45 citations · 204 across the 37 of their papers we have counts for
37 papers
Recursive Multi-Agent Systems
Jiaru Zou, Rui Pan, Ruizhong Qiu +8
Recursive or looped language models have recently emerged as a new scaling axis by iteratively refining the same model computation over latent states to deepen reasoning. We extend…
GAR: Generative Adversarial Reinforcement Learning for Formal Theorem Proving
Ruida Wang, Jiarui Yao, Rui Pan +2
Solving math problems through verifiable languages such as Lean has significantly impacted both the mathematics and computer science communities. Current state-of-the-art models ar…
Generalizable Geometric Image Caption Synthesis
Yue Xin, Wenyuan Wang, Rui Pan +5
Multimodal large language models have various practical applications that demand strong reasoning abilities. Despite recent advancements, these models still struggle to solve compl…
MA-LoT: Model-Collaboration Lean-based Long Chain-of-Thought Reasoning enhances Formal Theorem Proving
Ruida Wang, Rui Pan, Yuxin Li +6
Solving mathematical problems using computer-verifiable languages like Lean has significantly impacted the mathematical and computer science communities. State-of-the-art methods u…
Adapt-Pruner: Adaptive Structural Pruning for Efficient Small Language Model Training
Rui Pan, Shivanshu Shekhar, Boyao Wang +5
Small language models (SLMs) have attracted considerable attention from both academia and industry due to their broad range of applications in edge devices. To obtain SLMs with str…
Entropy-Regularized Process Reward Model
Hanning Zhang, Pengcheng Wang, Shizhe Diao +6
Large language models (LLMs) have shown promise in performing complex multi-step reasoning, yet they continue to struggle with mathematical reasoning, often making systematic error…
Hymba: A Hybrid-head Architecture for Small Language Models
Xin Dong, Yonggan Fu, Shizhe Diao +10
We propose Hymba, a family of small language models featuring a hybrid-head parallel architecture that integrates transformer attention mechanisms with state space models (SSMs) fo…
FIRST: Teach A Reliable Large Language Model Through Efficient Trustworthy Distillation
KaShun Shum, Minrui Xu, Jianshu Zhang +5
Large language models (LLMs) have become increasingly prevalent in our daily lives, leading to an expectation for LLMs to be trustworthy -- - both accurate and well-calibrated (the…
TheoremLlama: Transforming General-Purpose LLMs into Lean4 Experts
Ruida Wang, Jipeng Zhang, Yizhen Jia +4
Proving mathematical theorems using computer-verifiable formal languages like Lean significantly impacts mathematical reasoning. One approach to formal theorem proving involves gen…
VeraCT Scan: Retrieval-Augmented Fake News Detection with Justifiable Reasoning
Cheng Niu, Yang Guan, Yuanhao Wu +7
The proliferation of fake news poses a significant threat not only by disseminating misleading information but also by undermining the very foundations of democracy. The recent adv…
SaySelf: Teaching LLMs to Express Confidence with Self-Reflective Rationales
Tianyang Xu, Shujin Wu, Shizhe Diao +4
Large language models (LLMs) often generate inaccurate or fabricated information and generally fail to indicate their confidence, which limits their broader applications. Previous…
Arithmetic Control of LLMs for Diverse User Preferences: Directional Preference Alignment with Multi-Objective Rewards
Haoxiang Wang, Yong Lin, Wei Xiong +5
Fine-grained control over large language models (LLMs) remains a significant challenge, hindering their adaptability to diverse user needs. While Reinforcement Learning from Human…
LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning
Rui Pan, Xiang Liu, Shizhe Diao +4
The machine learning community has witnessed impressive advancements since large language models (LLMs) first appeared. Yet, their massive memory consumption has become a significa…
The Instinctive Bias: Spurious Images lead to Illusion in MLLMs
Tianyang Han, Qing Lian, Rui Pan +5
Large language models (LLMs) have recently experienced remarkable progress, where the advent of multi-modal large language models (MLLMs) has endowed LLMs with visual capabilities,…
ConstraintChecker: A Plugin for Large Language Models to Reason on Commonsense Knowledge Bases
Quyet V. Do, Tianqing Fang, Shizhe Diao +2
Reasoning over Commonsense Knowledge Bases (CSKB), i.e. CSKB reasoning, has been explored as a way to acquire new commonsense knowledge based on reference knowledge in the original…
R-Tuning: Instructing Large Language Models to Say `I Don't Know'
Hanning Zhang, Shizhe Diao, Yong Lin +6
Large language models (LLMs) have revolutionized numerous domains with their impressive performance but still face their challenges. A predominant issue is the propensity for these…
Plum: Prompt Learning using Metaheuristic
Rui Pan, Shuo Xing, Shizhe Diao +6
Since the emergence of large language models, prompt learning has become a popular method for optimizing and customizing these models. Special prompts, such as Chain-of-Thought, ha…
MarineGPT: Unlocking Secrets of Ocean to the Public
Ziqiang Zheng, Jipeng Zhang, Tuan-Anh Vu +3
Large language models (LLMs), such as ChatGPT/GPT-4, have proven to be powerful tools in promoting the user experience as an AI assistant. The continuous works are proposing multi-…
UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting
Xu Liu, Junfeng Hu, Yuan Li +4
Multivariate time series forecasting plays a pivotal role in contemporary web technologies. In contrast to conventional methods that involve creating dedicated models for specific…
Mitigating the Alignment Tax of RLHF
Yong Lin, Hangyu Lin, Wei Xiong +14
LLMs acquire a wide range of abilities during pre-training, but aligning LLMs under Reinforcement Learning with Human Feedback (RLHF) can lead to forgetting pretrained abilities, w…
Mixture-of-Domain-Adapters: Decoupling and Injecting Domain Knowledge to Pre-trained Language Models Memories
Shizhe Diao, Tianyang Xu, Ruijia Xu +2
Pre-trained language models (PLMs) demonstrate excellent abilities to understand texts in the generic domain while struggling in a specific domain. Although continued pre-training…
On the Difference of BERT-style and CLIP-style Text Encoders
Zhihong Chen, Guiming Hardy Chen, Shizhe Diao +2
Masked language modeling (MLM) has been one of the most popular pretraining recipes in natural language processing, e.g., BERT, one of the representative models. Recently, contrast…
LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models
Shizhe Diao, Rui Pan, Hanze Dong +4
Foundation models have demonstrated a great ability to achieve general human-level intelligence far beyond traditional approaches. As the technique keeps attracting attention from…
DetGPT: Detect What You Need via Reasoning
Renjie Pi, Jiahui Gao, Shizhe Diao +8
In recent years, the field of computer vision has seen significant advancements thanks to the development of large language models (LLMs). These models have enabled more effective…