85 citations · 203 across the 27 of their papers we have counts for
6 papers · 1 filter
A Glitch in the Matrix? Locating and Detecting Language Model Grounding with Fakepedia
Giovanni Monea, Maxime Peyrard, Martin Josifoski +6
Large language models (LLMs) have an impressive ability to draw on novel information supplied in their context. Yet the mechanisms underlying this contextual grounding remain unkno…
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…
Flows: Building Blocks of Reasoning and Collaborating AI
Martin Josifoski, Lars Klein, Maxime Peyrard +8
Recent advances in artificial intelligence (AI) have produced highly capable and controllable systems. This creates unprecedented opportunities for structured reasoning as well as…
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…