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Emanuele Marconato

@ema-ridopoco.bsky.social
293 followers 273 following 23 posts

Post-doc @ University of Trento. I did my PhD @ University of Trento and the University of Pisa. I like #concepts, #symbols, and #representations, but I still don't know what they are. 📍 Trento, Italy 🧵 #identifiability, #shortcuts, #interpretability

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Emanuele Marconato @ema-ridopoco.bsky.social · 21/10/2025
See you in San Diego 😉🏜️🏄‍♂️
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Emanuele Marconato @ema-ridopoco.bsky.social · 17/06/2025
Joint work with Sebastian Weichwald, Sebastien Lachapelle, and Luigi Gresele 🙏 For more info, check the full paper 👇 arxiv.org/abs/2410.235...
t.co
https://arxiv.org/abs/2410.23501
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Emanuele Marconato @ema-ridopoco.bsky.social · 17/06/2025
🧵Summary A mathematical proof that, under suitable conditions, linear properties hold for either all or none of the equivalent models with same next-token distribution 😎 Exciting open questions on empirical findings remain🤔 - check Section 6 (Discussion) in the paper! 8/9
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Emanuele Marconato @ema-ridopoco.bsky.social · 17/06/2025
3⃣ We demonstrate what linear properties are shared by all or none LLMs. 🔥 Under mild assumptions, relational linear properties are shared! ⚠️ Parallel vectors may not be shared (they are under diversity)! 7/9
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Emanuele Marconato @ema-ridopoco.bsky.social · 17/06/2025
We also describe other linear properties: linear subspaces, probing, steering, based on relational strings (Paccanaro and Hinton, 2001). 💡They arise when the LLM can predict next-tokens for textual queries like: "What is the written language?" for many context strings! 6/9
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Emanuele Marconato @ema-ridopoco.bsky.social · 17/06/2025
2⃣ We reformulate linear properties of LLMs based on textual strings, depending on how LLMs predict next tokens 💡Parallel vectors arise from same log-ratios of next-token probs E.g. same ratio for "easy"/"easiest" and "strong"/"strongest" in all contexts => parallel vecs 5/9
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Emanuele Marconato @ema-ridopoco.bsky.social · 17/06/2025
💡The extended linear equivalence underlies that two models' representations are linearly related, but in a subspace ‼️Outside that subspace, representations can differ a lot! 4/9
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Emanuele Marconato @ema-ridopoco.bsky.social · 17/06/2025
1⃣We extend the results by Khemakem et al. (2020), Roeder et al. (2021), removing a diversity assumption. For the first time, we relate models with different repr. dimensions & find that repr.s of LLMs with same distribution are related by an “extended linear equivalence”! 3/9
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Emanuele Marconato @ema-ridopoco.bsky.social · 17/06/2025
Contributions: 1⃣ An identifiability result for LLMs 2⃣A 𝙧𝙚𝙡𝙖𝙩𝙞𝙤𝙣𝙖𝙡 reformulation of linear properties 3⃣ A proof of what properties are 𝙘𝙤𝙫𝙖𝙧𝙞𝙖𝙣𝙩 (~to Physics, cf. Villar et al. (2023)): hold for all or none of the LLMs with same next-token distribution 2/9
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Emanuele Marconato @ema-ridopoco.bsky.social · 17/06/2025
🧵Why are linear properties so ubiquitous in LLM representations? We explore this question through the lens of 𝗶𝗱𝗲𝗻𝘁𝗶𝗳𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆: “All or None: Identifiable Linear Properties of Next-token Predictors in Language Modeling” Published at #AISTATS2025🌴 1/9
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Emanuele Marconato @ema-ridopoco.bsky.social · 03/05/2025
@yanai.bsky.social this is very interesting!! FYI, we studied the ubiquity, rather than emergence, of linear relational properties here: openreview.net/forum?id=XCm...
openreview.net
All or None: Identifiable Linear Properties of Next-Token...
We analyze identifiability as a possible explanation for the ubiquity of linear properties across language models, such as the vector difference between the representations of “easy” and “easiest”...
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Emanuele Marconato @ema-ridopoco.bsky.social · 03/05/2025
