17 citations · 42 across the 15 of their papers we have counts for
19 papers
One-Step Text Generation by Seq-Drifting
Jiayu Liu, Ruizhi Zhao, Tianyun Ji +3
Diffusion language models generate tokens in parallel, but still require multiple sampling steps to produce high-quality text. In this paper, we investigate whether a model can lea…
Label-free cell counting and viability prediction with brightfield imaging and deep learning
Amir Reza Vazifeh, Christian Zeigler, Sornanathan Meyyappan +2
Cell viability assessment is a core requirement in cell culture systems, with critical applications in biopharmaceutical manufacturing and drug development. Conventionally, it is m…
OpenSkillRisk: Benchmarking Agent Safety When Using Real-World Risky Third-Party Skills
Qiyuan Liu, Tingfeng Hui, Kun Zhan +2
LLM-based agents leverage third-party skills to extend their capabilities in open-world scenarios. However, third-party skills can introduce extra security vulnerabilities, as seem…
TD-Grokking: Learning from Zero-Reward Problems by Training-Time Decomposition
Ningyuan Xi, Hao Xu, Hongsheng Xin +1
Large language models (LLMs) have made remarkable progress in reasoning tasks, largely driven by post-training paradigms, especially reinforcement learning with verifiable rewards…
Beyond Ideal Instruction: A Comprehensive Framework for Evaluating LLMs in Realistic Interactions
Xuan Yang, Hao Xu, Tingfeng Hui +4
Despite great advances in tool-use capabilities of large language models (LLMs), existing evaluation benchmarks struggle to fully align with real-world scenarios. Such benchmarks m…
STT-Arena: A More Realistic Environment for Tool-Using with Spatio-Temporal Dynamics
Tingfeng Hui, Hao Xu, Pengyu Zhu +5
Large language models (LLMs) deployed in real-world agentic applications must be capable of replanning and adapting when mid-task disruptions invalidate their prior decisions. Exis…
Verifier-Backed Hard Problem Generation for Mathematical Reasoning
Yuhang Lai, Jiazhan Feng, Yee Whye Teh +1
Large Language Models (LLMs) demonstrate strong capabilities for solving scientific and mathematical problems, yet they struggle to produce valid, challenging, and novel problems -…
Step-Level Sparse Autoencoder for Reasoning Process Interpretation
Xuan Yang, Jiayu Liu, Yuhang Lai +3
Large Language Models (LLMs) have achieved strong complex reasoning capabilities through Chain-of-Thought (CoT) reasoning. However, their reasoning patterns remain too complicated…
Linear Dynamics in the RLVR Training of Large Language Models
Tianle Wang, Jiayu Liu, Zhongyuan Wu +4
Reinforcement learning with verifiable rewards (RLVR) has driven significant performance gains in reasoning-oriented large language models (LLMs), yet its internal training dynamic…
Enhancing Large Language Model Reasoning with Reward Models: An Analytical Survey
Qiyuan Liu, Hao Xu, Xuhong Chen +3
Reward models (RMs) play a critical role in enhancing the reasoning performance of LLMs. For example, they can provide training signals to finetune LLMs during reinforcement learni…
SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning
Ning Miao, Yee Whye Teh, Tom Rainforth
The recent progress in large language models (LLMs), especially the invention of chain-of-thought prompting, has made it possible to automatically answer questions by stepwise reas…
Learning Instance-Specific Augmentations by Capturing Local Invariances
Ning Miao, Tom Rainforth, Emile Mathieu +4
We introduce InstaAug, a method for automatically learning input-specific augmentations from data. Previous methods for learning augmentations have typically assumed independence b…
On Incorporating Inductive Biases into VAEs
Ning Miao, Emile Mathieu, N. Siddharth +2
We explain why directly changing the prior can be a surprisingly ineffective mechanism for incorporating inductive biases into VAEs, and introduce a simple and effective alternativ…
Generating Fluent Adversarial Examples for Natural Languages
Huangzhao Zhang, Hao Zhou, Ning Miao +1
Efficiently building an adversarial attacker for natural language processing (NLP) tasks is a real challenge. Firstly, as the sentence space is discrete, it is difficult to make sm…
Do You Have the Right Scissors? Tailoring Pre-trained Language Models via Monte-Carlo Methods
Ning Miao, Yuxuan Song, Hao Zhou +1
It has been a common approach to pre-train a language model on a large corpus and fine-tune it on task-specific data. In practice, we observe that fine-tuning a pre-trained model o…
Improving Maximum Likelihood Training for Text Generation with Density Ratio Estimation
Yuxuan Song, Ning Miao, Hao Zhou +3
Auto-regressive sequence generative models trained by Maximum Likelihood Estimation suffer the exposure bias problem in practical finite sample scenarios. The crux is that the numb…
Kernelized Bayesian Softmax for Text Generation
Ning Miao, Hao Zhou, Chengqi Zhao +2
Neural models for text generation require a softmax layer with proper token embeddings during the decoding phase. Most existing approaches adopt single point embedding for each tok…
Dispersed Exponential Family Mixture VAEs for Interpretable Text Generation
Wenxian Shi, Hao Zhou, Ning Miao +1
Deep generative models are commonly used for generating images and text. Interpretability of these models is one important pursuit, other than the generation quality. Variational a…
CGMH: Constrained Sentence Generation by Metropolis-Hastings Sampling
Ning Miao, Hao Zhou, Lili Mou +2
In real-world applications of natural language generation, there are often constraints on the target sentences in addition to fluency and naturalness requirements. Existing languag…