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Found 17 papers, 9 papers with code
Can Machines Learn Morality? The Delphi Experiment
As AI systems become increasingly powerful and pervasive, there are growing concerns about machines' morality or a lack thereof.
Edited Media Understanding Frames: Reasoning About the Intent and Implications of Visual Misinformation
Understanding manipulated media, from automatically generated {`}deepfakes{'} to manually edited ones, raises novel research challenges.
Is GPT-3 Text Indistinguishable from Human Text? Scarecrow: A Framework for Scrutinizing Machine Text
To support the broad range of real machine errors that can be identified by laypeople, the ten error categories of Scarecrow -- such as redundancy, commonsense errors, and incoherence -- are identified through several rounds of crowd annotation experiments without a predefined ontology.
CLIPScore: A Reference-free Evaluation Metric for Image Captioning
Image captioning has conventionally relied on reference-based automatic evaluations, where machine captions are compared against captions written by humans.
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Hallucination Pair-wise Detection (4-ref)
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Hallucination Pair-wise Detection (1-ref)
Hallucination Pair-wise Detection (4-ref)
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MultiTalk: A Highly-Branching Dialog Testbed for Diverse Conversations
We study conversational dialog in which there are many possible responses to a given history.
Moral Stories: Situated Reasoning about Norms, Intents, Actions, and their Consequences
In social settings, much of human behavior is governed by unspoken rules of conduct.
Edited Media Understanding: Reasoning About Implications of Manipulated Images
The difference between this example, and harmful edits that spread disinformation, is one of intent.
Social Chemistry 101: Learning to Reason about Social and Moral Norms
We present Social Chemistry, a new conceptual formalism to study people's everyday social norms and moral judgments over a rich spectrum of real life situations described in natural language.
Thinking Like a Skeptic: Defeasible Inference in Natural Language
Defeasible inference is a mode of reasoning in which an inference (X is a bird, therefore X flies) may be weakened or overturned in light of new evidence (X is a penguin).
Paragraph-level Commonsense Transformers with Recurrent Memory
Human understanding of narrative texts requires making commonsense inferences beyond what is stated explicitly in the text.
Neural Naturalist: Generating Fine-Grained Image Comparisons
We introduce the new Birds-to-Words dataset of 41k sentences describing fine-grained differences between photographs of birds.
Do Neural Language Representations Learn Physical Commonsense?
Humans understand language based on the rich background knowledge about how the physical world works, which in turn allows us to reason about the physical world through language.
The Curious Case of Neural Text Degeneration
Despite considerable advancements with deep neural language models, the enigma of neural text degeneration persists when these models are tested as text generators.
Balancing Shared Autonomy with Human-Robot Communication
Robotic agents that share autonomy with a human should leverage human domain knowledge and account for their preferences when completing a task.
Learning to Write with Cooperative Discriminators
Recurrent Neural Networks (RNNs) are powerful autoregressive sequence models, but when used to generate natural language their output tends to be overly generic, repetitive, and self-contradictory.
Learning to Write by Learning the Objective
Human evaluation demonstrates that text generated by the resulting generator is preferred over that of baselines by a large margin and significantly enhances the overall coherence, style, and information content of the generated text.
Verb Physics: Relative Physical Knowledge of Actions and Objects
Learning commonsense knowledge from natural language text is nontrivial due to reporting bias: people rarely state the obvious, e. g., "My house is bigger than me."
