406 citations · 710 across the 30 of their papers we have counts for
6 papers · 2 filters
Improved Instruction Ordering in Recipe-Grounded Conversation
Duong Minh Le, Ruohao Guo, Wei Xu +1
In this paper, we study the task of instructional dialogue and focus on the cooking domain. Analyzing the generated output of the GPT-J model, we reveal that the primary challenge…
Meta-Tuning LLMs to Leverage Lexical Knowledge for Generalizable Language Style Understanding
Ruohao Guo, Wei Xu, Alan Ritter
Language style is often used by writers to convey their intentions, identities, and mastery of language. In this paper, we show that current large language models struggle to captu…
Are Large Language Models Robust Coreference Resolvers?
Nghia T. Le, Alan Ritter
Recent work on extending coreference resolution across domains and languages relies on annotated data in both the target domain and language. At the same time, pre-trained large la…
Schema-Driven Information Extraction from Heterogeneous Tables
Fan Bai, Junmo Kang, Gabriel Stanovsky +3
In this paper, we explore the question of whether large language models can support cost-efficient information extraction from tables. We introduce schema-driven information extrac…
Translation and Fusion Improves Zero-shot Cross-lingual Information Extraction
Yang Chen, Vedaant Shah, Alan Ritter
Large language models (LLMs) combined with instruction tuning have shown significant progress in information extraction (IE) tasks, exhibiting strong generalization capabilities to…
Distill or Annotate? Cost-Efficient Fine-Tuning of Compact Models
Junmo Kang, Wei Xu, Alan Ritter
Fine-tuning large models is highly effective, however, inference can be expensive and produces carbon emissions. Knowledge distillation has been shown to be a practical solution to…