Our paper on multilingual reasoning is accepted to Findings of #EMNLP2025! 🎉 (OA: 3/3/3.5/4) We show SOTA LMs struggle with reasoning in non-English languages; prompt-hack & post-training improve alignment but trade off accuracy. 📄 arxiv.org/abs/2505.22888 See you in Suzhou! #EMNLP

📢 New paper: Can unsupervised metrics extracted from MT models detect their translation errors reliably? Do annotators even *agree* on what constitutes an error? 🧐 We compare uncertainty- and interp-based WQE metrics across 12 directions, with some surprising findings! 🧵 1/

“Child-Directed Language Does Not Consistently Boost Syntax Learning in Language Models” I’m happy to share that the preprint of my first PhD project is now online! 🎊 Paper: arxiv.org/abs/2505.23689

[1/]💡New Paper Large reasoning models (LRMs) are strong in English — but how well do they reason in your language? Our latest work uncovers their limitation and a clear trade-off: Controlling Thinking Trace Language Comes at the Cost of Accuracy 📄Link: arxiv.org/abs/2505.22888

✨ New Paper ✨ [1/] Retrieving passages from many languages can boost retrieval augmented generation (RAG) performance, but how good are LLMs at dealing with multilingual contexts in the prompt? 📄 Check it out: arxiv.org/abs/2504.00597 (w/ @arianna-bis.bsky.social @Raquel_Fernández) #NLProc

🎉 First post on Blue: Our paper on **efficient prompt engineering** has been accepted by NAACL2025 Main Conference! 🎉 Key Point: LLMs tend to generate better responses when the likelihood of the question segment is higher. I.e. p(question) ∝ Performance Paper available at: arxiv.org/abs/2411.07773