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Lorenzo Pacchiardi

@LPacchiardi

AI evaluations @LeverhulmeCFI @Cambridge_Uni. PhD in Stats and ML @UniofOxford. Studied at @PoliTOnews, @ictpnews, @Sissaschool, @UnivParisSaclay.

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[🌎English version] 📢 Introducing 🌐AcademicJobsItaly.com, a website for finding a job in Italian universities and research institutes. 1/n

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Lorenzo Pacchiardi Reposted

“I wish it need not have happened in my time," said Frodo. "So do I," said Gandalf, "and so do all who live to see such times. But that is not for them to decide. All we have to decide is what to do with the time that is given us.” ― J.R.R. Tolkien, The Fellowship of the Ring


I am currently working with Prof Hernández-Orallo and I can confirm he is great on all fronts. Happy to answer any question people may have.

Looking for a Postdoc position and wanna make an impact on developing new robust evaluation frameworks for general-purpose AI systems like LLMs? Please apply to this amazing position at TU Valencia working with Prof. José Hernández-Orallo (josephorallo.webs.upv.es)! He's a legend,…



Lorenzo Pacchiardi Reposted

1/ New paper @Nature! Discrepancy between human expectations of task difficulty and LLM errors harms reliability. In 2022, Ilya Sutskever @ilyasut predicted: "perhaps over time that discrepancy will diminish" (youtu.be/W-F7chPE9nU, min 61-64). We show this is *not* the case!

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Lorenzo Pacchiardi Reposted

Cos’è Benefficienza? La nostra missione è semplice ma potente: aiutarti a fare più bene possibile attraverso le tue donazioni in beneficenza. Come? Più informazioni nel thread 🧵👇

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Lorenzo Pacchiardi Reposted

The Government wants to build new towns. In a paper for @UKDayOne, @KaneEmerson and I propose a huge new town at the intersection of the East Coast Main Line and the planned East-West Rail. We believe this may be the best greenfield site in England. Here is why.

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Lorenzo Pacchiardi Reposted

New paper, surprising result: We finetune an LLM on just (x,y) pairs from an unknown function f. Remarkably, the LLM can: a) Define f in code b) Invert f c) Compose f —without in-context examples or chain-of-thought. So reasoning occurs non-transparently in weights/activations!

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Lorenzo Pacchiardi Reposted

Excited to announce a new version of our arXiv paper "Unified Differentiable Learning of Electric Response", now including ferroelectrics in addition to dielectrics! arxiv.org/abs/2403.17207 @ndrsjhnssn @chuinwei_tan @cameron_cowen (cr. video) @bkoz37 @Materials_Intel @hseas


Lorenzo Pacchiardi Reposted

Cosa si celebra il #25aprile? Ce lo racconta il professor #AlessandroBarbero.


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