Sue Kamli @family_shankar
Learning & growing with gratitude 🙏🏽 Joined August 2017-
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Congratulations Ladies! Bronze medalists at UIL State in 200 Medley Relay! Go Tiger Swimming! 🎉
Congratulations to Sujay Shankar @TAMUmedicine for his presentation at #ACSCC23 @AmCollSurgeons and his mentor Dr Chihara @HMSurgery @HMethodistMD!
The AI 100 2023: The top people in artificial intelligence trib.al/EKTheKs
Way to go Tigers!
❤️PLEASE SHARE: Rushi, a 9 year old from College Station, needs a kidney donor. 🩸Must have Type O blood. 🔗 DETAILS: search “Kidney for Rushi” on Facebook
Wow! This!
Girls Medley Relay on the PODIUM again at #UILState 🥉🐯🏊🏼♀️ #ProudCoach #HappySwimmers #FunTeam #FastSwims
Way to go ladies 👍🏽
Oh my! Talk about deep sleep 😱
Oh my! Talk about deep sleep 😱
The science says: -Meditation improve focus -Breathwork reduces stress -Non Sleep Deep Rest (NSDR) restores energy -Self directed hypnosis can solve specific problems Most of the tested protocols are only 5-10min per day & lead to persistent effects.
Swim fast Tigers!!
Swim fast Tigers!!
Delighted to share with you our latest paper. Here, we test the hypothesis that aging may be driven by DNA damage-induced changes to the epigenome. Mightbe due to a glitch in the software of the body that causes it to malfunction, which can be fixed with a reboot?👇 1/
Warren Buffet is worth $106 billion. Nick Saban has won 7 national titles. Both live by the “Inner Scorecard.” Here’s what it is + how you can use it:
The algorithm behind Tik Tok.
So proud of @venkyshankar 👍🏽Shankar Recognized as Accountable Marketer themasb.org/shankar-recogn…
Water may be the strangest thing in the universe
College Station friends, we can help bridge the gap in teachers’ pay by voting “yes” on proposition A in the November ballot. Let’s do this!
A sleep expert describes the perfect conditions for falling asleep quicker
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Evals are arguably the hardest part of LLMOps. LLMs mess up, so we check them w/ other LLMs, but this feels icky. Who validates the validators?? We built an interface to align LLM-based evals with user preferences, learning a lot about why this is hard: arxiv.org/abs/2404.12272
hard launching a new HCI paper on LLM evals tomorrow 🚀 (tldr: coming up with custom evals you can trust is _really_ hard, and our paper shares a number of reasons for why)
Here is another interesting paper on the future of LLM assertions! SPADE by @sh_reya, and @MadelonHulsebos, @hwchase17 et al. arxiv.org/pdf/2401.03038…
I'll be speaking about this paper tomorrow (Thurs) at the Stanford ISL Colloquium: Packard 202 @ 4pm isl.stanford.edu/talks/
What algorithms can Transformers learn? They can easily learn to sort lists (generalizing to longer lengths), but not to compute parity -- why? 🚨📰 In our new paper, we show that "thinking like Transformers" can tell us a lot about which tasks they generalize on!
last year i made a short course on data validation in ML pipelines with @weights_biases, and i'm back to say that the topic is still super relevant in the age of LLMs! don't be fooled into thinking LLMs remove the need for data validation 😉
🤔 How do you ensure data integrity in LLM pipelines? Learn to validate unstructured data in ML projects, maintaining high-quality outputs in our free Course, ‘Data Validation in Production ML Pipelines’ with @sh_reya. Enroll now!
@family_shankar @amchsswim @AMCHSTigerClub @CSISD @coachjenny_edu @ConsolHS Congratulations!
Way to go, Tigers! 🐅 🎉 #SuccessCSISD
Congratulations Ladies! Bronze medalists at UIL State in 200 Medley Relay! Go Tiger Swimming! 🎉
Event is broadcasted online: home.mlops.community/public/events/…
ever chuckled at a prompt like “DO NOT say this phrase, otherwise something CATASTROPHIC will happen?” I’m giving a ⚡️lightning talk⚡️ today at 2:20pm on how decoding prompt version history can improve AI engineering, and a downstream application in auto-generating LLM assertions
Berkeley Data Systems and Foundations team were a big presence at @cidrdb #cidr24 including two talks and two gong show wins. Happy to reunite with alumni and the godfather himself, Mike Stonebraker. Good times! @Berkeley_EECS @UCBerkeley @UCBEPIC @BerkeleySky
We’re the richest people in the country after a single generation. We’re not victims
Currently, we’re exploring how to put SPADE in an interactive system for anyone to use. Thanks to Haotian Li, @pgasawa, @MadelonHulsebos, Yiming Lin, @jdzamfi, @hwchase17, @WHinthorn, @adityagp, and @sirrice for a fun collaboration---looking forward to our future work! 🥳
Personally, I’m grateful to work on a project with fun research problems and practical potential. Our community has only scratched the surface of what LLMs can do & what to deploy. I’m excited by the work we’re all doing to make them more reliable for automated use.
With small datasets (~50-80) of LLM pipeline inputs & outputs, we demonstrate solutions that work for 9 LLM pipelines across various domains (e.g., finance, software, writing). The data and pipelines are open-sourced for further research on deployed LLMs.
Second, we filter bad & redundant candidate assertions without sampling lots of data or training custom evaluators. We formulate this as a problem of selecting a minimal set of non-overlapping and accurate assertions while maintaining coverage.
We analyzed 19 LLM pipelines from @LangChainAI, creating a taxonomy of prompt changes to develop assertions around. For example, if a developer adds an “exclusion instruction,” we synthesize assertion(s) to check for this.
I have 2 favorite contributions. First, we use prompt version history to seed candidate assertions. From studying prompt datasets, we discovered a novel insight: people implicitly encode failure criteria in revisions of their prompts, e.g., “do not say X.”
We all know LLMs make mistakes. One simply cannot deploy LLM pipelines without assertions, yet writing good assertions is tedious & hard. So, we built SPADE, a system that analyzes prompts & auto-generates custom assertions in low-data settings: arxiv.org/abs/2401.03038
Shreya @sh_reya will be presenting our paper on declarative crowdsourcing - meets - LLMs (cidrdb.org/cidr2024/paper…), and giving a preview of her other work on making LLM-powered data workflows easier to deploy and more reliable. This is an exciting vision and should be a fun talk!
looking forward to @cidrdb next week! many Berkeley folks will be in attendance. i am excited for the whiskey tasting and my (Wednesday) talk on scalable data processing with LLMs. reply or DM if you want to hang out ✨
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