Lightnews — Scholar-powered news

Reposted by Stella Li

Maarten Sap @maartensap.bsky.social · 2d

Day 1 (Tue Oct 7) 4:30-6:30pm, Poster Session 2

Poster #77: ALFA: Aligning LLMs to Ask Good Questions: A Case Study in Clinical Reasoning; led by
@stellali.bsky.social & @jiminmun.bsky.social

1 1 2

Stella Li @stellali.bsky.social · Jul 22

This project was done as part of the Meta FAIR AIM mentorship program. Special thanks to my amazing collaborators and awesome mentors @melaniesclar.bsky.social @jcqln_h @hunterjlang @AnsongNi @andrew_e_cohen @jacoby_xu @chan_young_park @tsvetshop.bsky.social ‪@asli-celikyilmaz.bsky.social‬ 🫶🏻💙

Stella Li @stellali.bsky.social · Jul 22

✨PrefPalette🎨 bridges cognitive science, social psychology, and AI for explainable preference modeling✨

📖Paper: arxiv.org/abs/2507.13541
💻Code: github.com/stellalisy/P...

Join us in shaping interpretable AI that you can trust and control🚀Feedback welcome!
#AI #Transparency

PrefPalette: Personalized Preference Modeling with Latent Attributes

Personalizing AI systems requires understanding not just what users prefer, but the reasons that underlie those preferences - yet current preference models typically treat human judgment as a black bo...

arxiv.org

1 1 3

Stella Li @stellali.bsky.social · Jul 22

🌍Bonus: PrefPalette🎨 is a computational social science goldmine!

📊 Quantify community values at scale
📈 Track how norms evolve over time
🔍 Understand group psychology
📋 Move beyond surveys to revealed preferences

1

Stella Li @stellali.bsky.social · Jul 22

💡Potential real-world applications:

🛡️Smart content moderation—explains why content is flagged/decisions are made

🎯Interpretable LM alignment—revealing prominent attributes

⚙️Controllable personalization—giving user agency to personalize select attributes

1

Stella Li @stellali.bsky.social · Jul 22

🔍More importantly‼️we can see WHY preferences differ:

r/AskHistorians:📚values verbosity
r/RoastMe:💥values directness
r/confession:❤️values empathy

We visualize each group’s unique preference decisions—no more one-size-fits-all. Understand your audience at a glance🏷️

1

Stella Li @stellali.bsky.social · Jul 22

🏆Results across 45 Reddit communities:

📈Performance boost: +46.6% vs GPT-4o
💪Outperforms other training-based baselines w/ statistical significance
🕰️Robust to temporal shifts—trained pref models can be used out-of-the box!

1

Stella Li @stellali.bsky.social · Jul 22

⚙️How it works (pt.2)

1: 🎛️Train compact, efficient detectors for every attribute

2: 🎯Learn community-specific attribute weights during preference training

3: 🔧Add attribute embeddings to preference model for accurate & explainable predictions

1

Stella Li @stellali.bsky.social · Jul 22

⚙️How it works (prep stage)

📜Define 19 sociolinguistics & cultural attributes from literature
🏭Novel preference data generation pipeline to isolate attributes

Our data gen pipeline generates pairwise data on *any* decomposed dimension, w/ applications beyond preference modeling

1

Stella Li @stellali.bsky.social · Jul 22

Meet PrefPalette🎨! Our approach:

🔍⚖️models preferences w/ 19 attribute detectors and dynamic, context-aware weights

🕶️👍uses unobtrusive signals from Reddit to avoid response bias

🧠mirrors attribute-mediated human judgment—so you know not just what it predicts, but *why*🧐

1

Stella Li @stellali.bsky.social · Jul 22

🔬Cognitive science reveals how humans break choices into attributes, e.g.:

😂 Humor
❤️ Empathy
💬 Conformity
...then weight them based on context (e.g. comedy vs counseling).

These traits shape every decision, from product picks to conversation tone. Your mind is a colorful palette🎨

1 1

Stella Li @stellali.bsky.social · Jul 22

🚨Current preference models only output a reward/score:

❌No transparency in decision-making
❌Personalization breaks easily, one-size-fits-all scores
❌Use explicit annotations (response bias)

They can’t adapt to individual tastes, can’t debug errors, and fail to build trust🙅

1

Stella Li @stellali.bsky.social · Jul 22

WHY do you prefer something over another?

