Lexing Xie

(2/n)
What is generalizable classification here? We think there are three key elements
1. New data domains - from short informal text to long passages.
2. New moral and value dimensions.
3. New frameworks - e.g. moral foundations, Schwartz human values, and many more!

(3/n)
Our new methodology insight: "all@once LLM prompting strategy" outperforms fine-tuned models across multiple domains and frameworks. Why does it work? It uses inter-label dependencies resembling a classifier chain approach in ML.

(4/n)
MoVa provides resources defining this generalizable classification -- 16 labeled datasets and benchmarking results across four major, theoretically-grounded frameworks: Moral Foundations Theory (MFT), Human Values, Common Morality, and Morality-as-Cooperation (MAC)

(5/n)
MoVa also offers a new application for evaluating psychological surveys:
By using MoVa to score the relevance of moral dimensions for each survey item, we can detect potentially multi-loaded items in instruments like MFQ, MAQ, and PVQ, helping researchers rethink questionnaire design.

So what? This technology could help us:
(1) Analyze Public Discourse: Understand the core values driving large-scale conversations on social and political issues.
(2) Build Better AI: Ensure that artificial intelligence systems communicate in a way that's aligned with basic human ethics #AIalignment

(6/n)
Future work? Generalizable classification across cultures and languages, and investigating generalisable prompting methodology on other subjective text classification tasks.

(7/n)
Led by CMlab PhD student Ziyu Chen, with @ml4x.bsky.social Junfei Sun, Chenxi Li at UChicago, @joshnguyen.bsky.social at UPenn, and Jing Yao, Xiaoyuan Yi and Xing Xie at Microsoft Research Asia

MoVa: Towards Generalizable Classification of Human Morals and Values Identifying human morals and values embedded in language is essential to empirical studies of communication. However, researchers often face substantial difficulty navigating the diversity of theoreti...

(8/n)
Read the paper here arxiv.org/abs/2509.24216
Explore the MoVa resources, data, and code supporting this work here: 👉 github.com/ZiyuChen0410...
#NLP #ResearchTools #DataScience

Identifying human morals and values in language is crucial for analysing lots of human- and AI-generated text.

We introduce "MoVa: Towards Generalizable Classification of Human Morals and Values" - to be presented at @emnlpmeeting.bsky.social oral session next Thu #CompSocialScience #LLMs
🧵 (1/n)

😂

We'll focus on complex information needs with a purpose: on climate change and beyond.
We are open to new algorithms, paradigms for human-AI collaboration, innovations with LLM

Research Fellow on Machine Learning/Natural Language Processing - Canberra / ACT, ACT, Australia Classification: Academic Level BSalary package: $112,916 - $128,026 per annum plus 17% superannuation  Terms: 2x Full time, Fixed Term (24 months)   These positions will provide opportunities to:   C...

2x Research Fellows positions on NLP and ML working w wonderful Jing Jiang, Thang Bui and myself + partners in Data61.
jobs.anu.edu.au/jobs/researc...
Apply by 31 Dec or help spread the words!
(more details below)