Search Results for author:
Found 17 papers, 4 papers with code
Don't lie to your friends: Learning what you know from collaborative self-play
To be helpful assistants, AI agents must be aware of their own capabilities and limitations.
Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More?
Long-context language models (LCLMs) have the potential to revolutionize our approach to tasks traditionally reliant on external tools like retrieval systems or databases.
To Adapt or to Annotate: Challenges and Interventions for Domain Adaptation in Open-Domain Question Answering
Recent advances in open-domain question answering (ODQA) have demonstrated impressive accuracy on standard Wikipedia style benchmarks.
Successive Prompting for Decomposing Complex Questions
The intermediate supervision is typically manually written, which can be expensive to collect.
Tricks for Training Sparse Translation Models
Multi-task learning with an unbalanced data distribution skews model learning towards high resource tasks, especially when model capacity is fixed and fully shared across all tasks.
Generative Context Pair Selection for Multi-hop Question Answering
Compositional reasoning tasks like multi-hop question answering, require making latent decisions to get the final answer, given a question.
Learning with Instance Bundles for Reading Comprehension
When training most modern reading comprehension models, all the questions associated with a context are treated as being independent from each other.
Easy, Reproducible and Quality-Controlled Data Collection with CROWDAQ
High-quality and large-scale data are key to success for AI systems.
Evaluating NLP Models via Contrast Sets
Unfortunately, when a dataset has systematic gaps (e. g., annotation artifacts), these evaluations are misleading: a model can learn simple decision rules that perform well on the test set but do not capture a dataset's intended capabilities.
Dynamic Sampling Strategies for Multi-Task Reading Comprehension
Building general reading comprehension systems, capable of solving multiple datasets at the same time, is a recent aspirational goal in the research community.
Benefits of Intermediate Annotations in Reading Comprehension
Complex compositional reading comprehension datasets require performing latent sequential decisions that are learned via supervision from the final answer.
Evaluating Models' Local Decision Boundaries via Contrast Sets
Unfortunately, when a dataset has systematic gaps (e. g., annotation artifacts), these evaluations are misleading: a model can learn simple decision rules that perform well on the test set but do not capture a dataset's intended capabilities.
ORB: An Open Reading Benchmark for Comprehensive Evaluation of Machine Reading Comprehension
A lot of diverse reading comprehension datasets have recently been introduced to study various phenomena in natural language, ranging from simple paraphrase matching and entity typing to entity tracking and understanding the implications of the context.
Comprehensive Multi-Dataset Evaluation of Reading Comprehension
A lot of diverse reading comprehension datasets have recently been introduced to study various phenomena in natural language, ranging from simple paraphrase matching and entity typing to entity tracking and understanding the implications of the context.
PoMo: Generating Entity-Specific Post-Modifiers in Context
Given a sentence about a target entity, the task is to automatically generate a post-modifier phrase that provides contextually relevant information about the entity.
DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs
We introduce a new English reading comprehension benchmark, DROP, which requires Discrete Reasoning Over the content of Paragraphs.
Ranked #14 on
Question Answering
on DROP Test
Generating Natural Adversarial Examples
Due to their complex nature, it is hard to characterize the ways in which machine learning models can misbehave or be exploited when deployed.
