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Found 14 papers, 9 papers with code
A Design Space for Intelligent and Interactive Writing Assistants
In our era of rapid technological advancement, the research landscape for writing assistants has become increasingly fragmented across various research communities.
Gentopia: A Collaborative Platform for Tool-Augmented LLMs
We present gentopia, an ALM framework enabling flexible customization of agents through simple configurations, seamlessly integrating various language models, task formats, prompting modules, and plugins into a unified paradigm.
MultiTurnCleanup: A Benchmark for Multi-Turn Spoken Conversational Transcript Cleanup
Current disfluency detection models focus on individual utterances each from a single speaker.
ConvXAI: Delivering Heterogeneous AI Explanations via Conversations to Support Human-AI Scientific Writing
The paper further discusses the practical human usage patterns in interacting with ConvXAI for scientific co-writing.
Does Human Collaboration Enhance the Accuracy of Identifying LLM-Generated Deepfake Texts?
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-called deepfake texts.
Parachute: Evaluating Interactive Human-LM Co-writing Systems
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 writing artifact.
SpeechPrompt v2: Prompt Tuning for Speech Classification Tasks
For speech processing, SpeechPrompt shows its high parameter efficiency and competitive performance on a few speech classification tasks.
Ranked #17 on
Spoken Language Understanding
on Fluent Speech Commands
(using extra training data)
ScatterShot: Interactive In-context Example Curation for Text Transformation
ScatterShot iteratively slices unlabeled data into task-specific patterns, samples informative inputs from underexplored or not-yet-saturated slices in an active learning manner, and helps users label more efficiently with the help of an LLM and the current example set.
Are Shortest Rationales the Best Explanations for Human Understanding?
Existing self-explaining models typically favor extracting the shortest possible rationales - snippets of an input text "responsible for" corresponding output - to explain the model prediction, with the assumption that shorter rationales are more intuitive to humans.
Improving fairness in speaker verification via Group-adapted Fusion Network
This is observed especially with underrepresented demographic groups sharing similar voice characteristics.
Explaining the Road Not Taken
It is unclear if existing interpretations of deep neural network models respond effectively to the needs of users.
How Useful Are the Machine-Generated Interpretations to General Users? A Human Evaluation on Guessing the Incorrectly Predicted Labels
Explaining to users why automated systems make certain mistakes is important and challenging.
A Tale of Evil Twins: Adversarial Inputs versus Poisoned Models
Specifically, (i) we develop a new attack model that jointly optimizes adversarial inputs and poisoned models; (ii) with both analytical and empirical evidence, we reveal that there exist intriguing "mutual reinforcement" effects between the two attack vectors -- leveraging one vector significantly amplifies the effectiveness of the other; (iii) we demonstrate that such effects enable a large design spectrum for the adversary to enhance the existing attacks that exploit both vectors (e. g., backdoor attacks), such as maximizing the attack evasiveness with respect to various detection methods; (iv) finally, we discuss potential countermeasures against such optimized attacks and their technical challenges, pointing to several promising research directions.
Interpretable Deep Learning under Fire
The improved interpretability is believed to offer a sense of security by involving human in the decision-making process.
