Gabrielle Kaili-May Liu
50 posts
@pybeebee Warm welcome! I will try to attend your poster.
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Traveling to #EMNLP2025 to present our work “MetaFaith: Faithful Natural Language Uncertainty Expression in LLMs” this week! 🇨🇳
Come by & let's chat about LLM faithfulness / uncertainty / calibration!
📍Poster Session 7 @ Hall C3
🗓Fri, Nov 7 @ 2-3:30p
🔗tinyurl.com/metafaith
Gabrielle Kaili-May Liu@pybeebee
🎉 Delighted to announce that MetaFaith has been accepted to #EMNLP2025 Main! In this work we systematically study how well LLMs can express their internal uncertainty in words, offering a metacognition-inspired way to improve this ability 🧠✨ Check out more details below!👇
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(12/n) 🙏 Lastly a big shoutout and thanks to the co-authors of this work, @bryanlics, @armancohan, Will Walden, and @EYangTW!
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(11/n) If you're interested in RAG, multi-hop reasoning, or evaluation of LLMs — give our paper a read and check out CRUMQs!
📄 Paper: arxiv.org/abs/2510.11956
🔗 Code: github.com/pybeebee/CRUMQs
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🚀 RAG systems excel at answering questions—but what happens when the corpus has NO answer or complex multi-hop reasoning is required?
Moreover, how can we build benchmarks to stress-test RAG systems in such settings in a realistic way?
See our new preprint to find out! 🧵👇
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Excited to present this at #EMNLP2025 in just over a month!
It turns out that even flagship models like GPT-5 still struggle at faithfully expressing uncertainty 🤔
📊 Full results for the newest models are now live👇
arxiv.org/abs/2505.24858
Gabrielle Kaili-May Liu@pybeebee
🎉 Delighted to announce that MetaFaith has been accepted to #EMNLP2025 Main! In this work we systematically study how well LLMs can express their internal uncertainty in words, offering a metacognition-inspired way to improve this ability 🧠✨ Check out more details below!👇
English
Gabrielle Kaili-May Liu 리트윗함
1/9 🚀 New paper: Demystifying Scientific Problem-Solving in LLMs — How does reasoning enhancement affect knowledge recall, and do LLMs benefit from external knowledge complimentary to reasoning?
Tldr;
📊 SciReas: holistic and efficient evaluation suite for scientific reasoning
🧠 KRUX: a novel framework to study knowledge vs reasoning in LLMs
🔑 Findings: knowledge is a bottleneck; reasoners + in-context knowledge help; long CoT helps knowledge recall/utilization
English
🎉 Delighted to announce that MetaFaith has been accepted to #EMNLP2025 Main! In this work we systematically study how well LLMs can express their internal uncertainty in words, offering a metacognition-inspired way to improve this ability 🧠✨
Check out more details below!👇
Gabrielle Kaili-May Liu@pybeebee
🔥 Excited to share MetaFaith: Understanding and Improving Faithful Natural Language Uncertainty Expression in LLMs🔥 How can we make LLMs talk about uncertainty in a way that truly reflects what they internally "know"? Check out our new preprint to find out! Details in 🧵(1/n):
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I will be presenting our work 𝗠𝗗𝗖𝘂𝗿𝗲 at #ACL2025NLP in Vienna this week! 🇦🇹
Come by if you’re interested in multi-doc reasoning and/or scalable creation of high-quality post-training data!
📍 Poster Session 4 @ Hall 4/5
🗓️ Wed, July 30 | 11-12:30
🔗 aclanthology.org/2025.acl-long.…
Gabrielle Kaili-May Liu@pybeebee
🔥Thrilled to introduce MDCure: A Scalable Pipeline for Multi-Document Instruction-Following 🔥 How can we systematically and scalably improve LLMs' ability to handle complex multi-document tasks? Check out our new preprint to find out! Details in 🧵 (1/n):
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Gabrielle Kaili-May Liu 리트윗함
Excited to see more investigation into LLM creativity. We have some pioneering work on this topic as well:
Creativity or Brute Force?
Using Brainteasers as a Window into the
Problem-Solving Abilities of Large Language Models. arxiv.org/pdf/2505.10844.
Yiyou Sun@YiyouSun
🚨 New study on LLM's reasoning boundary! Can LLMs really think out of the box? We introduce OMEGA—a benchmark probing how they generalize: 🔹 RL boosts accuracy on slightly harder problems with familiar strategies, 🔹 but struggles with creative leaps & strategy composition. 👇
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(15/n) Big shoutout and thanks to the co-authors of this work, @_galyo, @clu_avi, Idan Szpektor, @timrudner, and @armancohan!
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(14/n) If you're interested in LLM trustworthiness, uncertainty quantification, human-AI collaboration, or even metacognition — give our paper a read and check out MetaFaith! We'd love feedback or questions.
📄 Paper: arxiv.org/abs/2505.24858
🔗 Github: github.com/yale-nlp/MetaF…
English
🔥 Excited to share MetaFaith: Understanding and Improving Faithful Natural Language Uncertainty Expression in LLMs🔥
How can we make LLMs talk about uncertainty in a way that truly reflects what they internally "know"?
Check out our new preprint to find out!
Details in 🧵(1/n):
English