Philippe Laban @PhilippeLaban

The 2nd workshop on CustomNLP (models customized to individuals, groups, specialized domains) is back at #ACL2026! Join us if you're around! Schedule 👇👇👇👇👇

CustomNLP4U 2026 @customnlp4u

3 days ago

🚨 Join us on July 3 for the 2nd CustomNLP Workshop at #ACL2026! We're excited to share a fantastic lineup of invited talks & papers on personalizing, adapting, and customizing LLMs. Hope to see you there! 🎉 📅 customnlp4u.github.io/schedule/ 👇 Meet our amazing invited speakers:

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📚 AI-written stories get attention. AI-translated literature is reaching readers more quietly, shaping how they experience an author’s work. So what is lost in AI literary translation ⁉️🤔 🤖 AI translation into English is readable and often ‘fine’. ✍️ But human translation is valued more, especially on a closer look, for clarity and immersion. ‼️ Both vary in quality BUT AI varies more, even within a single book. (1/11)

AI NEWS UPDATE We've added 200k+ recent news articles and 5k op-eds from Oct 2025 - June 2026. Local news is up to ~10.81% AI-generated.

Are LLM-generated stories novel? They can have unique characters and cliché plots, or the other way around. A holistic score doesn’t help distinguish the two 😔. Meet GENIE 🧞 – a fine-grained novelty metric that tells you where and why a response is original!

Woke up this morning to read @vauhinivara’s brilliant existential essay at @nytimes on em dash that reinforces the beauty of language and resistance to a world where LLMs homogenize it :-)

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Post-training makes LLMs safer and better at following instructions, but less diverse. 🤔 Can we get that diversity back without sacrificing alignment? Introducing ReDiPO: a preference optimization recipe for restoring distributional diversity while preserving safety and instruction-following.

People are increasingly worried that AI tools make us overreliant. But how do we actually measure this? We introduce Offloading Score, a measure of reliance based on the fraction of cognitive effort offloaded to AI while completing a task. In a controlled user study, Offloading Score detects increased reliance under time pressure, while several common alternatives do not. (1/9)

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We built a joint experimental and computational platform for scalable multi-modal single-cell chemical screens — profiling RNA, protein (including phospho-signaling), and chromatin accessibility responses to thousands of small molecule perturbations in parallel. biorxiv.org/content/10.648…

New paper! Have you or a loved one been harmed by a bad multiple-choice benchmark? 😔 You may be entitled to a more reliable evaluation 🩺 At #ACL2026, we'll present BenchMarker: a toolkit to diagnose common flaws in MCQA benchmarks, inspired by best practices in education 🧑‍🏫🧵

Ran some 🧪 with @irisiris_l to 🔬 why the Granta story was certainly 🤖 slop A lot of bad writing happens coz AI hasn’t learned aesthetics. It has memorized the whole internet and called it a day. So sure, maybe you don't trust AI detectors. But you can trust your own 👁️.

Looking for 1 emergency reviewer for a @COLM_conf paper on Image Editing of Diffusion Model due Wednesday (05/20). Please DM me if interested. Thanks! Retweets appreciated

We like to delegate tasks to LLMs, where the models perform long-horizon and iterative operations on documents and return the results. But how far can you trust the models to stay faithful to the original content of the document? A new study by Microsoft Research answers this question with an interesting technique. They use "round-trip relay" tasks to evaluate the capability of LLMs to perform accurate edit tasks on documents. Basically, it is like back-translation. The model has to perform an operation on the document and then reverse it to produce the original content. To simulate multi-iteration tasks, they chain these operations together across 20 steps. They created a benchmark, DELEGATE-52, which measures delegation performance across different domains. The results: - Even the best LLMs corrupt up to 25% of the document contents. - Corruption doesn't happen by "death by a thousand cuts." Usually, one mishap derails the model, and it can happen on any of the iterations - Generic tools for code execution and file read/write access don't make things better What's the main takeaway: - Only delegate to the extent that you stay in control - Create domain-specific tools Many thanks to @PhilippeLaban for sharing comments and actionable insights for developers.

VentureBeat @VentureBeat

2 months ago

Frontier AI models don't just delete documents — they rewrite them. Microsoft Research tested 19 models: even the best corrupted 25% of content. Agentic tools made it worse. venturebeat.com/ai/frontier-ai…

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MyScholarQA is live! If you want a deep research system that actually knows about your work, check it out 👇 …rsonalized-scholarqa.apps.allenai.org

Ai2 @allen_ai

2 months ago

Now available in AstaLabs in limited research preview: MyScholarQA, a personalized version of ScholarQA for scientific deep research. ScholarQA helps synthesize evidence from 12M+ open-access papers. MyScholarQA adds user profiles to tailor that synthesis to you. 🧵

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@tae_skim Great work, Tae Soo. The methodology is super clean and the paper was an incredible read!

Ask an LLM for a "post that'll pop off". Output misses so you say "more unique." It asks "unique how?" but you don't know yet. DiscoverLLM (ICML'26): trains LLMs to help users discover their intents, not just execute them. 📑 arxiv.org/abs/2602.03429 🌐 taesookim.com/discoverllm

We use LLMs to role-play "users" to train, evaluate, and improve AI assistants. How do you know if your user simulator is any good? We argue: rather than measuring how realistic it sounds, start measuring how the assistants it trains perform with real humans. 🧵👇

Today, we’re excited to launch Recursive (@Recursive_SI): an exceptional team across London and San Francisco, building AI systems that can safely improve their own capabilities over time.

Recursive @Recursive_SI

2 months ago

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User simulators have emerged as promising tools for building interactive AI, but what makes a “good” simulator? We reframe the problem as what creates downstream value for humans Our new simulator test: how an LLM assistant trained with the simulator performs with human users🧵

What happens when you compare the distributions of real and simulated user behaviors? 🔍 The gap is large. We introduce a method to measure this gap and evaluate 24 LLM-based user simulators across coding and writing tasks. @convai_uiuc @MSFTResearch @berkeley_ai 🧵 1/N

We've just released open source MTP style drafters for Gemma 4 models ⚡ Now Gemma 4 models are even faster on your choice of hardware, without losing quality! Grateful for the fruitful collaboration between my team, Gemma team, and many collaborators to enable this release!

Omar Sanseviero @osanseviero

2 months ago

Excited to introduce Gemma 4 Multi-Token Prediction Drafters⚡️Accelerated inference right in your pockets - Up to a 3x speedup - Same quality guarantees - Available in your favorite open-source tools

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