489 citations · 1.9k across the 37 of their papers we have counts for
15 papers · 1 filter
Fly-Swat or Cannon? Cost-Effective Language Model Choice via Meta-Modeling
Marija Šakota, Maxime Peyrard, Robert West
Generative language models (LMs) have become omnipresent across data science. For a wide variety of tasks, inputs can be phrased as natural language prompts for an LM, from whose o…
Artificial Artificial Artificial Intelligence: Crowd Workers Widely Use Large Language Models for Text Production Tasks
Veniamin Veselovsky, Manoel Horta Ribeiro, Robert West
Large language models (LLMs) are remarkable data annotators. They can be used to generate high-fidelity supervised training data, as well as survey and experimental data. With the…
Generating Faithful Synthetic Data with Large Language Models: A Case Study in Computational Social Science
Veniamin Veselovsky, Manoel Horta Ribeiro, Akhil Arora +3
Large Language Models (LLMs) have democratized synthetic data generation, which in turn has the potential to simplify and broaden a wide gamut of NLP tasks. Here, we tackle a perva…
Grammar-Constrained Decoding for Structured NLP Tasks without Finetuning
Saibo Geng, Martin Josifoski, Maxime Peyrard +1
Despite their impressive performance, large language models (LMs) still struggle with reliably generating complex output structures when not finetuned to follow the required output…
REFINER: Reasoning Feedback on Intermediate Representations
Debjit Paul, Mete Ismayilzada, Maxime Peyrard +4
Language models (LMs) have recently shown remarkable performance on reasoning tasks by explicitly generating intermediate inferences, e.g., chain-of-thought prompting. However, the…
Exploiting Asymmetry for Synthetic Training Data Generation: SynthIE and the Case of Information Extraction
Martin Josifoski, Marija Sakota, Maxime Peyrard +1
Large language models (LLMs) have great potential for synthetic data generation. This work shows that useful data can be synthetically generated even for tasks that cannot be solve…