Lingjun Zhao

32 posts

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Lingjun Zhao

Lingjun Zhao

@lingjunzh

CS PhD student @UMD, enhancing communication efficacy of (visual) language models: faithfulness, pragmatics and alignment

College Park, MD Katılım Aralık 2012
79 Takip Edilen85 Takipçiler
Lingjun Zhao retweetledi
Khanh Nguyen
Khanh Nguyen@khanhxuannguyen·
🎓 Internship Opportunity – Deep Research Agents @ Microsoft M365 🎓 Hi all! Our team at Microsoft M365 is hiring interns for Spring 2026 (tentative start date: Feb 1). The position is flexible: full-time or part-time, in-person or remote (within the US). You’ll work closely with me (Khanh) and other applied scientists on evaluating and developing deep research agents for enterprise scenarios. We are a product team overseeing Researcher (Microsoft’s deep research agent), but the internship will be research-oriented with a strong focus on publishing papers. This is a unique chance to do serious research while tackling real-world problems that impact millions of users. 👉 Apply here (this is just our gateway for hiring interns; don’t worry about the content): centific.wd1.myworkdayjobs.com/Centific_Globa… Khanh’s research background: machineslearner.com/about
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Maarten Sap (he/him)
Maarten Sap (he/him)@MaartenSap·
CoT explanations can foster blind trust in users; we need to encourage critical thinking about model outputs and explanations! We find that users who agree with a model's output (a) trust the model more and (b) are less likely to detect errors in model explanations.
Eunkyu Eunice Park@uunicee_

🧵Sharing our most-recent work! Critical or Compliant? The Double-Edged Sword of Reasoning in Chain-of-Thought Explanations

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Hal Daumé III
Hal Daumé III@haldaume3·
AIM's 2nd round of TTK hiring - building up to 30 - is up! 📅 Ddl 12/22/25 🔬 Accessibility & Learning, plus Sustainability & Social Justice 🧑‍🏫 Associate/Full Prof* 🔗 umd.wd1.myworkdayjobs.com/en-US/UMCP/job… *Assistant-level candidates: apply to departments, mentioning AIM in a cover letter
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John Hewitt
John Hewitt@johnhewtt·
Come do a PhD with me at Columbia! My lab tackles basic problems in alignment, interpretability, safety, and capabilities of language systems. If you love adventuring in model internals and behaviors---to understand and improve---let's do it together! pic: a run in central park
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Sarah Wiegreffe
Sarah Wiegreffe@sarahwiegreffe·
I am recruiting 2 PhD students to work on LM interpretability at UMD @umdcs starting in fall 2026! We are #3 in AI and #4 in NLP research on @CSrankings. Come join us in our lovely building just a few miles from Washington, D.C. Details in 🧵
Sarah Wiegreffe tweet media
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Nishant Balepur
Nishant Balepur@NishantBalepur·
Just landed in Suzhou for #EMNLP2025! 🛬🦀🍜 Excited to present our paper showing human preferences != true helpfulness on: 📅 November 6th, 10:30 📌 Hall C3, Poster Session 4 Also happy to chat about evals, human feedback, personalization, deep research, and vibe coding! 😁
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Nishant Balepur@NishantBalepur

🚨 New Paper! 🚨 We want ~helpful~ LLMs, and RLHF-ing them with user preferences and reward models will get us there, right? WRONG! 🙅❌⛔️ Our #EMNLP2025 paper finds a major helpfulness-preferences gap: user/LLM judgments + agent simulations can totally miss what helps users

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Dayeon (Zoey) Ki
Dayeon (Zoey) Ki@zoeykii·
Unfortunately I won't be at #EMNLP2025 but my advisor @MarineCarpuat will be there in-person presenting our work! ❤️ Come stop by if you're interested in designing decision support for reliable human-AI interaction ✨ 📆 11/5 Wednesday 2:30-4:00 pm 📍 Hall C (Session 4)
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Dayeon (Zoey) Ki@zoeykii

1/ 📧 Ever wondered if you can trust a machine translation before hitting send? We study how different kinds of quality feedback shape monolingual users’ trust, reliance, and decisions 🧠🗣️ Appearing at #EMNLP2025!

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Maarten Sap (he/him)
Maarten Sap (he/him)@MaartenSap·
Coding agents should always consider and adapt their user's preferences! Our new 🧠🤖 ToM-SWE (theory of mind SWE) coding agent left real human users much happier than existing coding agents! See below for more info
Xuhui Zhou@nlpxuhui

Hoping your coding agents could understand you and adapt to your preferences? Meet TOM-SWE, our new framework for coding agents that don’t just write code, but model the user's mind persistently (ranging from general preferences to small details) arxiv: arxiv.org/abs/2510.21903 ❓Motivation: Most coding agents today can plan, edit, run, and test code. But they still fail at a key part of real-world development, understanding the user! Underspecified, shifting, or context-dependent instructions can easily break them. You must have those moments when coding agents were running for 10 minutes and ended up producing things largely misaligned. (1/)

