@williamheld.com
Modeling Linguistic Variation to expand ownership of NLP tools Views my own, but affiliations that might influence them: ML PhD Student under Prof. Diyi Yang 2x RS Intern🦙 Pretraining Alum NYU Abu Dhabi Burqueño he/him
OpenAI addresses this with a backend classifier "to detect if the GPT‑4o output is using a voice that’s different from our approved list".
But that isn't possible for open-source models, so would be great to at least partially mitigate this by baking in this gating mechanism.
While many systems exist explicitly designed for voice cloning, some systems can do it unintentionally due to ICL.
For example, in the original 4o card
"""
During testing, we also observed rare instances where the model would unintentionally generate an output emulating the user’s voice
"""
Super interested to what degree this interaction can be fine-tuned into models in a non-reversible fashion!
Voice cloning is unfortunately a capability which inherently shows up in pretrained audio models. It would be great to be able to largely limit the capability at the level of model weights!
Now that school is starting for lots of folks, it's time for a new release of Speech and Language Processing! Jim and I added all sorts of material for the August 2025 release! With slides to match! Check it out here: web.stanford.edu/~jurafsky/sl...
"GPT-5 shows scaling laws are coming to an end"
We’ve discovered a literal miracle with almost unlimited potential and it’s being scrapped for *no reason whatsoever*. This isn’t even nihilism, it’s outright worship of death and human suffering.
Really great pointer from Hao Zhang on the other site in relation to GPT OSS use of attention sinks.
If I were to guess, the attention sink is what allows them to omit QK-Norm which has become otherwise standard.
www.evanmiller.org/attention-is...
Alt Text: Conference schedule for July 28th (Monday) and July 29th (Tuesday), listing talk titles, locations, times, and authors: July 28th, Monday: 1. Attacking Vision-Language Computer Agents via Pop-ups Location: Hall 4/5, Time: 11:00–12:30 Authors: Yanzhe Zhang, Tao Yu, Diyi Yang 2. SPHERE: An Evaluation Card for Human-AI Systems Location: Hall 4/5, Time: 18:00–19:30 Authors: Dora Zhao*, Qianou Ma*, Xinran Zhao, Chenglei Si, Chenyang Yang, Ryan Louie, Ehud Reiter, Diyi Yang*, Tongshuang Wu* (asterisk denotes equal contribution) July 29th, Tuesday: 1. SynthesizeMe! Inducing Persona-Guided Prompts for Personalized Reward Models in LLMs Location: Hall 4/5, Time: 10:30–12:00 Authors: Michael J Ryan, Omar Shaikh, Aditri Bhagirath, Daniel Frees, William Barr Held, Diyi Yang 2. Distilling an End-to-End Voice Assistant Without Instruction Training Data Location: Room 1.61, Time: 14:12 (Second Talk) Authors: William Barr Held, Yanzhe Zhang, Weiyan Shi, Minzhi Li, Michael J Ryan, Diyi Yang 3. Mind the Gap: Static and Interactive Evaluations of Large Audio Models Location: Room 1.61 (implied), follows previous talk Authors: Minzhi Li*, William Barr Held*, Michael J Ryan, Kunat Pipatanakul, Potsawee Manakul, Hao Zhu, Diyi Yang (asterisk denotes equal contribution) 4. EgoNormia: Benchmarking Physical Social Norm Understanding Location: Hall 4/5, Time: 16:00–17:30 Authors: MohammadHossein Rezaei*, Yicheng Fu*, Phil Cuvin*, Caleb Ziems, Yanzhe Zhang, Hao Zhu, Diyi Yang (asterisk denotes equal contribution)
The SALT Lab is at #ACL2025 with our genius leader @diyiyang.bsky.social.
Come see work from
@yanzhe.bsky.social,
@dorazhao.bsky.social @oshaikh.bsky.social,
@michaelryan207.bsky.social, and myself at any of the talks and posters below!
Paper: aclanthology.org/2025.acl-lon...
I'm in Vienna for #ACL2025!
My work is all presented tomorrow, but today you'll find me today at the poster session from 11-12:30 evangelizing
my labmate Yanzhe Zhang's work on his behalf.
