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Found 18 papers, 7 papers with code
Evaluating Human-Language Model Interaction
To evaluate human-LM interaction, we develop a new framework, Human-AI Language-based Interaction Evaluation (HALIE), that defines the components of interactive systems and dimensions to consider when designing evaluation metrics.
On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning
Generating a Chain of Thought (CoT) has been shown to consistently improve large language model (LLM) performance on a wide range of NLP tasks.
Explanations Can Reduce Overreliance on AI Systems During Decision-Making
Prior work has identified a resilient phenomenon that threatens the performance of human-AI decision-making teams: overreliance, when people agree with an AI, even when it is incorrect.
ELIGN: Expectation Alignment as a Multi-Agent Intrinsic Reward
These results identify tasks where expectation alignment is a more useful strategy than curiosity-driven exploration for multi-agent coordination, enabling agents to do zero-shot coordination.
Visual Intelligence through Human Interaction
Over the last decade, Computer Vision, the branch of Artificial Intelligence aimed at understanding the visual world, has evolved from simply recognizing objects in images to describing pictures, answering questions about images, aiding robots maneuver around physical spaces and even generating novel visual content.
Conceptual Metaphors Impact Perceptions of Human-AI Collaboration
In a third study, we assess effects of metaphor choices on potential users' desire to try out the system and find that users are drawn to systems that project higher competence and warmth.
Deep Bayesian Active Learning for Multiple Correct Outputs
The assumption that these tasks always have exactly one correct answer has resulted in the creation of numerous uncertainty-based measurements, such as entropy and least confidence, which operate over a model's outputs.
Learning Predicates as Functions to Enable Few-shot Scene Graph Prediction
We introduce the first scene graph prediction model that supports few-shot learning of predicates.
Scene Graph Prediction with Limited Labels
All scene graph models to date are limited to training on a small set of visual relationships that have thousands of training labels each.
Ranked #1 on
Scene Graph Detection
on VRD
Information Maximizing Visual Question Generation
We build a model that maximizes mutual information between the image, the expected answer and the generated question.
Referring Relationships
We formulate the cyclic condition between the entities in a relationship by modelling predicates that connect the entities as shifts in attention from one entity to another.
Iris: A Conversational Agent for Complex Tasks
Today's conversational agents are restricted to simple standalone commands.
Visual Relationship Detection with Language Priors
We improve on prior work by leveraging language priors from semantic word embeddings to finetune the likelihood of a predicted relationship.
Ranked #2 on
Scene Graph Generation
on VRD
Empath: Understanding Topic Signals in Large-Scale Text
Given a small set of seed words that characterize a category, Empath uses its neural embedding to discover new related terms, then validates the category with a crowd-powered filter.
Augur: Mining Human Behaviors from Fiction to Power Interactive Systems
From smart homes that prepare coffee when we wake, to phones that know not to interrupt us during important conversations, our collective visions of HCI imagine a future in which computers understand a broad range of human behaviors.
Visual7W: Grounded Question Answering in Images
It enables a new type of QA with visual answers, in addition to textual answers used in previous work.
Multiple-choice
Multiple Choice Question Answering (MCQA)
+2
Image Retrieval Using Scene Graphs
We introduce a novel dataset of 5, 000 human-generated scene graphs grounded to images and use this dataset to evaluate our method for image retrieval.
ImageNet Large Scale Visual Recognition Challenge
The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images.
