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University of Southern California

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Svitlana Volkova

Aptima Inc.

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References (42)

[1]
The Llama 3 Herd of Models
2024Abhimanyu Dubey, Abhinav Jauhri et al.
[2]
Community-Cross-Instruct: Unsupervised Instruction Generation for Aligning Large Language Models to Online Communities
2024Zihao He, Rebecca Dorn et al.
[3]
Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators
2024Yann Dubois, Bal'azs Galambosi et al.
[4]
Judging LLM-as-a-judge with MT-Bench and Chatbot Arena
2023Lianmin Zheng, Wei-Lin Chiang et al.
[5]
Direct Preference Optimization: Your Language Model is Secretly a Reward Model
2023Rafael Rafailov, Archit Sharma et al.
[6]
NormBank: A Knowledge Bank of Situational Social Norms
2023Caleb Ziems, Jane Dwivedi-Yu et al.
[7]
Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling
2023Stella Biderman, Hailey Schoelkopf et al.
[8]
Constitutional AI: Harmlessness from AI Feedback
2022Yuntao Bai, Saurav Kadavath et al.
[9]
Training language models to follow instructions with human feedback
2022Long Ouyang, Jeff Wu et al.
[10]
Understanding Dataset Difficulty with V-Usable Information
2021Kawin Ethayarajh, Yejin Choi et al.
[11]
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜
2021Emily M. Bender, Timnit Gebru et al.
[12]
Decolonising Speech and Language Technology
2020Steven Bird
[13]
Social Media, Echo Chambers, and Political Polarization
2020Pablo Barberá
[14]
Social Biases in NLP Models as Barriers for Persons with Disabilities
2020Ben Hutchinson, Vinodkumar Prabhakaran et al.
[15]
Directions in abusive language training data, a systematic review: Garbage in, garbage out
2020Bertie Vidgen, Leon Derczynski
[16]
Does Transparency in Moderation Really Matter?
2019Shagun Jhaver, A. Bruckman et al.
[17]
How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings
2019Kawin Ethayarajh
[18]
Preventing harassment and increasing group participation through social norms in 2,190 online science discussions
2019J. N. Matias
[19]
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019Jinhyuk Lee, WonJin Yoon et al.
[20]
Deep Reinforcement Learning from Human Preferences
2017P. Christiano, Jan Leike et al.

Showing 20 of 42 references

Founder's Pitch

"Align language models with community norms using density-guided response optimization without explicit preference labeling."

AI AlignmentScore: 5View PDF ↗

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4/4 signals

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2.5

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Why It Matters

This research enables language models to align with diverse online community norms using implicit signals, reducing reliance on explicit preference data which can be hard to collect ethically and practically for many communities.

Product Angle

Develop a moderation tool or plugin for community platforms to ensure language model outputs conform to community-specific standards without explicit data collection.

Disruption

Replaces more rigid, manually tuned systems for model alignment that depend heavily on explicit human annotation and supervision.

Product Opportunity

Growing demand for customized language models in niche online communities that lack resources for defining explicit preferences. Social media companies and forum administrators are potential customers who require moderation solutions.

Use Case Idea

AI moderation tools in online forums that dynamically adapt to emerging community norms and provide guidance for human moderators.

Science

The research introduces DGRO, which leverages the natural acceptance patterns of a community in embedding space as implicit preference signals to align language models. By focusing on high-density regions where accepted content clusters, the approach extracts geometric structure representing community norms.

Method & Eval

The approach was tested across various online communities using labeled preference data to verify that local density correlates with human judgments, and extended to settings with scarce annotation to check if the derived preferences produce better outcomes than existing baselines.

Caveats

Reliant on implicit signals, which may be biased and not universally endorsed by all community members. Potentially amplifies existing biases within community norms.

Author Intelligence

Patrick Gerard

LEAD
University of Southern California
pgerard@isi.edu

Svitlana Volkova

Aptima Inc.
svolkova@aptima.com