27 citations · 137 across the 17 of their papers we have counts for
21 papers
Replacing Judges with Juries: Evaluating LLM Generations with a Panel of Diverse Models
Pat Verga, Sebastian Hofstatter, Sophia Althammer +6
As Large Language Models (LLMs) have become more advanced, they have outpaced our abilities to accurately evaluate their quality. Not only is finding data to adequately probe parti…
To Adapt or to Annotate: Challenges and Interventions for Domain Adaptation in Open-Domain Question Answering
Dheeru Dua, Emma Strubell, Sameer Singh +1
Recent advances in open-domain question answering (ODQA) have demonstrated impressive accuracy on standard Wikipedia style benchmarks. However, it is less clear how robust these mo…
Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models
Bernd Bohnet, Vinh Q. Tran, Pat Verga +19
Large language models (LLMs) have shown impressive results while requiring little or no direct supervision. Further, there is mounting evidence that LLMs may have potential in info…
MuRAG: Multimodal Retrieval-Augmented Generator for Open Question Answering over Images and Text
Wenhu Chen, Hexiang Hu, Xi Chen +2
While language Models store a massive amount of world knowledge implicitly in their parameters, even very large models often fail to encode information about rare entities and even…
QA Is the New KR: Question-Answer Pairs as Knowledge Bases
Wenhu Chen, William W. Cohen, Michiel De Jong +4
In this position paper, we propose a new approach to generating a type of knowledge base (KB) from text, based on question generation and entity linking. We argue that the proposed…
Faithful to the Document or to the World? Mitigating Hallucinations via Entity-linked Knowledge in Abstractive Summarization
Yue Dong, John Wieting, Pat Verga
Despite recent advances in abstractive summarization, current summarization systems still suffer from content hallucinations where models generate text that is either irrelevant or…
Augmenting Pre-trained Language Models with QA-Memory for Open-Domain Question Answering
Wenhu Chen, Pat Verga, Michiel de Jong +2
Retrieval augmented language models have recently become the standard for knowledge intensive tasks. Rather than relying purely on latent semantics within the parameters of large n…
Multilingual Fact Linking
Keshav Kolluru, Martin Rezk, Pat Verga +2
Knowledge-intensive NLP tasks can benefit from linking natural language text with facts from a Knowledge Graph (KG). Although facts themselves are language-agnostic, the fact label…
Reasoning Over Virtual Knowledge Bases With Open Predicate Relations
Haitian Sun, Pat Verga, Bhuwan Dhingra +2
We present the Open Predicate Query Language (OPQL); a method for constructing a virtual KB (VKB) trained entirely from text. Large Knowledge Bases (KBs) are indispensable for a wi…
Facts as Experts: Adaptable and Interpretable Neural Memory over Symbolic Knowledge
Pat Verga, Haitian Sun, Livio Baldini Soares +1
Massive language models are the core of modern NLP modeling and have been shown to encode impressive amounts of commonsense and factual information. However, that knowledge exists…
Simultaneously Linking Entities and Extracting Relations from Biomedical Text Without Mention-level Supervision
Trapit Bansal, Pat Verga, Neha Choudhary +1
Understanding the meaning of text often involves reasoning about entities and their relationships. This requires identifying textual mentions of entities, linking them to a canonic…
Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Autoencoders
Andrew Drozdov, Pat Verga, Mohit Yadav +2
We introduce deep inside-outside recursive autoencoders (DIORA), a fully-unsupervised method for discovering syntax that simultaneously learns representations for constituents with…
Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking
Shikhar Murty*, Patrick Verga*, Luke Vilnis +2
Extraction from raw text to a knowledge base of entities and fine-grained types is often cast as prediction into a flat set of entity and type labels, neglecting the rich hierarchi…
Linguistically-Informed Self-Attention for Semantic Role Labeling
Emma Strubell, Patrick Verga, Daniel Andor +2
Current state-of-the-art semantic role labeling (SRL) uses a deep neural network with no explicit linguistic features. However, prior work has shown that gold syntax trees can dram…
Simultaneously Self-Attending to All Mentions for Full-Abstract Biological Relation Extraction
Patrick Verga, Emma Strubell, Andrew McCallum
Most work in relation extraction forms a prediction by looking at a short span of text within a single sentence containing a single entity pair mention. This approach often does no…
Finer Grained Entity Typing with TypeNet
Shikhar Murty, Patrick Verga, Luke Vilnis +1
We consider the challenging problem of entity typing over an extremely fine grained set of types, wherein a single mention or entity can have many simultaneous and often hierarchic…
Attending to All Mention Pairs for Full Abstract Biological Relation Extraction
Patrick Verga, Emma Strubell, Ofer Shai +1
Most work in relation extraction forms a prediction by looking at a short span of text within a single sentence containing a single entity pair mention. However, many relation type…
Fast and Accurate Entity Recognition with Iterated Dilated Convolutions
Emma Strubell, Patrick Verga, David Belanger +1
Today when many practitioners run basic NLP on the entire web and large-volume traffic, faster methods are paramount to saving time and energy costs. Recent advances in GPU hardwar…
Generalizing to Unseen Entities and Entity Pairs with Row-less Universal Schema
Patrick Verga, Arvind Neelakantan, Andrew McCallum
Universal schema predicts the types of entities and relations in a knowledge base (KB) by jointly embedding the union of all available schema types---not only types from multiple s…
Row-less Universal Schema
Patrick Verga, Andrew McCallum
Universal schema jointly embeds knowledge bases and textual patterns to reason about entities and relations for automatic knowledge base construction and information extraction. In…
Multilingual Relation Extraction using Compositional Universal Schema
Patrick Verga, David Belanger, Emma Strubell +2
Universal schema builds a knowledge base (KB) of entities and relations by jointly embedding all relation types from input KBs as well as textual patterns expressing relations from…