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Edoardo Ponti

@edoardo-ponti.bsky.social
1.3K followers 78 following 27 posts

Assistant professor in Natural Language Processing at the University of Edinburgh and visiting professor at NVIDIA | A Kleene star shines on the hour of our meeting.

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Reposted by Edoardo Ponti
Digital Futures @digitaluom.bsky.social · 09/06/2025
Up next on stage, Dr. @edoardo-ponti.bsky.social ( @edinburgh-uni.bsky.social / NVIDIA) 🎤 “Adaptive Units of Computation: Towards Sublinear-Memory and Tokenizer-Free Foundation Models” Fascinating glimpse into the next gen of foundation models. #FoundationModels #NLP #TokenizerFree #ADSAI2025
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Edoardo Ponti @edoardo-ponti.bsky.social · 06/06/2025
Thanks to the amazing collaborators Adrian Łańcucki, Konrad Staniszewski, and Piotr Nawrot! It was amazing to spend a year at NVIDIA as a visiting professor! arXiv: arxiv.org/pdf/2506.05345 Code and models coming soon!
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Edoardo Ponti @edoardo-ponti.bsky.social · 06/06/2025
🏆 We evaluate inference-time hyper-scaling on DeepSeek R1-distilled models of different sizes, increasing accuracy on maths, science, and coding by up to 15 points for a given budget.
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Edoardo Ponti @edoardo-ponti.bsky.social · 06/06/2025
💡The idea behind DMS is to *train* existing LLMs to evict tokens from the KV cache, while delaying the eviction some time after the decision. This allows LLMs to preserve information while reducing latency and memory size.
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Edoardo Ponti @edoardo-ponti.bsky.social · 06/06/2025
⚖️ The magic works only if accuracy is preserved even at high compression ratios. Enter Dynamic Memory Sparsification (DMS), which achieves 8x KV cache compression with 1K training steps and retains accuracy better than SOTA methods.
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Edoardo Ponti @edoardo-ponti.bsky.social · 06/06/2025
🚀 By *learning* to compress the KV cache in Transformer LLMs, we can generate more tokens for the same compute budget. This unlocks *inference-time hyper-scaling* For the same runtime or memory load, we can boost LLM accuracy by pushing reasoning even further!
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Reposted by Edoardo Ponti
Emile van Krieken @emilevankrieken.com · 21/05/2025
We propose Neurosymbolic Diffusion Models! We find diffusion is especially compelling for neurosymbolic approaches, combining powerful multimodal understanding with symbolic reasoning 🚀 Read more 👇
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Edoardo Ponti @edoardo-ponti.bsky.social · 25/04/2025
Code: github.com/PiotrNawrot/... Paper: arxiv.org/abs/2504.17768 Thanks to the lead author, Piotr Nawrot, and all the amazing collaborators!
arxiv.org
The Sparse Frontier: Sparse Attention Trade-offs in Transformer LLMs
Sparse attention offers a promising strategy to extend long-context capabilities in Transformer LLMs, yet its viability, its efficiency-accuracy trade-offs, and systematic scaling studies remain unexp...
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Edoardo Ponti @edoardo-ponti.bsky.social · 25/04/2025
4) Finally, we introduce novel scaling laws for sparse attention and validate them on held-out results: evidence that our findings will likely hold true broadly. Our insights demonstrate that sparse attention will play a key role in next-generation foundation models.
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Edoardo Ponti @edoardo-ponti.bsky.social · 25/04/2025
3) There is no single best strategy across tasks and phases. However, on average Verticals-Slashes for prefilling and Quest for decoding are the most competitive. Context-aware, and highly adaptive variants are preferable.
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Edoardo Ponti @edoardo-ponti.bsky.social · 25/04/2025
2) Sparsity attainable while statistically guaranteeing accuracy preservation is higher during decoding ✍️ than prefilling 🧠, and correlates with model size in the former. Importantly, for most settings there is at least one degraded task, even at moderate compressions (<5x).
