35 citations · 138 across the 16 of their papers we have counts for
17 papers
Over-Searching in Search-Augmented Large Language Models
Roy Xie, Deepak Gopinath, David Qiu +4
Search-augmented large language models (LLMs) excel at knowledge-intensive tasks by integrating external retrieval. However, they often over-search -- unnecessarily invoking search…
Automatic Question-Answer Generation for Long-Tail Knowledge
Rohan Kumar, Youngmin Kim, Sunitha Ravi +4
Pretrained Large Language Models (LLMs) have gained significant attention for addressing open-domain Question Answering (QA). While they exhibit high accuracy in answering question…
SEMQA: Semi-Extractive Multi-Source Question Answering
Tal Schuster, Adam D. Lelkes, Haitian Sun +4
Recently proposed long-form question answering (QA) systems, supported by large language models (LLMs), have shown promising capabilities. Yet, attributing and verifying their gene…
Answering Ambiguous Questions with a Database of Questions, Answers, and Revisions
Haitian Sun, William W. Cohen, Ruslan Salakhutdinov
Many open-domain questions are under-specified and thus have multiple possible answers, each of which is correct under a different interpretation of the question. Answering such am…
Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions?
Yang Chen, Hexiang Hu, Yi Luan +4
Pre-trained vision and language models have demonstrated state-of-the-art capabilities over existing tasks involving images and texts, including visual question answering. However,…
Reasoning over Logically Interacted Conditions for Question Answering
Haitian Sun, William W. Cohen, Ruslan Salakhutdinov
Some questions have multiple answers that are not equally correct, i.e. answers are different under different conditions. Conditions are used to distinguish answers as well as to p…
ConditionalQA: A Complex Reading Comprehension Dataset with Conditional Answers
Haitian Sun, William W. Cohen, Ruslan Salakhutdinov
We describe a Question Answering (QA) dataset that contains complex questions with conditional answers, i.e. the answers are only applicable when certain conditions apply. We call…
Iterative Hierarchical Attention for Answering Complex Questions over Long Documents
Haitian Sun, William W. Cohen, Ruslan Salakhutdinov
We propose a new model, DocHopper, that iteratively attends to different parts of long, hierarchically structured documents to answer complex questions. Similar to multi-hop questi…
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…
Differentiable Open-Ended Commonsense Reasoning
Bill Yuchen Lin, Haitian Sun, Bhuwan Dhingra +3
Current commonsense reasoning research focuses on developing models that use commonsense knowledge to answer multiple-choice questions. However, systems designed to answer multiple…
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…
Faithful Embeddings for Knowledge Base Queries
Haitian Sun, Andrew O. Arnold, Tania Bedrax-Weiss +2
The deductive closure of an ideal knowledge base (KB) contains exactly the logical queries that the KB can answer. However, in practice KBs are both incomplete and over-specified,…
Scalable Neural Methods for Reasoning With a Symbolic Knowledge Base
William W. Cohen, Haitian Sun, R. Alex Hofer +1
We describe a novel way of representing a symbolic knowledge base (KB) called a sparse-matrix reified KB. This representation enables neural modules that are fully differentiable,…
Differentiable Representations For Multihop Inference Rules
William W. Cohen, Haitian Sun, R. Alex Hofer +1
We present efficient differentiable implementations of second-order multi-hop reasoning using a large symbolic knowledge base (KB). We introduce a new operation which can be used t…
PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text
Haitian Sun, Tania Bedrax-Weiss, William W. Cohen
We consider open-domain queston answering (QA) where answers are drawn from either a corpus, a knowledge base (KB), or a combination of both of these. We focus on a setting in whic…
Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text
Haitian Sun, Bhuwan Dhingra, Manzil Zaheer +3
Open Domain Question Answering (QA) is evolving from complex pipelined systems to end-to-end deep neural networks. Specialized neural models have been developed for extracting answ…
Semi-Supervised Learning with Declaratively Specified Entropy Constraints
Haitian Sun, William W. Cohen, Lidong Bing
We propose a technique for declaratively specifying strategies for semi-supervised learning (SSL). The proposed method can be used to specify ensembles of semi-supervised learning,…