Yu (Hope) Hou

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...

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

A bit late to announce, but I’m excited to share that I'll be starting as an assistant professor at UMD CS @univofmaryland.bsky.social this August.

I'll be recruiting PhD students this upcoming cycle for fall 2026. (And if you're a UMD grad student, sign up for my fall seminar!)

🤔 What if you gave an LLM thousands of random human-written paragraphs and told it to write something new -- while copying 90% of its output from those texts?

🧟 You get what we call a Frankentext!

💡 Frankentexts are surprisingly coherent and tough for AI detectors to flag.

Book cover - Lost in Automatic Translation: Navigating Life in English in the Age of Language Technologies. By Vered Shwartz. Publisher: Cambridge University Press.

I guess that now that I have 1% of my Twitter followers follow me here 😅, I should announce it here too for those of you no longer checking Twitter: my nonfiction book, "Lost in Automatic Translation" is coming out this July: lostinautomatictranslation.com. I'm very excited to share it with you!

1/ How can a monolingual English speaker 🇺🇸 decide if an automatic 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

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

Please help us spread the word! 📣

FATE is hiring a pre-doc research assistant! We're looking for candidates who will have completed their bachelor's degree (or equivalent) by summer 2025 and want to advance their research skills before applying to PhD programs.

Wisconsin-Madison's tree-filled campus, next to a big shiny lake

A computer render of the interior of the new computer science, information science, and statistics building. A staircase crosses an open atrium with visibility across multiple floors

I'm joining Wisconsin CS as an assistant professor in fall 2026!! There, I'll continue working on language models, computational social science, & responsible AI. 🌲🧀🚣🏻‍♀️ Apply to be my PhD student!

Before then, I'll postdoc for a year in the NLP group at another UW 🏔️ in the Pacific Northwest

Can you map it to English? The Role of Cross-Lingual Alignment in Multilingual Performance of LLMs Large language models (LLMs) pre-trained predominantly on English text exhibit surprising multilingual capabilities, yet the mechanisms driving cross-lingual generalization remain poorly understood. T...

🔈 NEW PAPER 🔈
Excited to share my paper that analyzes the effect of cross-lingual alignment on multilingual performance
Paper: arxiv.org/abs/2504.09378 🧵

🚨 New Paper 🚨

1/ We often assume that well-written text is easier to translate ✏️

But can #LLMs automatically rewrite inputs to improve machine translation? 🌍

Here’s what we found 🧵

A bit of a mess around the conflict of COLM with the ARR (and to lesser degree ICML) reviews release. We feel this is creating a lot of pressure and uncertainty. So, we are pushing our deadlines:

Abstracts due March 22 AoE (+48hr)
Full papers due March 28 AoE (+24hr)

Plz RT 🙏

Nice modern NLP (Ai) intro talk slides isabelleaugenstein.github.io/slides/2025_... Isabelle Augenstein

🚨 Our team at UMD is looking for participants to study how #LLM agent plans can help you answer complex questions

💰 $1 per question
🏆 Top-3 fastest + most accurate win $50
⏳ Questions take ~3 min => $20/hr+

Click here to sign up (please join, reposts appreciated 🙏): preferences.umiacs.umd.edu

Our FATE MTL team has been working on a series of projects on anthropomorphic AI systems for which we recently put out a few pre-prints I’m excited about. While working on these we tried to think carefully not only about key research questions but also how we study and write about these systems

New synthetic benchmark for multilingual long-context LLMs! Surprisingly, English and Chinese are not the top-performing languages (it's Polish!). We also observe a widening gap between high and low-resource languages as context size increases. Check out the paper for more 👇

🚨 New Position Paper 🚨

Multiple choice evals for LLMs are simple and popular, but we know they are awful 😬

We complain they're full of errors, saturated, and test nothing meaningful, so why do we still use them? 🫠

Here's why MCQA evals are broken, and how to fix them 🧵

⚠️Current methods for generating instruction-following data fall short for long-range reasoning tasks like narrative claim verification.

We present CLIPPER ✂️, a compression-based pipeline that produces grounded instructions for ~$0.5 each, 34x cheaper than human annotations.

