4 citations · 7 across the 12 of their papers we have counts for
12 papers
Test-time Recursive Thinking: Self-Improvement without External Feedback
Yufan Zhuang, Chandan Singh, Liyuan Liu +5
Modern Large Language Models (LLMs) have shown rapid improvements in reasoning capabilities, driven largely by reinforcement learning (RL) with verifiable rewards. Here, we ask whe…
Contamination Detection for VLMs using Multi-Modal Semantic Perturbation
Jaden Park, Mu Cai, Feng Yao +3
Recent advances in Vision-Language Models (VLMs) have achieved state-of-the-art performance on numerous benchmark tasks. However, the use of internet-scale, often proprietary, pret…
Finish First, Perfect Later: Test-Time Token-Level Cross-Validation for Diffusion Large Language Models
Runchu Tian, Junxia Cui, Xueqiang Xu +2
Diffusion large language models (dLLMs) have recently emerged as a promising alternative to autoregressive (AR) models, offering advantages such as accelerated parallel decoding an…
Text Generation Beyond Discrete Token Sampling
Yufan Zhuang, Liyuan Liu, Chandan Singh +2
In standard autoregressive generation, an LLM predicts the next-token distribution, samples a discrete token, and then discards the distribution, passing only the sampled token as…
Self-Taught Agentic Long Context Understanding
Yufan Zhuang, Xiaodong Yu, Jialian Wu +7
Answering complex, long-context questions remains a major challenge for large language models (LLMs) as it requires effective question clarifications and context retrieval. We prop…
Next-Token Prediction Task Assumes Optimal Data Ordering for LLM Training in Proof Generation
Chenyang An, Shima Imani, Feng Yao +8
In the field of large language model (LLM)-based proof generation, despite extensive training on large datasets such as ArXiv, LLMs still exhibit only modest performance on proving…
Vector-ICL: In-context Learning with Continuous Vector Representations
Yufan Zhuang, Chandan Singh, Liyuan Liu +2
Large language models (LLMs) have shown remarkable in-context learning (ICL) capabilities on textual data. We explore whether these capabilities can be extended to continuous vecto…
Configurable Foundation Models: Building LLMs from a Modular Perspective
Chaojun Xiao, Zhengyan Zhang, Chenyang Song +20
Advancements in LLMs have recently unveiled challenges tied to computational efficiency and continual scalability due to their requirements of huge parameters, making the applicati…
Evaluating the Smooth Control of Attribute Intensity in Text Generation with LLMs
Shang Zhou, Feng Yao, Chengyu Dong +2
Controlling the attribute intensity of text generation is crucial across scenarios (e.g., writing conciseness, chatting emotion, and explanation clarity). The remarkable capabiliti…
Data Contamination Can Cross Language Barriers
Feng Yao, Yufan Zhuang, Zihao Sun +3
The opacity in developing large language models (LLMs) is raising growing concerns about the potential contamination of public benchmarks in the pre-training data. Existing contami…
Beyond Scaling: Predicting Patent Approval with Domain-specific Fine-grained Claim Dependency Graph
Xiaochen Kev Gao, Feng Yao, Kewen Zhao +4
Model scaling is becoming the default choice for many language tasks due to the success of large language models (LLMs). However, it can fall short in specific scenarios where simp…
Learning a Decision Tree Algorithm with Transformers
Yufan Zhuang, Liyuan Liu, Chandan Singh +2
Decision trees are renowned for their ability to achieve high predictive performance while remaining interpretable, especially on tabular data. Traditionally, they are constructed…