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Alexander Hoyle
Alexander Hoyle@miserlis_·
LLMs are increasingly used for text annotation, esp. in the social sciences. Often, this involves placing text items on a scale: eg, 1 for liberal and 9 for conservative There are a few ways to accomplish this task. Which work best? Our new #EMNLP2025 paper has some answers 🧵
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HyoJung Han
HyoJung Han@h__j___han·
Lots of work on cross-lingual alignment encourages multilingual LLMs to generalize knowledge across languages. But this push for uniformity creates a tension: what happens to knowledge that should remain local? We look into this trade-off of transfer and cultural erasure:🧵
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Nishant Balepur
Nishant Balepur@NishantBalepur·
🚨 New Paper! 🚨 We want ~helpful~ LLMs, and RLHF-ing them with user preferences and reward models will get us there, right? WRONG! 🙅❌⛔️ Our #EMNLP2025 paper finds a major helpfulness-preferences gap: user/LLM judgments + agent simulations can totally miss what helps users
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Lingjun Zhao
Lingjun Zhao@lingjunzh·
🚨 New #EMNLP2025 (main) paper! LLMs often produce inconsistent explanations (62–86%), hurting faithfulness and trust in explainable AI. We introduce PEX consistency, a measure for explanation consistency, and show that optimizing it via DPO improves faithfulness by up to 9.7%.
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Yoo Yeon Sung@ACL2025
Yoo Yeon Sung@ACL2025@YooYeonSung1·
I’ll be presenting this work in Room 1.62 today! If you're curious about how calibration errors in LLMs can be measured through human calibration, come find me and @enfleisig! 📍Oral Session 3 - HC: Human-centered NLP 📅Monday, July 28@ 2PM
Yoo Yeon Sung@ACL2025@YooYeonSung1

🎉Our GRACE paper is heading to #ACL2025 Main conference! 🇦🇹 LLMs don’t just make mistakes; they make them with confidence, often more than people. Excited to push the boundaries of how we evaluate and understand LMs alongside humans! 👥🤝🤖 Grateful for amazing collab!

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Ruijie Zheng
Ruijie Zheng@ruijie_zheng12·
Representation also matters for VLA models! Introducing FLARE: Robot Learning with Implicit World Modeling. With future latent alignment objective, FLARE significantly improves policy performance on multitask imitation learning & unlocks learning from egocentric human videos.
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Dayeon (Zoey) Ki
Dayeon (Zoey) Ki@zoeykii·
1/ How can a monolingual English speaker 🇺🇸 decide if a French translation 🇫🇷 is good enough to be shared? Introducing ❓AskQE❓, an #LLM-based Question Generation + Answering framework that detects critical MT errors and provides actionable feedback 🗣️ #ACL2025
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Lingjun Zhao
Lingjun Zhao@lingjunzh·
We introduce a super simple yet effective strategy to improve video-language alignment (+18%): add hallucination correction in your training objective👌 Excited to share our accepted paper at ACL: Can Hallucination Correction Improve Video-language Alignment
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Ruijie Zheng
Ruijie Zheng@ruijie_zheng12·
Excited to introduce Gr00T-N1! Setting itself apart from existing VLA models, Gr00T-N1 pioneers a novel synthetic data generation strategy, leveraging a diverse suite of simulation environments & demonstration, and action-free trajectories from video generation model. Try it out!
Jim Fan@DrJimFan

Excited to announce GR00T N1, the world’s first open foundation model for humanoid robots! We are on a mission to democratize Physical AI. The power of general robot brain, in the palm of your hand - with only 2B parameters, N1 learns from the most diverse physical action dataset ever compiled and punches above its weight: - Real humanoid teleoperation data. - Large-scale simulation data: we are open-sourcing 300K+ trajectories! - Neural trajectories: we apply SOTA video generation models to “hallucinate” new synthetic data that features accurate physics in pixels. Using Jensen’s words, “systematically infinite data”! - Latent actions: we develop novel algorithms to extract action tokens from in-the-wild human videos and neural generated videos. GR00T N1 is a single end-to-end neural net, from photons to actions: - Vision-Language Model (System 2) that interprets the physical world through vision and language instructions, enabling robots to reason about their environment and instructions, and plan the right actions. - Diffusion Transformer (System 1) that “renders” smooth and precise motor actions at 120 Hz, executing the latent plan made by System 2. We deploy N1 on GR1 robot, 1X Neo robot, and a large collection of simulation benchmarks. N1 achieves up to +30% boost in diverse manipulation tasks for household and industrial settings. While humanoid robots are the main focus of N1, our model also supports cross-embodiment. We finetune it to work on the $110 HuggingFace LeRobot SO100 robot arm! Open robot brain runs on open hardware. Sounds just right. Let’s solve robotics, together, one token at a time. Links to our Whitepaper, Github repo, HuggingFace model, and open dataset page in the thread: 🧵

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