Now in Thailand to present our paper at #AISTATS2025 🇹🇭🌴 📍Today at 3:00-6:00 pm, poster number 118! More details here: openreview.net/forum?id=XCm...
openreview.net
All or None: Identifiable Linear Properties of Next-Token...
We analyze identifiability as a possible explanation for the ubiquity of linear properties across language models, such as the vector difference between the representations of “easy” and “easiest”...
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Emanuele Marconato @ema-ridopoco.bsky.social · 17/04/2025
Only yesterday I discovered that my PhD thesis has been made public to everyone 😅 I worked three years on "Learning concepts" and I tried to spot the connection between #concepts, #symbols, and #representations, and how they're used in ML today 👾🪄 etd.adm.unipi.it/t/etd-012620...
etd.adm.unipi.it
Tesi etd-01262025-170550
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Emanuele Marconato @ema-ridopoco.bsky.social · 23/01/2025
Hey hey! We have an accepted paper at #AISTATS2025!! Time to prepare for Thailand 🪷🏖️🌴🐒 Huge thanks to my coauthors Luigi Gresele, Sebastian Weichwald, and @seblachap.bsky.social for all the joint effort! More details soon 👇 arxiv.org/abs/2410.235...
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Emanuele Marconato @ema-ridopoco.bsky.social · 11/12/2024
Don't miss the chance to learn more about our new #benchmark suite @ #NeurIPS2024 📊 New benchmarks to test concept quality learned by all kinds of models: Neural, NeSy, Concept-based, and Foundation models. 🤔 All models learn to solve the task but, beware, do they learn concepts?? Spoiler: 😱
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Reposted by Emanuele Marconato
Samuele Bortolotti @samubortolotti.bsky.social · 10/12/2024
📣 Does your model learn high-quality #concepts, or does it learn a #shortcut? Test it with our #NeurIPS2024 dataset & benchmark track paper! rsbench: A Neuro-Symbolic Benchmark Suite for Concept Quality and Reasoning Shortcuts What's the deal with rsbench? 🧵
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Reposted by Emanuele Marconato
Stefano Teso @looselycorrect.bsky.social · 09/12/2024
The #NeurIPS experience is about to start! ✈ Drop me a line if you want to chat about #neurosymbolic reasoning #shortcuts, human-interpretable machine #concepts, logically-consistent #LLMs, or human-in-the-➰ #XAI! See you in Vancouver!
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Emanuele Marconato @ema-ridopoco.bsky.social · 06/12/2024
For those attending #NeurIPS2024 go to UNIREPS @unireps.bsky.social workshop to know more about representations similarity. Nice work led by @beatrixmgn.bsky.social 🌟
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David Debot @daviddebot.bsky.social · 04/12/2024
🚨 Interpretable AI often means sacrificing accuracy—but what if we could have both? Most interpretable AI models, like Concept Bottleneck Models, force us to trade accuracy for interpretability. But not anymore, due to Concept-Based Memory Reasoner (CMR)! #NeurIPS2024 (1/7)
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Emanuele Marconato @ema-ridopoco.bsky.social · 04/12/2024
Ask you to please add me :)
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Reposted by Emanuele Marconato
uai2026 @auai.org · 03/12/2024
We are the premier conference on #uncertainty in #AI and #ML since 1985 🧓 Hello, 🦋! Follow us to reduce uncertainty!
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Emanuele Marconato @ema-ridopoco.bsky.social · 02/12/2024
Then I agree 😄
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Emanuele Marconato @ema-ridopoco.bsky.social · 02/12/2024
What is the precise definition of feature?
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Emanuele Marconato @ema-ridopoco.bsky.social · 28/11/2024
I would like to ask for some back stabs to reviewer 2 🤬
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Emanuele Marconato @ema-ridopoco.bsky.social · 21/11/2024
I know @looselycorrect.bsky.social well enough eheh
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Emanuele Marconato @ema-ridopoco.bsky.social · 21/11/2024
A symbol is a "physical token" (Harnad 1990, arxiv.org/html/cs/9906...)
arxiv.org
The Symbol Grounding Problem
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Emanuele Marconato @ema-ridopoco.bsky.social · 21/11/2024
Do you have answers? 😁
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