2 citations · 3 across the 5 of their papers we have counts for
7 papers
Verify to Amplify: Improving Reasoning via Learned Chain-of-Thought Verification
Maria-Florina Balcan, Avrim Blum, Kiriaki Fragkia +2
Large Language Models (LLMs) using chain-of-thought have demonstrated great potential for solving complex reasoning and planning tasks. Despite these advances, LLM-generated output…
SAIL: Self-Amplified Iterative Learning for Diffusion Model Alignment with Minimal Human Feedback
Xiaoxuan He, Siming Fu, Wanli Li +5
Aligning diffusion models with human preferences remains challenging, particularly when reward models are unavailable or impractical to obtain, and collecting large-scale preferenc…
Honesty over Accuracy: Trustworthy Language Models through Reinforced Hesitation
Mohamad Amin Mohamadi, Tianhao Wang, Zhiyuan Li
Modern language models fail a fundamental requirement of trustworthy intelligence: knowing when not to answer. Despite achieving impressive accuracy on benchmarks, these models pro…
Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning
Wenjin Mo, Zhiyuan Li, Minghong Fang +1
Federated learning (FL) allows multiple clients to collaboratively train a global machine learning model with coordination from a central server, without needing to share their raw…
On Learning Verifiers and Implications to Chain-of-Thought Reasoning
Maria-Florina Balcan, Avrim Blum, Zhiyuan Li +1
Chain-of-Thought reasoning has emerged as a powerful approach for solving complex mathematical and logical problems. However, it can often veer off track through incorrect or unsub…
Reasoning with Latent Thoughts: On the Power of Looped Transformers
Nikunj Saunshi, Nishanth Dikkala, Zhiyuan Li +2
Large language models have shown remarkable reasoning abilities and scaling laws suggest that large parameter count, especially along the depth axis, is the primary driver. In this…
Why Do You Grok? A Theoretical Analysis of Grokking Modular Addition
Mohamad Amin Mohamadi, Zhiyuan Li, Lei Wu +1
We present a theoretical explanation of the ``grokking'' phenomenon, where a model generalizes long after overfitting,for the originally-studied problem of modular addition. First,…