Screenshot of top half of first page of paper. The paper is titled: "When People are Floods: Analyzing Dehumanizing Metaphors in Immigration Discourse with Large Language Models". The authors are Julia Mendelsohn (University of Chicago) and Ceren Budak (University of Michigan). The top right corner contains a visual showing the sentence "They want immigrants to pour into and infest this country". The caption says: Figure 1: Dehumanizing sentence likening immigrants to the source domain concepts of Water and Vermin via the words "pour" and "infest". The abstract text on the left reads: Metaphor, discussing one concept in terms of another, is abundant in politics and can shape how people understand important issues. We develop a computational approach to measure metaphorical language, focusing on immigration discourse on social media. Grounded in qualitative social science research, we identify seven concepts evoked in immigration discourse (e.g. "water" or "vermin"). We propose and evaluate a novel technique that leverages both word-level and document-level signals to measure metaphor with respect to these concepts. We then study the relationship between metaphor, political ideology, and user engagement in 400K US tweets about immigration. While conservatives tend to use dehumanizing metaphors more than liberals, this effect varies widely across concepts. Moreover, creature-related metaphor is associated with more retweets, especially for liberal authors. Our work highlights the potential for computational methods to complement qualitative approaches in understanding subtle and implicit language in political discourse.

New preprint!
Metaphors shape how people understand politics, but measuring them (& their real-world effects) is hard.

We develop a new method to measure metaphor & use it to study dehumanizing metaphor in 400K immigration tweets Link: bit.ly/4i3PGm3

#NLP #NLProc #polisky #polcom #compsocialsci
🐦🐦

New open source reasoning model!

Huginn-3.5B reasons implicitly in latent space 🧠

Unlike O1 and R1, latent reasoning doesn’t need special chain-of-thought training data, and doesn't produce extra CoT tokens at test time.

We trained on 800B tokens 👇

I have learned a lot in this project! If you are interested in how NLI can be used in VLMs to complement its representation, check it out!

Accepted at #ICLR2025✨

🧐Which languages benefit the most from vocabulary adaptation?

We introduce VocADT, a new vocabulary adaptation method with a vocabulary adapter.
We explore the impact of various adaptation strategies on languages with diverse scripts and fragmentation to answer this question

COLM 2025 Program Committee Volunteer Form This form is to volunteer to serve on the COLM 2025 (https://colmweb.org/) program committee as a reviewer or an area chair.

We are looking for volunteers for reviewing and AC roles! Please sign up here:
forms.gle/rZp67YvMn1hn...

🎉Announcing... the 2024 TMLR Outstanding Certifications! (aka, our "best paper" awards!)

Are you bursting with anticipation to see what they are? Check out this blog post, and read down-thread!! 🎉🧵👇 1/n
medium.com/@TmlrOrg/ann...

📣 📣 Interested in an internship on human-centred AI, human agency, AI evaluation & the impacts of AI systems? Our team/FATE MLT (Su Lin Blodgett, @qveraliao.bsky.social & I) is looking for a few summer interns 🎉 Apply by Jan 10 for full consideration: jobs.careers.microsoft.com/global/en/jo...

Reminder that we are looking for papers using LLMs for humanities research, for a special issue of the Computational Humanities Research Journal.

Deadline January 31st!

#NLP #DigitalHumanities #CulturalAnalytics

Is your #FAccT2025 paper draft accessible? This is important for both peer review and camera ready publications.

Check out this guide from CHI with tips for both Word and LaTeX users!

sigchi.org/resources/gu...

meme with three rows. "this human-ai decision making leads to unfair outcomes" --> "panik" "let's show explanations to help people be more fair" --> "kalm" "those explanations are based on proxy features" --> "panik"

The Impact of Explanations on Fairness in Human-AI Decision-Making: Protected vs Proxy Features

Despite hopes that explanations improve fairness, we see that when biases are hidden behind proxy features, explanations may not help.

Navita Goyal, Connor Baumler +al IUI’24
hal3.name/docs/daume23...
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starter pack for the Computational Linguistics and Information Processing group at the University of Maryland - get all your NLP and data science here!

go.bsky.app/V9qWjEi

Drake meme. Top panel (dismissive): "Helping people make sense of model errors just by highlighting confabulations." Bottom panel (happy): "Also showing them potential alternatives to those confabulations."

Successfully Guiding Humans with Imperfect Instructions by Highlighting Potential Errors and Suggesting Corrections

When generating instructions for people, we can help them by highlighting potential confabs, AND by suggesting alternatives.

by Lingjun Zhao EMNLP’24

hal3.name/docs/daume24...
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ASL STEM Wiki: Dataset and Benchmark for Interpreting STEM Articles

We develop a continuous signing dataset for ASL on a STEM subset of Wikipedia; challenges suggest problems related to fingerspelling detection, sign linking, & translation.

by Kayo Yin et al EMNLP’24
hal3.name/docs/daume24...
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