63 citations · 153 across the 15 of their papers we have counts for
15 papers
What Is Missing: Interpretable Ratings for Large Language Model Outputs
Nicholas Stranges, Yimin Yang
Current Large Language Model (LLM) preference learning methods such as Proximal Policy Optimization and Direct Preference Optimization learn from direct rankings or numerical ratin…
SPL-LNS: Sampling-Enhanced Large Neighborhood Search for Solving Integer Linear Programs
Shengyu Feng, Zhiqing Sun, Yiming Yang
Large Neighborhood Search (LNS) is a common heuristic in combinatorial optimization that iteratively searches over a large neighborhood of the current solution for a better one. Re…
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…
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…
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…
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…
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,…
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…
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…
SALMON: Self-Alignment with Instructable Reward Models
Zhiqing Sun, Yikang Shen, Hongxin Zhang +5
Supervised Fine-Tuning (SFT) on response demonstrations combined with Reinforcement Learning from Human Feedback (RLHF) constitutes a powerful paradigm for aligning LLM-based AI ag…
Active Retrieval Augmented Generation
Zhengbao Jiang, Frank F. Xu, Luyu Gao +6
Despite the remarkable ability of large language models (LMs) to comprehend and generate language, they have a tendency to hallucinate and create factually inaccurate output. Augme…
Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision
Zhiqing Sun, Yikang Shen, Qinhong Zhou +5
Recent AI-assistant agents, such as ChatGPT, predominantly rely on supervised fine-tuning (SFT) with human annotations and reinforcement learning from human feedback (RLHF) to alig…
A Neural PDE Solver with Temporal Stencil Modeling
Zhiqing Sun, Yiming Yang, Shinjae Yoo
Numerical simulation of non-linear partial differential equations plays a crucial role in modeling physical science and engineering phenomena, such as weather, climate, and aerodyn…
DIMES: A Differentiable Meta Solver for Combinatorial Optimization Problems
Ruizhong Qiu, Zhiqing Sun, Yiming Yang
Recently, deep reinforcement learning (DRL) models have shown promising results in solving NP-hard Combinatorial Optimization (CO) problems. However, most DRL solvers can only scal…
Recitation-Augmented Language Models
Zhiqing Sun, Xuezhi Wang, Yi Tay +2
We propose a new paradigm to help Large Language Models (LLMs) generate more accurate factual knowledge without retrieving from an external corpus, called RECITation-augmented gEne…