565 citations · 1.2k across the 68 of their papers we have counts for
11 papers · 2 filters
NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics
Ximing Lu, Sean Welleck, Peter West +9
The dominant paradigm for neural text generation is left-to-right decoding from autoregressive language models. Constrained or controllable generation under complex lexical constra…
Prompt Waywardness: The Curious Case of Discretized Interpretation of Continuous Prompts
Daniel Khashabi, Shane Lyu, Sewon Min +8
Fine-tuning continuous prompts for target tasks has recently emerged as a compact alternative to full model fine-tuning. Motivated by these promising results, we investigate the fe…
Time Waits for No One! Analysis and Challenges of Temporal Misalignment
Kelvin Luu, Daniel Khashabi, Suchin Gururangan +2
When an NLP model is trained on text data from one time period and tested or deployed on data from another, the resulting temporal misalignment can degrade end-task performance. In…
Hey AI, Can You Solve Complex Tasks by Talking to Agents?
Tushar Khot, Kyle Richardson, Daniel Khashabi +1
Training giant models from scratch for each complex task is resource- and data-inefficient. To help develop models that can leverage existing systems, we propose a new challenge: L…
Reframing Instructional Prompts to GPTk's Language
Swaroop Mishra, Daniel Khashabi, Chitta Baral +2
What kinds of instructional prompts are easier to follow for Language Models (LMs)? We study this question by conducting extensive empirical analysis that shed light on important f…
Ethical-Advice Taker: Do Language Models Understand Natural Language Interventions?
Jieyu Zhao, Daniel Khashabi, Tushar Khot +2
Is it possible to use natural language to intervene in a model's behavior and alter its prediction in a desired way? We investigate the effectiveness of natural language interventi…
Cross-Task Generalization via Natural Language Crowdsourcing Instructions
Swaroop Mishra, Daniel Khashabi, Chitta Baral +1
Humans (e.g., crowdworkers) have a remarkable ability in solving different tasks, by simply reading textual instructions that define them and looking at a few examples. Despite the…
GooAQ: Open Question Answering with Diverse Answer Types
Daniel Khashabi, Amos Ng, Tushar Khot +3
While day-to-day questions come with a variety of answer types, the current question-answering (QA) literature has failed to adequately address the answer diversity of questions. T…
Think you have Solved Direct-Answer Question Answering? Try ARC-DA, the Direct-Answer AI2 Reasoning Challenge
Sumithra Bhakthavatsalam, Daniel Khashabi, Tushar Khot +6
We present the ARC-DA dataset, a direct-answer ("open response", "freeform") version of the ARC (AI2 Reasoning Challenge) multiple-choice dataset. While ARC has been influential in…
Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies
Mor Geva, Daniel Khashabi, Elad Segal +3
A key limitation in current datasets for multi-hop reasoning is that the required steps for answering the question are mentioned in it explicitly. In this work, we introduce Strate…
GENIE: Toward Reproducible and Standardized Human Evaluation for Text Generation
Daniel Khashabi, Gabriel Stanovsky, Jonathan Bragg +5
While often assumed a gold standard, effective human evaluation of text generation remains an important, open area for research. We revisit this problem with a focus on producing c…