🔥The 9th Round of Easy Loan, Earn $40 Reward is in progress❗️ ⏰ Promotion Period: January 15th - Feburary 15th, 2025 👉 Register now and check more details at gate.io/campaigns/358

Hi friends, I will be at #NAACL2025 to present: 🧷 AI-LieDar: a framework to study LLMs navigating truthfulness-utility conflicts in interactions, and we found agents "lie" in goal-driven tasks with truthfulness rates below 50% 🫨 🧷 Sotopia-S4: a demo for our Sotopia

(((ل()(ل() 'yoav))))👾 yeah we some debates about what models "lying" really means and whether to use those words; some related terms have been used before, which we discuss in the paper. I like the conclusion you reached, but agree with the fear of wrongly anthropomorphizing / attributing intent

What does it mean for #LLM output to be novel? In work w/ John(Yueh-Han) Chen, Jane Pan, Valerie Chen, He He we argue it needs to be both original and high quality. While prompting tricks trade one for the other, better models (scaling/post-training) can shift the novelty frontier 🧵

Would you trust an AI that chooses deception over truth when faced with conflicting goals? 📅 Checkout our poster at #NAACL2025 on April 30 @ 11am poster session 1 presented by Xuhui Zhou and led by Zhe Su

Have questions? We’re an open book! We’re excited to host an AMA to answer your Qs about OLMo, our family of open language models. 🗓️ When: May 8, 8-10 am PT 🌐 Where: r/huggingface 🧠 Why: Gain insights from our expert researchers Chat soon!

Delighted to see BigGen Bench paper receive the 🏆best paper award 🏆at NAACL HLT 2025 2025! BigGen Bench introduces fine-grained, scalable, & human-aligned evaluations: 📈 77 challenging, diverse tasks 🛠️ 765 instances w/ ex-specific scoring rubrics 📋More human-aligned than

The story of OLMo, our Open Language Model, goes back to February 2023 when a group of researchers gathered at Ai2 and started planning. What if we made a language model with state-of-the-art performance, but we did it completely in the open? 🧵

📢We’re taking your questions now on Reddit for tomorrow’s AMA! Ask us anything about OLMo, our family of fully-open language models. Our researchers will be on hand to answer them Thursday, May 8 at 8am PST.

🆕paper: LLMs Get Lost in Multi-Turn Conversation In real life, people don’t speak in perfect prompts. So we simulate multi-turn conversations — less lab-like, more like real use. We find that LLMs get lost in conversation. 👀What does that mean? 🧵1/N 📄arxiv.org/abs/2505.06120

One of the trickiest problems in LLM deployment: preventing models from mindlessly reproducing training data while keeping intentional recall capabilities intact. Our ParaPO approach achieves this "smart memorization" elegantly through post-training preference optimization. 🎯

Now accepted at ICML 2025

📢I'm thrilled to announce that I’ll be joining @KAIST_AI as an Assistant Professor in 2026, leading the Computation & Cognition (COCO) Lab🤖🧠: coco-kaist.github.io We'll be exploring reasoning, learning w/ synthetic data, and social agents! +I'm spending a gap year NVIDIA✨

🤔 Can simple string-matching metrics like BLEU rival reward models for LLM alignment? 🔍 We show that given access to a reference, BLEU can match reward models in human preference agreement, and even train LLMs competitively with them using GRPO. 🫐 Introducing BLEUBERI:

🤯 We cracked RLVR with... Random Rewards?! Training Qwen2.5-Math-7B with our Spurious Rewards improved MATH-500 by: - Random rewards: +21% - Incorrect rewards: +25% - (FYI) Ground-truth rewards: + 28.8% How could this even work⁉️ Here's why: 🧵 Blogpost: tinyurl.com/spurious-rewar…

Data curation is crucial for LLM reasoning, but how do we know if our dataset is not overfit to one benchmark and generalizes to unseen distributions? 🤔 𝐃𝐚𝐭𝐚 𝐝𝐢𝐯𝐞𝐫𝐬𝐢𝐭𝐲 is key, when measured correct—it strongly predicts model generalization in reasoning tasks! 🧵

🚨 New Paper! 🚨 Guard models slow, language-specific, and modality-limited? Meet OmniGuard that detects harmful prompts across multiple languages & modalities all using one approach with SOTA performance in all 3 modalities!! while being 120X faster 🚀 arxiv.org/abs/2505.23856

I’m thrilled to share RewardBench 2 📊— We created a new multi-domain reward model evaluation that is substantially harder than RewardBench, we trained and released 70 reward models, and we gained insights about reward modeling benchmarks and downstream performance!

Tired of AI slop? Our work on "Frankentexts" shows how LLMs can stitch together random fragments of human writing into coherent, relevant responses to arbitrary prompts. Frankentexts are weirdly creative, and they also pose problems for AI detectors: are they AI? human? More 👇

We enabled OLMoTrace for Tülu 3 models! 🤠 Matched spans are shorter than for OLMo models, bc we can only search in Tülu's post-training data (base model is Llama). Yet we thought it'd still bring some value. Try yourself on the Ai2 playground -- playground.allenai.org