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20222026
most citedPrinciple-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision

63 citations · 153 across the 15 of their papers we have counts for

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Showing 2024Show all

7 papers · 1 filter

cs.AI2024

Improve Vision Language Model Chain-of-thought Reasoning

Ruohong Zhang, Bowen Zhang, Yanghao Li +6

Chain-of-thought (CoT) reasoning in vision language models (VLMs) is crucial for improving interpretability and trustworthiness. However, current training recipes lack robust CoT r…

cs.AI2024★ 5 cited

Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models

Yangzhen Wu, Zhiqing Sun, Shanda Li +2

While the scaling laws of large language models (LLMs) training have been extensively studied, optimal inference configurations of LLMs remain underexplored. We study inference sca…

cs.AI2024★ 2 cited

Lean-STaR: Learning to Interleave Thinking and Proving

Haohan Lin, Zhiqing Sun, Sean Welleck +1

Traditional language model-based theorem proving assumes that by training on a sufficient amount of formal proof data, a model will learn to prove theorems. Our key observation is…

cs.LG2024★ 12 cited

Self-Play Preference Optimization for Language Model Alignment

Yue Wu, Zhiqing Sun, Huizhuo Yuan +3

Standard reinforcement learning from human feedback (RLHF) approaches relying on parametric models like the Bradley-Terry model fall short in capturing the intransitivity and irrat…

cs.CV2024★ 1 cited

Direct Preference Optimization of Video Large Multimodal Models from Language Model Reward

Ruohong Zhang, Liangke Gui, Zhiqing Sun +8

Preference modeling techniques, such as direct preference optimization (DPO), has shown effective in enhancing the generalization abilities of large language model (LLM). However,…

cs.LG2024★ 1 cited

Easy-to-Hard Generalization: Scalable Alignment Beyond Human Supervision

Zhiqing Sun, Longhui Yu, Yikang Shen +4

Current AI alignment methodologies rely on human-provided demonstrations or judgments, and the learned capabilities of AI systems would be upper-bounded by human capabilities as a…

cs.CL2024★ 3 cited

HaluEval-Wild: Evaluating Hallucinations of Language Models in the Wild

Zhiying Zhu, Yiming Yang, Zhiqing Sun

Hallucinations pose a significant challenge to the reliability of large language models (LLMs) in critical domains. Recent benchmarks designed to assess LLM hallucinations within c…