If you're interested in the risks traditional pop-up attacks present for AI agents, come chat!
It seems (at a minimum) like they post-trained on the virulently racist content from this thread. Musk framed this as a request for training data... and the top post is eugenics. Seems unlikely to be coincidence that the post uses the same phrasing as the prompt they later removed...
Btw, all of this is very nice for something that was a quick 15 line addition to Levanter.
github.com/stanford-crf...
Have an optimizer you want to prove works better than AdamC/Muon/etc?
Submit a speedrun to Marin! marin.readthedocs.io/en/latest/tu...
For PRs with promising results, we're lucky to be able to help test at scale on compute generously provided by the TPU Research Cloud!
In our most similar setting to the original work (130M model), we don't see AdamC's benefits but
- We use a smaller WD (0.01) identified from sweeps v.s. what is used in the paper (0.05).
- We only train to Chnichilla optimal (2B tokens) whereas the original paper was at 200B.
We see the same pattern at 300m and 500m!
Remember, everything else in these experiments is held constant by Levanter & Marin (data order, model init. etc.)
Experiment files here: github.com/marin-commun...
As a side note, Kaiyue Wen found that weight decay also causes slower loss decrease at the start of training in wandb.ai/marin-commun...
Similar to the end of training, this is likely because LR warmup also impacts the LR/WD ratio.
AdamC seems to mitigate this too.
TL;DR: 3/4 of our scales we find the AdamC results to reproduce out of the box!
When compared to AdamW with all other factors held constant, AdamC mitigates the gradient ascent at the end of training and leads to an overall lower loss (-0.04)!
A while ago I mentioned that for marin.community project, this gradient increase led to problematic loss ascent which we patched with Z-loss.
I was curious, does AdamC just work?
So over the weekend, I ran 4 experiments—130M to 1.4B params—all at ~compute-optimal token counts...🧵
kyutai.org/next/unmute has built in turn-detection on the ASR and full I/O streaming for the TTS. Solves the latency issues that I think are 90% of why people use end-to-end speech models in the first place!
From the details, you can @kyutai-labs.bsky.social is focused on real-world utility.
Flattered and shocked for our paper to receive the #facct2025 best paper award.
As far as I can tell, the models aren't good enough right now that they can replace VFX at any high quality commercial scale.
They are exactly good enough to generate fake viral videos for ad revenue on TikTok/Instagram & spread misinformation. Is there any serious argument for their safe release??
I don't really see an argument for releasing such models with photorealistic generation capabilities.
What valid & frequent business use case is there for photorealistic video & voice generation like Veo 3 offers?
I've only seen Veo 3 (or any other video generation model) used to produce viral videos. The fake videos seem to successfully trick the majority of commenters and have no visible watermark or disclosure of AI use.
What would you say if you saw it in another country? A senator from a coequal branch of government dragged away by security from asking a question of a Cabinet official
🚨 70 million US workers are about to face their biggest workplace transmission due to AI agents. But nobody’s asking them what they want.
While AI R&D races to automate everything, we took a different approach: auditing what workers want vs. what AI can deliver across the US workforce.🧵
Really cool to see theory connect to practice! We observed this phenomenon when trying to do deeper WSD cooldowns of our 8B model in the marin.community project!
We Z-Lossed our way through the pain, but cool to see some stronger theory: marin.readthedocs.io/en/latest/re...
Now, I wouldn't do research on LLMs if I thought that was true in the long term!
But I think it's reasonable for skeptics to question whether advances in inference efficiency, hardware efficiency, and even core energy infrastructure will happen soon enough for current companies to capitalize.
The underlying assumption being that they can (a la Uber/Lyft) eventually increase prices once the core customers are fundamentally reliant on AI.
The real question then is "what is demand once you start charging the true unit costs?". Personally, I found this article sobering but well reasoned.
Without knowing all the model details or with transparent financials, it's hard to say but I would naively suspect most AI companies are in the red both on a cost per query basis (for API services) and on a cost per user basis (for subscription services).
I haven't seen people mocking the revenue forecasts, but I agree with your take w.r.t. demand. The bigger question is whether demand is the constraint?
Unlike standard software or even manufacturing businesses, I'm not sure the economies of scale look great if you factor in cost per query.