Alessandro Stolfo @alesstolfo
PhD Student @ ETH Zürich in #NLProc | Prev. @oracle Labs alestolfo.github.io Joined March 2012-
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🧵@alesstolfo : "Our results show that while larger models tend to ground their outputs more effectively, a significant portion of correct answers remains compromised by hallucinations." ↩️
Welcome to our poster at 11 am! We will introduce our #emnlp2023 work: understanding how LLMs answer multi-step reasoning questions (by memorization or step-by-step reasoning). Paper: arxiv.org/abs/2310.14491
I and many of our group members are at #EMNLP2023 this week. We have a number of contributions to the conference spanning various topics in NLP, Reasoning and AI in Education. Please drop by if you are interested in any of the following:
@gu_yuling In “A Causal Framework to Quantify the Robustness of Mathematical Reasoning with LMs.”, @alesstolfo @ZhijingJin @JupyterAI @bschoelkopf @mrinmayasachan examine LLM behavior and robustness in solving math problems, but using causal graphs. Paper: arxiv.org/abs/2210.12023…
Excited to be at #ACL2023NLP to present our paper “A Causal Framework to Quantify the Robustness of Mathematical Reasoning with LMs.” Stop by and check out our poster tomorrow @ 9am (Frontenac Ballroom) arxiv.org/abs/2210.12023 @ZhijingJin @JupyterAI @bschoelkopf @mrinmayasachan
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@gfodor This makes perfect sense? They trained the model from scratch, of course it can learn to use filler tokens
Fantastic work from @sen_r and @ArthurConmy - done in an impressive 2 week paper sprint! Gated SAEs are a new sparse autoencoder architecture that seem a major Pareto improvement. This is now my team's preferred way to train SAEs, and I hope it'll accelerate the community's work!
New @GoogleDeepMind MechInterp work! We introduce Gated SAEs, a Pareto improvement over existing sparse autoencoders. They find equally good reconstructions with around half as many firing features, while maintaining interpretability (CI 0-13% improvement). Joint w/ @ArthurConmy
We’re excited to share Gated SAEs: an improvement to Sparse Autoencoder training that scales to 7B parameter models (at least!)
New @GoogleDeepMind MechInterp work! We introduce Gated SAEs, a Pareto improvement over existing sparse autoencoders. They find equally good reconstructions with around half as many firing features, while maintaining interpretability (CI 0-13% improvement). Joint w/ @ArthurConmy
The latest talk at @zurichnlp also exists as a video presentation since this morning. 🌂 I welcome feedback on the format, what works, and what doesn't. 🙏 youtu.be/yeEZpf4BlDA
(A chi ha scelto come mestiere di parlare male continuamente di ragazzi e ragazze vorrei dire che al corso pomeridiano di astrofisica per il triennio vengono volontariamente 35 persone a seguire due ore dopo averne fatte cinque la mattina. Senza voti, per interesse.)
instruction tuning & size & beam search ➡️ less hallucination (however, sampling is a seemingly necessary evil to avoid degenerate outputs). Some caveats/limitations: 1. model-based evaluation of groundness 2. Only OSS models are evaluated arxiv.org/abs/2404.07060
@alesstolfo This is such a cool single-author paper! Kudos Ale!
We've spent a tremendous amount of time reflecting whether the NLP task of Sentiment Classification (x=review, y=rating) is causal or anticausal since 2020. Check out our 2024 latest answer➡️arxiv.org/abs/2404.11055 💡We combined Causality and Psychology insights & improved #LLMs!
🧵@alesstolfo : "Our results show that while larger models tend to ground their outputs more effectively, a significant portion of correct answers remains compromised by hallucinations." ↩️
AC: what's the problem? me: the review is clearly ChatGPT generated reviewer: diving into the discourse tapestry, it is crucial to unweave the possibilit-
Thank you @ReviewAcl that the options aren't 0, 4, 5, 6, 7, 8 anymore.
@rao2z I have an idea for how to fix it...
This is the path that Italy should have followed, too. It's not too late to correct course.
Until 50 years ago, CO₂ emissions developed in lockstep with economic growth in France. Since the early 1970s, the opposite has been true: emissions declined as people in France got richer.
Can we localize the weights and mechanisms used by a language model to recite entire paragraphs of its training data?📄➡️🤖➡️📄 arxiv.org/pdf/2403.19851… To find out, have a look at my @GoogleAI intern project advised by Owen Lewis, @MitchellAGordon and Chiyuan Zhang. Thread ⬇️
The Dream-Comes-True❤️ moment for causality researchers -- meeting Judea Pearl @yudapearl in person! Yuda's profoundly knowledgeable in philosophy, genuinely enjoys singing, and is just charming. Truly touched by his talk & personal stories w/ @kahneman_daniel (Two great minds!)
Day 2 wraps up with an enlightening speech by Prof Judea Pearl.
Yesterday! @yanaiela came to @EdinburghNLP @InfAtEd to talk about What’s In My Big Data from @allen_ai
@pmddomingos Let’s add so much noise that the signal gets obscured and then claim there’s no signal
@pmddomingos Try magnifying that scale. You can clearly see an upward slope, and given that a 1c average temp change is significant, your scale is way too compressed.
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