73 citations · 122 across the 15 of their papers we have counts for
18 papers
Stepped MoE: Segment-Level Routing with Configurable Inference Complexity
Arnav Kundu, Zhaoyang Xu, Bairu Hou +3
Training large language models (LLMs) is resource-intensive, and adapting them for diverse deployment scenarios with varying computational constraints remains challenging. While el…
CompliBench: Benchmarking LLM Judges for Compliance Violation Detection in Dialogue Systems
Jingbo Yang, Guanyu Yao, Bairu Hou +5
As Large Language Models (LLMs) are increasingly deployed as task-oriented agents in enterprise environments, ensuring their strict adherence to complex, domain-specific operationa…
Ares: Adaptive Reasoning Effort Selection for Efficient LLM Agents
Jingbo Yang, Bairu Hou, Wei Wei +2
Modern agents powered by thinking LLMs achieve high accuracy through long chain-of-thought reasoning but incur substantial inference costs. While many LLMs now support configurable…
WebDART: Dynamic Decomposition and Re-planning for Complex Web Tasks
Jingbo Yang, Bairu Hou, Wei Wei +2
Large language model (LLM) agents are becoming competent at straightforward web tasks, such as opening an item page or submitting a form, but still struggle with objectives that re…
ThinkPrune: Pruning Long Chain-of-Thought of LLMs via Reinforcement Learning
Bairu Hou, Yang Zhang, Jiabao Ji +4
We present ThinkPrune, a simple yet effective method for pruning the thinking length for long-thinking LLMs, which has been found to often produce inefficient and redundant thinkin…
KVLink: Accelerating Large Language Models via Efficient KV Cache Reuse
Jingbo Yang, Bairu Hou, Wei Wei +2
We describe KVLink, an approach for efficient key-value (KV) cache reuse in large language models (LLMs). In many LLM applications, different inputs can share overlapping context,…
Instruction-Following Pruning for Large Language Models
Bairu Hou, Qibin Chen, Jianyu Wang +6
With the rapid scaling of large language models (LLMs), structured pruning has become a widely used technique to learn efficient, smaller models from larger ones, delivering superi…
A Probabilistic Framework for LLM Hallucination Detection via Belief Tree Propagation
Bairu Hou, Yang Zhang, Jacob Andreas +1
This paper focuses on the task of hallucination detection, which aims to determine the truthfulness of LLM-generated statements. To address this problem, a popular class of methods…
Advancing the Robustness of Large Language Models through Self-Denoised Smoothing
Jiabao Ji, Bairu Hou, Zhen Zhang +7
Although large language models (LLMs) have achieved significant success, their vulnerability to adversarial perturbations, including recent jailbreak attacks, has raised considerab…
Defending Large Language Models against Jailbreak Attacks via Semantic Smoothing
Jiabao Ji, Bairu Hou, Alexander Robey +5
Aligned large language models (LLMs) are vulnerable to jailbreaking attacks, which bypass the safeguards of targeted LLMs and fool them into generating objectionable content. While…
A Survey on Data Selection for Language Models
Alon Albalak, Yanai Elazar, Sang Michael Xie +11
A major factor in the recent success of large language models is the use of enormous and ever-growing text datasets for unsupervised pre-training. However, naively training a model…
Decomposing Uncertainty for Large Language Models through Input Clarification Ensembling
Bairu Hou, Yujian Liu, Kaizhi Qian +3
Uncertainty decomposition refers to the task of decomposing the total uncertainty of a predictive model into aleatoric (data) uncertainty, resulting from inherent randomness in the…
Certified Robustness for Large Language Models with Self-Denoising
Zhen Zhang, Guanhua Zhang, Bairu Hou +5
Although large language models (LLMs) have achieved great success in vast real-world applications, their vulnerabilities towards noisy inputs have significantly limited their uses,…
Improving Diffusion Models for Scene Text Editing with Dual Encoders
Jiabao Ji, Guanhua Zhang, Zhaowen Wang +4
Scene text editing is a challenging task that involves modifying or inserting specified texts in an image while maintaining its natural and realistic appearance. Most previous appr…
TextGrad: Advancing Robustness Evaluation in NLP by Gradient-Driven Optimization
Bairu Hou, Jinghan Jia, Yihua Zhang +4
Robustness evaluation against adversarial examples has become increasingly important to unveil the trustworthiness of the prevailing deep models in natural language processing (NLP…
PromptBoosting: Black-Box Text Classification with Ten Forward Passes
Bairu Hou, Joe O'Connor, Jacob Andreas +2
We describe PromptBoosting, a query-efficient procedure for building a text classifier from a neural language model (LM) without access to the LM's parameters, gradients, or hidden…
Learning to Attack: Towards Textual Adversarial Attacking in Real-world Situations
Yuan Zang, Bairu Hou, Fanchao Qi +3
Adversarial attacking aims to fool deep neural networks with adversarial examples. In the field of natural language processing, various textual adversarial attack models have been…
OpenAttack: An Open-source Textual Adversarial Attack Toolkit
Guoyang Zeng, Fanchao Qi, Qianrui Zhou +6
Textual adversarial attacking has received wide and increasing attention in recent years. Various attack models have been proposed, which are enormously distinct and implemented wi…