h0wpt @__bradmiller__ @kcoleman @msaveski: Scaling Human Judgment in Community Notes with LLMs Very bullish on the LLM-assisted future of Community Notes 📈
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