20 citations · 31 across the 6 of their papers we have counts for
6 papers
InstructExcel: A Benchmark for Natural Language Instruction in Excel
Justin Payan, Swaroop Mishra, Mukul Singh +7
With the evolution of Large Language Models (LLMs) we can solve increasingly more complex NLP tasks across various domains, including spreadsheets. This work investigates whether L…
ZEROTOP: Zero-Shot Task-Oriented Semantic Parsing using Large Language Models
Dheeraj Mekala, Jason Wolfe, Subhro Roy
We explore the use of large language models (LLMs) for zero-shot semantic parsing. Semantic parsing involves mapping natural language utterances to task-specific meaning representa…
BenchCLAMP: A Benchmark for Evaluating Language Models on Syntactic and Semantic Parsing
Subhro Roy, Sam Thomson, Tongfei Chen +4
Recent work has shown that generation from a prompted or fine-tuned language model can perform well at semantic parsing when the output is constrained to be a valid semantic repres…
Addressing Resource and Privacy Constraints in Semantic Parsing Through Data Augmentation
Kevin Yang, Olivia Deng, Charles Chen +3
We introduce a novel setup for low-resource task-oriented semantic parsing which incorporates several constraints that may arise in real-world scenarios: (1) lack of similar datase…
Constrained Language Models Yield Few-Shot Semantic Parsers
Richard Shin, Christopher H. Lin, Sam Thomson +7
We explore the use of large pretrained language models as few-shot semantic parsers. The goal in semantic parsing is to generate a structured meaning representation given a natural…
Task-Oriented Dialogue as Dataflow Synthesis
Semantic Machines, Jacob Andreas, John Bufe +43
We describe an approach to task-oriented dialogue in which dialogue state is represented as a dataflow graph. A dialogue agent maps each user utterance to a program that extends th…