Search Results for author:
Found 17 papers, 10 papers with code
Multilingual End to End Entity Linking
Entity Linking is one of the most common Natural Language Processing tasks in practical applications, but so far efficient end-to-end solutions with multilingual coverage have been lacking, leading to complex model stacks.
Language Models with Rationality
Our goal is to uncover such dependencies and reduce inconsistencies among them, so that answers are supported by faithful, system-believed chains of reasoning drawn from a consistent network of beliefs.
Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages
The NLP community has mainly focused on scaling Large Language Models (LLMs) vertically, i. e., making them better for about 100 languages.
Polar Ducks and Where to Find Them: Enhancing Entity Linking with Duck Typing and Polar Box Embeddings
Inspired by duck typing in programming languages, we propose to define the type of an entity based on the relations that it has with other entities in a knowledge graph.
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions.
Measuring Causal Effects of Data Statistics on Language Model's `Factual' Predictions
Our causal framework and our results demonstrate the importance of studying datasets and the benefits of causality for understanding NLP models.
EDIN: An End-to-end Benchmark and Pipeline for Unknown Entity Discovery and Indexing
This paper created the Unknown Entity Discovery and Indexing (EDIN) benchmark where unknown entities, that is entities without a description in the knowledge base and labeled mentions, have to be integrated into an existing entity linking system.
Language Models As or For Knowledge Bases
Pre-trained language models (LMs) have recently gained attention for their potential as an alternative to (or proxy for) explicit knowledge bases (KBs).
BeliefBank: Adding Memory to a Pre-Trained Language Model for a Systematic Notion of Belief
We show that, in a controlled experimental setting, these two mechanisms result in more consistent beliefs in the overall system, improving both the accuracy and consistency of its answers over time.
Enriching a Model's Notion of Belief using a Persistent Memory
(This is an old and now obsolete draft.
Static Embeddings as Efficient Knowledge Bases?
Recent research investigates factual knowledge stored in large pretrained language models (PLMs).
Multilingual LAMA: Investigating Knowledge in Multilingual Pretrained Language Models
(i) Can mBERT be used as a multilingual knowledge base?
Measuring and Improving Consistency in Pretrained Language Models
In this paper we study the question: Are Pretrained Language Models (PLMs) consistent with respect to factual knowledge?
Dirichlet-Smoothed Word Embeddings for Low-Resource Settings
We evaluate on standard word similarity data sets and compare to word2vec and the recent state of the art for low-resource settings: Positive and Unlabeled (PU) Learning for word embeddings.
Are Pretrained Language Models Symbolic Reasoners Over Knowledge?
How can pretrained language models (PLMs) learn factual knowledge from the training set?
BERT-kNN: Adding a kNN Search Component to Pretrained Language Models for Better QA
Khandelwal et al. (2020) use a k-nearest-neighbor (kNN) component to improve language model performance.
Negated and Misprimed Probes for Pretrained Language Models: Birds Can Talk, But Cannot Fly
We find that PLMs do not distinguish between negated ("Birds cannot [MASK]") and non-negated ("Birds can [MASK]") cloze questions.
