768 citations · 1.3k across the 29 of their papers we have counts for
30 papers
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,…
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…
Aligning Large Multimodal Models with Factually Augmented RLHF
Zhiqing Sun, Sheng Shen, Shengcao Cao +9
Large Multimodal Models (LMM) are built across modalities and the misalignment between two modalities can result in "hallucination", generating textual outputs that are not grounde…
Accelerating Diffusion-based Combinatorial Optimization Solvers by Progressive Distillation
Junwei Huang, Zhiqing Sun, Yiming Yang
Graph-based diffusion models have shown promising results in terms of generating high-quality solutions to NP-complete (NPC) combinatorial optimization (CO) problems. However, thos…
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…
DIFUSCO: Graph-based Diffusion Solvers for Combinatorial Optimization
Zhiqing Sun, Yiming Yang
Neural network-based Combinatorial Optimization (CO) methods have shown promising results in solving various NP-complete (NPC) problems without relying on hand-crafted domain knowl…
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
BigScience Workshop, :, Teven Le Scao +391
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to wi…
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…
PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts
Stephen H. Bach, Victor Sanh, Zheng-Xin Yong +24
PromptSource is a system for creating, sharing, and using natural language prompts. Prompts are functions that map an example from a dataset to a natural language input and target…
Rethinking Transformer-based Set Prediction for Object Detection
Zhiqing Sun, Shengcao Cao, Yiming Yang +1
DETR is a recently proposed Transformer-based method which views object detection as a set prediction problem and achieves state-of-the-art performance but demands extra-long train…
An EM Approach to Non-autoregressive Conditional Sequence Generation
Zhiqing Sun, Yiming Yang
Autoregressive (AR) models have been the dominating approach to conditional sequence generation, but are suffering from the issue of high inference latency. Non-autoregressive (NAR…
MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices
Zhiqing Sun, Hongkun Yu, Xiaodan Song +3
Natural Language Processing (NLP) has recently achieved great success by using huge pre-trained models with hundreds of millions of parameters. However, these models suffer from he…
A Re-evaluation of Knowledge Graph Completion Methods
Zhiqing Sun, Shikhar Vashishth, Soumya Sanyal +2
Knowledge Graph Completion (KGC) aims at automatically predicting missing links for large-scale knowledge graphs. A vast number of state-of-the-art KGC techniques have got publishe…
Fast Structured Decoding for Sequence Models
Zhiqing Sun, Zhuohan Li, Haoqing Wang +3
Autoregressive sequence models achieve state-of-the-art performance in domains like machine translation. However, due to the autoregressive factorization nature, these models suffe…
Dynamically Pruned Message Passing Networks for Large-Scale Knowledge Graph Reasoning
Xiaoran Xu, Wei Feng, Yunsheng Jiang +3
We propose Dynamically Pruned Message Passing Networks (DPMPN) for large-scale knowledge graph reasoning. In contrast to existing models, embedding-based or path-based, we learn an…