63 citations · 308 across the 11 of their papers we have counts for
11 papers
Lost in the Middle: How Language Models Use Long Contexts
Nelson F. Liu, Kevin Lin, John Hewitt +4
While recent language models have the ability to take long contexts as input, relatively little is known about how well they use longer context. We analyze the performance of langu…
AssistGPT: A General Multi-modal Assistant that can Plan, Execute, Inspect, and Learn
Difei Gao, Lei Ji, Luowei Zhou +4
Recent research on Large Language Models (LLMs) has led to remarkable advancements in general NLP AI assistants. Some studies have further explored the use of LLMs for planning and…
Decomposing Complex Queries for Tip-of-the-tongue Retrieval
Kevin Lin, Kyle Lo, Joseph E. Gonzalez +1
When re-finding items, users who forget or are uncertain about identifying details often rely on creative strategies for expressing their information needs -- complex queries that…
Constructing Taxonomies from Pretrained Language Models
Catherine Chen, Kevin Lin, Dan Klein
We present a method for constructing taxonomic trees (e.g., WordNet) using pretrained language models. Our approach is composed of two modules, one that predicts parenthood relatio…
Evaluating Models' Local Decision Boundaries via Contrast Sets
Matt Gardner, Yoav Artzi, Victoria Basmova +23
Standard test sets for supervised learning evaluate in-distribution generalization. Unfortunately, when a dataset has systematic gaps (e.g., annotation artifacts), these evaluation…
Train Large, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers
Zhuohan Li, Eric Wallace, Sheng Shen +4
Since hardware resources are limited, the objective of training deep learning models is typically to maximize accuracy subject to the time and memory constraints of training and in…
Neural Module Networks for Reasoning over Text
Nitish Gupta, Kevin Lin, Dan Roth +2
Answering compositional questions that require multiple steps of reasoning against text is challenging, especially when they involve discrete, symbolic operations. Neural module ne…
QuaRTz: An Open-Domain Dataset of Qualitative Relationship Questions
Oyvind Tafjord, Matt Gardner, Kevin Lin +1
We introduce the first open-domain dataset, called QuaRTz, for reasoning about textual qualitative relationships. QuaRTz contains general qualitative statements, e.g., "A sunscreen…
Reasoning Over Paragraph Effects in Situations
Kevin Lin, Oyvind Tafjord, Peter Clark +1
A key component of successfully reading a passage of text is the ability to apply knowledge gained from the passage to a new situation. In order to facilitate progress on this kind…
Grammar-based Neural Text-to-SQL Generation
Kevin Lin, Ben Bogin, Mark Neumann +2
The sequence-to-sequence paradigm employed by neural text-to-SQL models typically performs token-level decoding and does not consider generating SQL hierarchically from a grammar.…
DeepBase: Deep Inspection of Neural Networks
Thibault Sellam, Kevin Lin, Ian Yiran Huang +4
Although deep learning models perform remarkably well across a range of tasks such as language translation and object recognition, it remains unclear what high-level logic, if any,…