118 citations · 195 across the 5 of their papers we have counts for
5 papers
Revisiting Generalization Across Difficulty Levels: It's Not So Easy
Yeganeh Kordi, Nihal V. Nayak, Max Zuo +2
We investigate how well large language models (LLMs) generalize across different task difficulties, a key question for effective data curation and evaluation. Existing research is…
Tur[k]ingBench: A Challenge Benchmark for Web Agents
Kevin Xu, Yeganeh Kordi, Tanay Nayak +7
Can advanced multi-modal models effectively tackle complex web-based tasks? Such tasks are often found on crowdsourcing platforms, where crowdworkers engage in challenging micro-ta…
Self-Instruct: Aligning Language Models with Self-Generated Instructions
Yizhong Wang, Yeganeh Kordi, Swaroop Mishra +4
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, th…
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi +37
How well can NLP models generalize to a variety of unseen tasks when provided with task instructions? To address this question, we first introduce Super-NaturalInstructions, a benc…
UnifiedQA-v2: Stronger Generalization via Broader Cross-Format Training
Daniel Khashabi, Yeganeh Kordi, Hannaneh Hajishirzi
We present UnifiedQA-v2, a QA model built with the same process as UnifiedQA, except that it utilizes more supervision -- roughly 3x the number of datasets used for UnifiedQA. This…