60 citations · 151 across the 14 of their papers we have counts for
14 papers
Gentopia: A Collaborative Platform for Tool-Augmented LLMs
Binfeng Xu, Xukun Liu, Hua Shen +7
Augmented Language Models (ALMs) empower large language models with the ability to use tools, transforming them into intelligent agents for real-world interactions. However, most e…
MultiTurnCleanup: A Benchmark for Multi-Turn Spoken Conversational Transcript Cleanup
Hua Shen, Vicky Zayats, Johann C. Rocholl +2
Current disfluency detection models focus on individual utterances each from a single speaker. However, numerous discontinuity phenomena in spoken conversational transcripts occur…
ConvXAI: Delivering Heterogeneous AI Explanations via Conversations to Support Human-AI Scientific Writing
Hua Shen, Chieh-Yang Huang, Tongshuang Wu +1
Despite a surge collection of XAI methods, users still struggle to obtain required AI explanations. Previous research suggests chatbots as dynamic solutions, but the effective desi…
Does Human Collaboration Enhance the Accuracy of Identifying LLM-Generated Deepfake Texts?
Adaku Uchendu, Jooyoung Lee, Hua Shen +3
Advances in Large Language Models (e.g., GPT-4, LLaMA) have improved the generation of coherent sentences resembling human writing on a large scale, resulting in the creation of so…
Parachute: Evaluating Interactive Human-LM Co-writing Systems
Hua Shen, Tongshuang Wu
A surge of advances in language models (LMs) has led to significant interest in using LMs to build co-writing systems, in which humans and LMs interactively contribute to a shared…
SpeechPrompt v2: Prompt Tuning for Speech Classification Tasks
Kai-Wei Chang, Yu-Kai Wang, Hua Shen +4
Prompt tuning is a technology that tunes a small set of parameters to steer a pre-trained language model (LM) to directly generate the output for downstream tasks. Recently, prompt…
ScatterShot: Interactive In-context Example Curation for Text Transformation
Tongshuang Wu, Hua Shen, Daniel S. Weld +2
The in-context learning capabilities of LLMs like GPT-3 allow annotators to customize an LLM to their specific tasks with a small number of examples. However, users tend to include…
Too Slow to Be Useful? On Incorporating Humans in the Loop of Smart Speakers
Shih-Hong Huang, Chieh-Yang Huang, Yuxin Deng +3
Real-time crowd-powered systems, such as Chorus/Evorus, VizWiz, and Apparition, have shown how incorporating humans into automated systems could supplement where the automatic solu…
Are Shortest Rationales the Best Explanations for Human Understanding?
Hua Shen, Tongshuang Wu, Wenbo Guo +1
Existing self-explaining models typically favor extracting the shortest possible rationales - snippets of an input text "responsible for" corresponding output - to explain the mode…
Improving fairness in speaker verification via Group-adapted Fusion Network
Hua Shen, Yuguang Yang, Guoli Sun +4
Modern speaker verification models use deep neural networks to encode utterance audio into discriminative embedding vectors. During the training process, these networks are typical…
Explaining the Road Not Taken
Hua Shen, Ting-Hao 'Kenneth' Huang
It is unclear if existing interpretations of deep neural network models respond effectively to the needs of users. This paper summarizes the common forms of explanations (such as f…
How Useful Are the Machine-Generated Interpretations to General Users? A Human Evaluation on Guessing the Incorrectly Predicted Labels
Hua Shen, Ting-Hao Kenneth Huang
Explaining to users why automated systems make certain mistakes is important and challenging. Researchers have proposed ways to automatically produce interpretations for deep neura…
A Tale of Evil Twins: Adversarial Inputs versus Poisoned Models
Ren Pang, Hua Shen, Xinyang Zhang +5
Despite their tremendous success in a range of domains, deep learning systems are inherently susceptible to two types of manipulations: adversarial inputs -- maliciously crafted sa…
Interpretable Deep Learning under Fire
Xinyang Zhang, Ningfei Wang, Hua Shen +3
Providing explanations for deep neural network (DNN) models is crucial for their use in security-sensitive domains. A plethora of interpretation models have been proposed to help u…