1/ Into Image Captioning? Don’t miss this! Struggling to keep up with the influx of new metrics but still see the same 5 (BLEU, METEOR, ROUGE, CIDEr, SPICE) leading? Read our recent Captioning evaluation survey! arxiv.org/abs/2408.04909 w\ Gabriel Stanovsky Omri Abend Lea Frermann >

📢Paper release📢 : 🔍 Ever wondered how LLMs understand words when all they see are tokens? 🧠 Our latest study uncovers how LLMs reconstruct full words from sub-word tokens, even when misspelled or previously unseen. arxiv.org/pdf/2410.05864 (preprint) 👀 👇 [1/7]

Really cool work by Daria!

Care about LLM evaluation? 🤖 🤔 We bring you🕊️ DOVE a massive (250M!) collection of LLMs outputs On different prompts, domains, tokens, models... Join our community effort to expand it with YOUR model predictions & become a co-author!

RefVNLI Towards Scalable Evaluation of Subject-driven Text-to-image Generation

The longer reasoning LLM thinks - the more likely to be correct, right? Apparently not. Presenting our paper: “Don’t Overthink it. Preferring Shorter Thinking Chains for Improved LLM Reasoning”. Link: arxiv.org/abs/2505.17813 1/n

🚨New paper alert🚨 🧠 Instruction-tuned LLMs show amplified cognitive biases — but are these new behaviors, or pretraining ghosts resurfacing? Excited to share our new paper, accepted to CoLM 2025🎉! See thread below 👇 #BiasInAI #LLMs #MachineLearning #NLProc