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Found 15 papers, 9 papers with code
Box-To-Box Transformations for Modeling Joint Hierarchies
In Patel et al., (2020), the authors demonstrate that only the transitive reduction is required and further extend box embeddings to capture joint hierarchies by augmenting the graph with new nodes.
Incorporating Geo-Diverse Knowledge into Prompting for Increased Geographical Robustness in Object Recognition
In the absence of training data from target geographies, we hypothesize that geographically diverse descriptive knowledge of categories can enhance robustness.
UNcommonsense Reasoning: Abductive Reasoning about Uncommon Situations
Language technologies that accurately model the dynamics of events must perform commonsense reasoning.
In Search of the Long-Tail: Systematic Generation of Long-Tail Inferential Knowledge via Logical Rule Guided Search
We further use the data generated by LINK to construct a dataset Logic-Induced-Long-Tail (LINT) that can be used to evaluate downstream models on the long-tail distribution; LINT contains 108K knowledge statements spanning four domains.
PlaSma: Making Small Language Models Better Procedural Knowledge Models for (Counterfactual) Planning
In addition, we introduce a novel task, Counterfactual Planning, that requires a revision of a plan to cope with a counterfactual situation.
Faith and Fate: Limits of Transformers on Compositionality
We formulate compositional tasks as computation graphs to systematically quantify the level of complexity, and break down reasoning steps into intermediate sub-procedures.
Editing Common Sense in Transformers
However, these editing methods have only been evaluated on statements about encyclopedic knowledge with a single correct answer.
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.
Ranked #1 on
Language Modelling
on StackExchange
A Systematic Investigation of Commonsense Knowledge in Large Language Models
Language models (LMs) trained on large amounts of data have shown impressive performance on many NLP tasks under the zero-shot and few-shot setup.
Word2Box: Capturing Set-Theoretic Semantics of Words using Box Embeddings
In this work, we provide a fuzzy-set interpretation of box embeddings, and learn box representations of words using a set-theoretic training objective.
Looking Beyond Sentence-Level Natural Language Inference for Question Answering and Text Summarization
Natural Language Inference (NLI) has garnered significant attention in recent years; however, the promise of applying NLI breakthroughs to other downstream NLP tasks has remained unfulfilled.
Probabilistic Box Embeddings for Uncertain Knowledge Graph Reasoning
Knowledge bases often consist of facts which are harvested from a variety of sources, many of which are noisy and some of which conflict, resulting in a level of uncertainty for each triple.
Improving Local Identifiability in Probabilistic Box Embeddings
Geometric embeddings have recently received attention for their natural ability to represent transitive asymmetric relations via containment.
Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval
We propose a simple and efficient multi-hop dense retrieval approach for answering complex open-domain questions, which achieves state-of-the-art performance on two multi-hop datasets, HotpotQA and multi-evidence FEVER.
Ranked #14 on
Question Answering
on HotpotQA
ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning
Given questions regarding some prototypical situation such as Name something that people usually do before they leave the house for work?
