Daniel Khashabi

EDISON: Feature Extraction for NLP, Simplified

2016

When designing Natural Language Processing (NLP) applications that use Machine Learning (ML) techniques, feature extraction becomes a significant part of the development effort, whether developing a new application or attempting to reproduce results reported for …

Self-Instruct: Aligning Language Models with Self-Generated Instructions

2022 · arXiv (Cornell University)

Large "instruction-tuned" language models (i.e., finetuned to respond to instructions) have demonstrated a remarkable ability to generalize zero-shot to new tasks. Nevertheless, they depend heavily on human-written instruction data that is often limited in quantity, …

Dated Data: Tracing Knowledge Cutoffs in Large Language Models

2024 · arXiv (Cornell University)

Released Large Language Models (LLMs) are often paired with a claimed knowledge cutoff date, or the dates at which training data was gathered. Such information is crucial for applications where the LLM must provide up …

Solving Hard Coreference Problems

2015

Coreference resolution is a key problem in natural language understanding that still escapes reliable solutions. One fundamental difficulty has been that of resolving instances involving pronouns since they often require deep language understanding and use …

Combining Retrieval, Statistics, and Inference to Answer Elementary Science Questions

2016 · Proceedings of the AAAI Conference on Artificial Intelligence

What capabilities are required for an AI system to pass standard 4th Grade Science Tests? Previous work has examined the use of Markov Logic Networks (MLNs) to represent the requisite background knowledge and interpret test …

Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences

2018

Daniel Khashabi, Snigdha Chaturvedi, Michael Roth, Shyam Upadhyay, Dan Roth. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.

Temporal Common Sense Acquisition with Minimal Supervision

2020

Temporal common sense (e.g., duration and frequency of events) is crucial for understanding natural language. However, its acquisition is challenging, partly because such information is often not expressed explicitly in text, and human annotation on …

Self-Instruct: Aligning Language Models with Self-Generated Instructions

2023

Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, Hannaneh Hajishirzi. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.