Search Results for author:
Found 17 papers, 12 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.
Persuasiveness of Generated Free-Text Rationales in Subjective Decisions: A Case Study on Pairwise Argument Ranking
Recently, there has been growing interest in using these rationales to provide insights for various important downstream tasks.
Every Answer Matters: Evaluating Commonsense with Probabilistic Measures
We also propose a method of probabilistic evaluation that strongly correlates with human judgments.
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
To instead investigate the ability to model unusual, unexpected, and unlikely situations, we explore the task of uncommonsense abductive reasoning.
In Search of the Long-Tail: Systematic Generation of Long-Tail Inferential Knowledge via Logical Rule Guided Search
To effectively use large language models (LLMs) for real-world queries, it is imperative that they generalize to the long-tail distribution, i. e. rare examples where models exhibit low confidence.
PlaSma: Making Small Language Models Better Procedural Knowledge Models for (Counterfactual) Planning
We present PlaSma, a novel two-pronged approach to endow small language models with procedural knowledge and (constrained) language planning capabilities.
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?
