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Found 23 papers, 8 papers with code
Human-Centered Planning
The vision of creating AI-powered personal assistants also involves creating structured outputs, such as a plan for one's day, or for an overseas trip.
Factuality Challenges in the Era of Large Language Models
The emergence of tools based on Large Language Models (LLMs), such as OpenAI's ChatGPT, Microsoft's Bing Chat, and Google's Bard, has garnered immense public attention.
VerifAI: Verified Generative AI
We propose that verifying the outputs of generative AI from a data management perspective is an emerging issue for generative AI.
Reimagining Retrieval Augmented Language Models for Answering Queries
We present a reality check on large language models and inspect the promise of retrieval augmented language models in comparison.
NormBank: A Knowledge Bank of Situational Social Norms
We present NormBank, a knowledge bank of 155k situational norms.
Multimodal Neural Databases
The rise in loosely-structured data available through text, images, and other modalities has called for new ways of querying them.
Learnings from Data Integration for Augmented Language Models
One of the limitations of large language models is that they do not have access to up-to-date, proprietary or personal data.
Building Human Values into Recommender Systems: An Interdisciplinary Synthesis
We collect a set of values that seem most relevant to recommender systems operating across different domains, then examine them from the perspectives of current industry practice, measurement, product design, and policy approaches.
Detecting and Understanding Harmful Memes: A Survey
One interesting finding is that many types of harmful memes are not really studied, e. g., such featuring self-harm and extremism, partly due to the lack of suitable datasets.
The Moral Integrity Corpus: A Benchmark for Ethical Dialogue Systems
In this work, we introduce a new resource, not to authoritatively resolve moral ambiguities, but instead to facilitate systematic understanding of the intuitions, values and moral judgments reflected in the utterances of dialogue systems.
Quantifying Adaptability in Pre-trained Language Models with 500 Tasks
When a neural language model (LM) is adapted to perform a new task, what aspects of the task predict the eventual performance of the model?
Detecting Inspiring Content on Social Media
We release a dataset of 5, 800 inspiring and 5, 800 non-inspiring English-language public post unique ids collected from a dump of Reddit public posts made available by a third party and use linguistic heuristics to automatically detect which social media English-language posts are inspiring.
Database Reasoning Over Text
Neural models have shown impressive performance gains in answering queries from natural language text.
Neural Databases
We describe NeuralDB, a database system with no pre-defined schema, in which updates and queries are given in natural language.
Preserving Integrity in Online Social Networks
Online social networks provide a platform for sharing information and free expression.
Happiness Entailment: Automating Suggestions for Well-Being
We prototype one necessary component of such a system, the Happiness Entailment Recognition (HER) module, which takes as input a short text describing an event, a candidate suggestion, and outputs a determination about whether the suggestion is more likely to be good for this user based on the event described.
Voyageur: An Experiential Travel Search Engine
We describe Voyageur, which is an application of experiential search to the domain of travel.
Open Information Extraction from Question-Answer Pairs
We describe NeurON, a system for extracting tuples from question-answer pairs.
Subjective Databases
In order to support experiential queries, a database system needs to model subjective data and also be able to process queries where the user can express varied subjective experiences in words chosen by the user, in addition to specifying predicates involving objective attributes.
Databases
FrameIt: Ontology Discovery for Noisy User-Generated Text
A common need of NLP applications is to extract structured data from text corpora in order to perform analytics or trigger an appropriate action.
Scalable Semantic Querying of Text
KOKO is novel in that its extraction language simultaneously supports conditions on the surface of the text and on the structure of the dependency parse tree of sentences, thereby allowing for more refined extractions.
HappyDB: A Corpus of 100,000 Crowdsourced Happy Moments
The science of happiness is an area of positive psychology concerned with understanding what behaviors make people happy in a sustainable fashion.