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Edoardo Ponti @edoardo-ponti.bsky.social · 25/04/2025
1) For very long sequences, *larger and highly sparse models* are preferable to small, dense ones for the same FLOPS budget. This suggests a strategy shift where scaling up model size must be combined with sparse attention to achieve an optimal trade-off.
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Edoardo Ponti @edoardo-ponti.bsky.social · 25/04/2025
Sparse attention is one of the most promising strategies to unlock long-context processing and long-generation reasoning in LLMs. We performed the most comprehensive study on training-free sparse attention to date. Here is what we found:
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Reposted by Edoardo Ponti
Digital Futures @digitaluom.bsky.social · 01/04/2025
🚀 Excited to welcome Dr. @edoardo-ponti.bsky.social to #ADSAI2025! Lecturer in NLP @edinburghuni.bsky.social , Affiliated Lecturer @cambridgeuni.bsky.social & Visiting Prof NVIDIA. 🎟️ Tickets for Advances in Data Science & AI Conference 2025 are live! 🔗Secure your spot: tinyurl.com/yurknk7y #AI
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Reposted by Edoardo Ponti
Benjamin Minixhofer @bminixhofer.bsky.social · 02/04/2025
We created Approximate Likelihood Matching, a principled (and very effective) method for *cross-tokenizer distillation*! With ALM, you can create ensembles of models from different families, convert existing subword-level models to byte-level and a bunch more🧵
Image illustrating that ALM can enable Ensembling, Transfer to Bytes, and general Cross-Tokenizer Distillation.
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Edoardo Ponti @edoardo-ponti.bsky.social · 31/01/2025
I have a scholarship for a PhD in efficient memory and tokenization in LLM architectures at @edinburgh-uni.bsky.social! Eligibility: UK home fee status Starting date: flexible, from July 2025 onwards. informatics.ed.ac.uk/study-with-u... Please contact me if you're interested!
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Edoardo Ponti @edoardo-ponti.bsky.social · 31/01/2025
Code and models for Dynamic Memory Compression are finally available! Stay tuned for architectures with even more efficient inference. developer.nvidia.com/blog/dynamic...
developer.nvidia.com
Dynamic Memory Compression | NVIDIA Technical Blog
Despite the success of large language models (LLMs) as general-purpose AI tools, their high demand for computational resources make their deployment challenging in many real-world scenarios.
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Edoardo Ponti @edoardo-ponti.bsky.social · 22/12/2024
We're hiring a lecturer or reader in embodied NLP at the University of Edinburgh! Deadline: 31 Jan 2025 Call for applications: elxw.fa.em3.oraclecloud.com/hcmUI/Candid...
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Edoardo Ponti @edoardo-ponti.bsky.social · 20/12/2024
What's in the future? - Richer proxies for meaning, including a temporal dimension and internal agent states - The study of grammaticalization under the lens of groundedness We release an extensive dataset to support these studies: osf.io/bdhna/
osf.io
A Grounded Typology of Word Classes
Hosted on the Open Science Framework
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Edoardo Ponti @edoardo-ponti.bsky.social · 20/12/2024
We focus on the groundedness of lexical classes and find that it - follows a continuous cline cross-linguistically: nouns > adjectives > verbs - is non-zero even for functional classes (e.g., adpositions) - is contextual, so agrees with psycholinguistic norms only in part
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Edoardo Ponti @edoardo-ponti.bsky.social · 20/12/2024
We leverage advances in multilingual and multimodal foundation models to quantify their surprisal for both form alone and form given function Their difference (pointwise mutual information) corresponds to the groundedness of a word: the remaining surprisal once function is known
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Edoardo Ponti @edoardo-ponti.bsky.social · 20/12/2024
**Grounded typology**: a new paradigm. Traditionally, linguists posit functions to compare forms in different languages; however, these are aprioristic and partly arbitrary. Instead, we resort to perceptual modalities (like vision) as measurable proxies for function.
