iiinova — where your digital self keeps evolving

Reposted by Lingjun Zhao

Hal Daumé III @haldaume3.bsky.social · 21/11/2025

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/j... *Assistant-level candidates: apply to departments, mentioning AIM in a cover letter

umd.wd1.myworkdayjobs.com

Senior Tenure Track Faculty at the Artificial Intelligence Interdisciplinary Institute at Maryland (AIM) - Associate Professor/Professor (Open Rank Joint Appointment)

Job Description Summary Organization Summary Statement: The Artificial Intelligence Interdisciplinary Institute at Maryland - AIM (aim.umd.edu) - is hiring 40 faculty over the next several years, incl...

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Reposted by Lingjun Zhao

Alexander Hoyle @alexanderhoyle.bsky.social · 27/10/2025

LLMs are often used for text annotation, especially in social science. In some cases, 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 EMNLP paper has some answers🧵 arxiv.org/pdf/2507.00828

A diagram illustrating pointwise scoring with a large language model (LLM). At the top is a text box containing instructions: 'You will see the text of a political advertisement about a candidate. Rate it on a scale ranging from 1 to 9, where 1 indicates a positive view of the candidate and 9 indicates a negative view of the candidate.' Below this is a green text box containing an example ad text: 'Joe Biden is going to eat your grandchildren for dinner.' An arrow points down from this text to an illustration of a computer with 'LLM' displayed on its monitor. Finally, an arrow points from the computer down to the number '9' in large teal text, representing the LLM's scoring output. This diagram demonstrates how an LLM directly assigns a numerical score to text based on given criteria

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Lingjun Zhao @lingjunz.bsky.social · 14/10/2025

🚨 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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Reposted by Lingjun Zhao

Marine Carpuat @marinecarpuat.bsky.social · 18/06/2025

What should Machine Translation research look like in the age of multilingual LLMs? Here’s one answer from researchers across NLP/MT, Translation Studies, and HCI. "An Interdisciplinary Approach to Human-Centered Machine Translation" arxiv.org/abs/2506.13468

arxiv.org

An Interdisciplinary Approach to Human-Centered Machine Translation

Machine Translation (MT) tools are widely used today, often in contexts where professional translators are not present. Despite progress in MT technology, a gap persists between system development and...

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Reposted by Lingjun Zhao

Jordan Boyd-Graber @boydgraber.bsky.social · 05/06/2025

Do you like trivia? Can you spot when AI is feeding you BS? Or can you make AIs turn themselves inside out? Then on June 14 at College Park (or June 21 online), we have a competition for you.

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Lingjun Zhao @lingjunz.bsky.social · 20/05/2025

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? Link: arxiv.org/abs/2502.15079

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Lingjun Zhao @lingjunz.bsky.social · 30/03/2025

For the ACL ARR review, I’ve heard complaints about the workload—some reviewers have 16 papers. Even though I only need to write 1 rebuttal and respond to 4, it still feels substantial. For those managing more (thank you!), it can be difficult to thoroughly engage with every rebuttal.

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Reposted by Lingjun Zhao

Hal Daumé III @haldaume3.bsky.social · 04/03/2025

There is a new version of the Research Plan for NIST's AI Safety Consortium (AISIC) in response to EOs. I did a diff. Out: safety, responsibility, sociotechnical, fairness, working w fed agencies, authenticating content, watermarking, RN of CBRN, autonomous replication, ctrl of physical systems >

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Reposted by Lingjun Zhao

Paola Cascante-Bonilla @pcascanteb.bsky.social · 05/12/2024

This is my first time serving as an AC for a big conference. Just read this great work by Goyal et al. arxiv.org/abs/2411.11437 I'm optimizing for high coverage and low redundancy—assigning reviewers based on relevant topics or affinity scores alone feels off. Seniority and diversity matter!

arxiv.org

Causal Effect of Group Diversity on Redundancy and Coverage in Peer-Reviewing

A large host of scientific journals and conferences solicit peer reviews from multiple reviewers for the same submission, aiming to gather a broader range of perspectives and mitigate individual biase...

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