Search Results for author:
Found 22 papers, 11 papers with code
EASE: Extractive-Abstractive Summarization End-to-End using the Information Bottleneck Principle
To achieve the best of both worlds, we propose EASE, an extractive-abstractive framework that generates concise abstractive summaries that can be traced back to an extractive summary.
Instruction-tuned Language Models are Better Knowledge Learners
The standard recipe for doing so involves continued pre-training on new documents followed by instruction-tuning on question-answer (QA) pairs.
OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization
To this end, we create OPT-IML Bench: a large benchmark for Instruction Meta-Learning (IML) of 2000 NLP tasks consolidated into task categories from 8 existing benchmarks, and prepare an evaluation framework to measure three types of model generalizations: to tasks from fully held-out categories, to held-out tasks from seen categories, and to held-out instances from seen tasks.
Ranked #26 on
Natural Language Inference
on RTE
Complementary Explanations for Effective In-Context Learning
Large language models (LLMs) have exhibited remarkable capabilities in learning from explanations in prompts, but there has been limited understanding of exactly how these explanations function or why they are effective.
Efficient Large Scale Language Modeling with Mixtures of Experts
This paper presents a detailed empirical study of how autoregressive MoE language models scale in comparison with dense models in a wide range of settings: in- and out-of-domain language modeling, zero- and few-shot priming, and full-shot fine-tuning.
Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs
In this paper, we discuss approaches to detecting when models have beliefs about the world, and we improve on methods for updating model beliefs to be more truthful, with a focus on methods based on learned optimizers or hypernetworks.
RECONSIDER: Improved Re-Ranking using Span-Focused Cross-Attention for Open Domain Question Answering
State-of-the-art Machine Reading Comprehension (MRC) models for Open-domain Question Answering (QA) are typically trained for span selection using distantly supervised positive examples and heuristically retrieved negative examples.
EASE: Extractive-Abstractive Summarization with Explanations
Current abstractive summarization systems outperform their extractive counterparts, but their widespread adoption is inhibited by the inherent lack of interpretability.
FiD-Ex: Improving Sequence-to-Sequence Models for Extractive Rationale Generation
Natural language (NL) explanations of model predictions are gaining popularity as a means to understand and verify decisions made by large black-box pre-trained models, for NLP tasks such as Question Answering (QA) and Fact Verification.
Human Evaluation of Spoken vs. Visual Explanations for Open-Domain QA
While research on explaining predictions of open-domain QA systems (ODQA) to users is gaining momentum, most works have failed to evaluate the extent to which explanations improve user trust.
RECONSIDER: Re-Ranking using Span-Focused Cross-Attention for Open Domain Question Answering
State-of-the-art Machine Reading Comprehension (MRC) models for Open-domain Question Answering (QA) are typically trained for span selection using distantly supervised positive examples and heuristically retrieved negative examples.
Efficient One-Pass End-to-End Entity Linking for Questions
We present ELQ, a fast end-to-end entity linking model for questions, which uses a biencoder to jointly perform mention detection and linking in one pass.
DeLighT: Deep and Light-weight Transformer
We introduce a deep and light-weight transformer, DeLighT, that delivers similar or better performance than standard transformer-based models with significantly fewer parameters.
Ranked #1 on
Machine Translation
on WMT2016 English-French
JuICe: A Large Scale Distantly Supervised Dataset for Open Domain Context-based Code Generation
Interactive programming with interleaved code snippet cells and natural language markdown is recently gaining popularity in the form of Jupyter notebooks, which accelerate prototyping and collaboration.
Learning Programmatic Idioms for Scalable Semantic Parsing
Programmers typically organize executable source code using high-level coding patterns or idiomatic structures such as nested loops, exception handlers and recursive blocks, rather than as individual code tokens.
Mapping Language to Code in Programmatic Context
To study this phenomenon, we introduce the task of generating class member functions given English documentation and the programmatic context provided by the rest of the class.
Neural Semantic Parsing
Semantic parsing, the study of translating natural language utterances into machine-executable programs, is a well-established research area and has applications in question answering, instruction following, voice assistants, and code generation.
Learning to Map Context-Dependent Sentences to Executable Formal Queries
We propose a context-dependent model to map utterances within an interaction to executable formal queries.
Learning a Neural Semantic Parser from User Feedback
We present an approach to rapidly and easily build natural language interfaces to databases for new domains, whose performance improves over time based on user feedback, and requires minimal intervention.
Ranked #1 on
SQL Parsing
on Restaurants
Neural AMR: Sequence-to-Sequence Models for Parsing and Generation
Sequence-to-sequence models have shown strong performance across a broad range of applications.
Ranked #6 on
AMR Parsing
on LDC2015E86
