Kaiser Sun

@kaiserwholearns.bsky.social

Ph.D. student at @jhuclsp, human LM that hallucinates. Formerly @MetaAI, @uwnlp, and @AWS they/them🏳️‍🌈 #NLProc #NLP Crossposting on X.

If an LLM agent notices conflicts, will it tell you? Our #EMNLP2026 Findings paper finds that higher accuracy doesn't always mean greater epistemic humility: agents can spot conflicts early yet leave uncertainty out of wrong answers. 📄 arxiv.org/abs/2610.12360 🧵👇 #NLProc #LLM

Accurate but Not Humble: Evaluating Epistemic Humility in LLM...

When retrieved evidence contradicts an agent's prior beliefs, does it revise its answer, acknowledge uncertainty, or persist with an incorrect conclusion? Existing evaluations of agentic systems...

arxiv.org

What happens when an LLM is asked to use information that contradicts its knowledge? We explore knowledge conflict in a new preprint📑 TLDR: Performance drops, and this could affect the overall performance of LLMs in model-based evaluation.📑🧵⬇️ 1/8 #NLProc #LLM #AIResearch

What Is Seen Cannot Be Unseen: The Disruptive Effect of Knowledge Conflict on Large Language Models

Large language models frequently rely on both contextual input and parametric knowledge to perform tasks. However, these sources can come into conflict, especially when retrieved documents contradict…

arxiv.org

Had so many fruitful discussions and made many friends this #NAACL2025 🌵🏜️Thanks for everyone who came to my poster or listened to me talking about my audacious thoughts! 😜 (I should have printed more stickers as they were more popular than I anticipated😅)

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Dialects lie on continua of (structured) linguistic variation, right? And we can’t collect data for every point on the continuum...🤔 📢 Check out DialUp, a technique to make your MT model robust to the dialect continua of its training languages, including unseen dialects. arxiv.org/abs/2501.16581

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with reasonable freedom, depending on the scale/focus of the business. Case in point, we are looking to expand the research/foundation models team at Orby AI and are looking for highly motivated researchers and ML/Research engineers. Please reach out if you're interested in learning more! /fin

🚨I too am on the job market‼️🤯 I'm searching for faculty positions/postdocs in multilingual/multicultural NLP, vision+language models, and eval for genAI! I'll be at #NeurIPS2024 presenting our work on meta-evaluation for text-to-image faithfulness! Let's chat there! Papers in🧵, see more: saxon.me

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Excited to share OLMo 2! 🐟 7B and 13B weights, trained up to 4-5T tokens, fully open data, code, etc 🐠 better architecture and recipe for training stability 🐡 staged training, with new data mix Dolmino🍕 added during annealing 🦈 state-of-the-art OLMo 2 Instruct models #nlp #mlsky links below👇

A scatter plot comparing language models by performance (y-axis, measured in average performance on 10 benchmarks) versus training computational cost (x-axis, in approximate FLOPs). The plot shows OLMo 2 models (marked with stars) achieving Pareto-optimal efficiency among open models, with OLMo-2-13B and OLMo-2-7B sitting at the performance frontier relative to other open models like DCLM, Llama 3.1, StableLM 2, and Qwen 2.5. The x-axis ranges from 4x10^22 to 2x10^24 FLOPs, while the y-axis ranges from 35 to 70 benchmark points.