Please share widely!

My research focuses on human-centered NLP, both in evaluating and training LLMs as well as designing safe and reliable human-LM interactions. More information here!
katezhou.github.io

Application fee waivers can be requested here: gradschool.cornell.edu/admissions/a...

and @hannah-cha.bsky.social

Together, we’re excited to continue work on developing LLMs for the needs of a broader user audience! This work is done in collaboration with:

@gligoric.bsky.social @myra.bsky.social @mlam.bsky.social @jurafsky.bsky.social

@stanfordnlp.bsky.social @togetherai.bsky.social

We outline several strategies known in the HCI literature to elevate non-adopter needs and integrate them into LLM development:
1️⃣ re-balancing data annotation and interaction logs
2️⃣ participatory design for developing evaluations
3️⃣ non-adopter-centered task ideation

Many non-adopters have legitimate reasons to resist adopting or to stop using chat models; however, as research practitioners who have the power to design and shape model capabilities, we urge the community to develop technologies where non-use is a choice, rather than an inevitable circumstance.

From users interviews (n=23) and an online survey (n=230), we found:
1️⃣ Non-adopters are interested in chat models, but face barriers to adoption
2️⃣ Non-adopters prioritize tasks rarely reflected in model evals: navigating healthcare portals, coordinating caregiving, contextualized IR

Many current methods rely on logs, preferences, and feedback from existing users—who represent a narrow slice of the population.

Adopter-centered methods risk widening the divide between adopters and non-adopters as datasets, benchmarks, and evaluations evolve around current adopter needs.

Congrats!!!

Congrats!!! 🥳🤩

Here are some graphs