Reward models treat preference as a black-box😶‍🌫️but human brains🧠decompose decisions into hidden attributes

We built the first system to mirror how people really make decisions in our recent COLM paper🎨PrefPalette✨

Why it matters👉🏻🧵

1 2 6

Reposted by Stella Li

Eric Frankel @esfrankel.bsky.social · Feb 27

Want to quickly sample high-quality images from diffusion models, but can’t afford the time or compute to distill them? Introducing S4S, or Solving for the Solver, which learns the coefficients and discretization steps for a DM solver to improve few-NFE generation.

Thread 👇 1/

1 1 3

Stella Li @stellali.bsky.social · Feb 21

This work was jointly done with the amazing @jiminmun.bsky.social !
And huge shout out to our awesome collaborators and mentors faebrahman.bsky.social, Jonathan Ilgen, Yulia (tsvetshop.bsky.social) and maartensap.bsky.social 🩵🥰

Faeze Brahman (@faebrahman.bsky.social)

faebrahman.bsky.social

1

Stella Li @stellali.bsky.social · Feb 21

ALFA is open-source! There are many more analyses in the paper, check them out!📖

🔗 Paper: arxiv.org/abs/2502.14860
💻 Code: github.com/stellalisy/ALFA
🤖 Data: tinyurl.com/MedicAskDocsData

Join us in the effort to make LLMs better at question asking! 🚀
#Healthcare #NLProc #AI4Science

Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning

Large language models (LLMs) often fail to ask effective questions under uncertainty, making them unreliable in domains where proactive information-gathering is essential for decisionmaking. We presen...

arxiv.org

1 1

Stella Li @stellali.bsky.social · Feb 21

Why this matters for AI safety & reliability: 🛡️

Better information gathering = Better decisions✅
Proactive questioning = Fewer blind spots🧐
Structured attributes = More controllable AI🤖
Interactive systems = More natural AI assistants🫶🏻

1 1

Stella Li @stellali.bsky.social · Feb 21

ALFA isn't just for medicine! The framework could be adapted to ANY field where proactive information gathering matters:

Legal consultation ⚖️
Financial advising 💰
Educational tutoring 📚
Investigative journalism 🕵️

Anywhere an AI needs to ask (not just answer), you should try ALFA out!🌟

1 1

Stella Li @stellali.bsky.social · Feb 21

🌟 Impressive Generalization!
ALFA-trained models maintain strong performance even on completely new interactive medical tasks (MediQ-MedQA).
highlighting ALFA’s potential for broader applicability in real-world clinical scenarios‼️

1 1

Stella Li @stellali.bsky.social · Feb 21

🔬 Key Finding #2: Every Attribute Matters!

Removing any single attribute hurts performance‼️

Grouping general (clarify, focus, answerability) vs. clinical (medical accuracy, diagnostic relevance, avoiding DDX bias) attributes leads to drastically different outputs👩‍⚕️

Check out some cool examples!👇

1 1

Stella Li @stellali.bsky.social · Feb 21

🔬 Key Finding #1: Preference Learning > Supervised Learning

Is it just good synthetic data❓ No❗️

Simply showing good examples isn't enough! Models need to learn directional differences between good and bad questions.

(but only SFT no DPO also doesn't work!)

1 1

Stella Li @stellali.bsky.social · Feb 21

Results show ALFA’s strengths🚀
ALFA-aligned models achieve:
⭐️56.6% reduction in diagnostic errors🦾
⭐️64.4% win rate in question quality✅
⭐️Strong generalization.
in comparison with baseline SoTA instruction-tuned LLMs.

1 2

Stella Li @stellali.bsky.social · Feb 21

The secret sauce of ALFA? 🔍
6 key attributes from theory (cognitive science, medicine):
General:
- Clarity ✨
- Focus 🎯
- Answerability 💭
Clinical:
- Medical Accuracy 🏥
- Diagnostic Relevance 🔬
- Avoiding Bias ⚖️
Each attribute contributes to different aspect of the complex goal of question asking!

1 1

Stella Li @stellali.bsky.social · Feb 21

📚 Exciting Dataset Release: MediQ-AskDocs!
17k real clinical interactions
80k attribute-specific question variations
302 expert-annotated scenarios
Perfect for research on interactive medical AI
First major dataset for training & evaluating medical question-asking! 🎯
huggingface.co/datasets/ste...

stellalisy/MediQ_AskDocs_preference · Datasets at Hugging Face

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

huggingface.co

1 1

Stella Li @stellali.bsky.social · Feb 21

Introducing ALFA: ALignment via Fine-grained Attributes 🎓
A systematic, general question-asking framework that:
1️⃣ Decomposes the concept of good questioning into attributes📋
2️⃣ Generates targeted attribute-specific data📚
3️⃣ Teaches LLMs through preference learning🧑‍🏫

1 1