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Edoardo Ponti @edoardo-ponti.bsky.social · 12/12/2024
Two considerations: 1) reusing / interpolating old token is reminiscent of our FOCUS baseline. Unfortunately it degrades performance as even identical tokens may change their function. 2) you incur a large overhead for calculating the co-occurrence matrix for every new tokenizer.
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Edoardo Ponti @edoardo-ponti.bsky.social · 12/12/2024
Two amazing papers from my students at #NeurIPS today: ⛓️💥 Switch the vocabulary and embeddings of your LLM tokenizer zero-shot on the fly (@bminixhofer.bsky.social) neurips.cc/virtual/2024... 🌊 Align your LLM gradient-free with spectral editing of activations (Yifu Qiu) neurips.cc/virtual/2024...
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Edoardo Ponti @edoardo-ponti.bsky.social · 04/12/2024
ML is the fox?
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Edoardo Ponti @edoardo-ponti.bsky.social · 04/12/2024
Plus Edinburgh gets the Saltire, but Cambridge gets the Union Jack. Vexillologists are in shambles
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Edoardo Ponti @edoardo-ponti.bsky.social · 28/11/2024
We had a blast at this year's @ellis.eu Dagstuhl seminar on "Modular and Agentive LLMs". Thanks everyone for participating!
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Pasquale Minervini @neuralnoise.com · 26/11/2024
Check out this piece on Strawberry 🍓/o1 we just authored on TheConversation! theconversation.com/ai-that-mimi... with @edoardo-ponti.bsky.social and Kolya 🚀
theconversation.com
AI that mimics human problem solving is a big advance – but comes with new risks and problems
Chain of thought reasoning has been used in OpenAI’s new AI model.
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Reposted by Edoardo Ponti
Sasha Rush @srushnlp.bsky.social · 21/11/2024
Several incredible NeurIPS tutorials this year. Worth navigating through the Swifties.
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Edoardo Ponti @edoardo-ponti.bsky.social · 22/11/2024
Thanks, Sasha! Your tutorials are a great source of inspiration for us.
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Edoardo Ponti @edoardo-ponti.bsky.social · 21/11/2024
P.S. Make sure to follow @pnawrot.bsky.social!
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Reposted by Edoardo Ponti
Alessandro Sordoni @murefil.bsky.social · 21/11/2024
Explore zero-shot routing of parameter-efficient experts with Phatgoose arxiv.org/abs/2402.05859 and Arrow arxiv.org/abs/2405.11157 w. github.com/microsoft/mttl 👉 github.com/sordonia/pg_mb… Part of "Dynamic Sparsity in ML" tuto #neurips2024, feedback welcome and join for discussions! 😊
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Edoardo Ponti @edoardo-ponti.bsky.social · 21/11/2024
Last 5 days to apply for a PhD at #EdinburghNLP! Deadline: November 25 www.ed.ac.uk/studying/pos... If you are passionate about: - adaptive tokenization and memory in foundation models - modular deep learning - computational typology please message me or meet me at #NeurIPS2024!
ed.ac.uk
Informatics: ILCC: Language Processing, Speech Technology, Information Retrieval, Cognition
Study Informatics: ILCC: Language Processing, Speech Technology, Information Retrieval, Cognition at the University of Edinburgh. Our postgraduate degree programmes focus on natural language processin...
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Edoardo Ponti @edoardo-ponti.bsky.social · 20/11/2024
Another nano gem from my amazing student Piotr Nawrot! A repo & notebook on sparse attention for efficient LLM inference: github.com/PiotrNawrot/... This will also feature in my #NeurIPS 2024 tutorial "Dynamic Sparsity in ML" with André Martins: dynamic-sparsity.github.io Stay tuned!
A sparse mask of attention scores based on VerticalAndSlashAttention and a plot of loss vs sparsity ratio for various methods.
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