165 citations · 165 across the 5 of their papers we have counts for
5 papers
Fine-Tuning LLMs with Fine-Grained Human Feedback on Text Spans
Sky CH-Wang, Justin Svegliato, Helen Appel +1
We present a method and dataset for fine-tuning language models with preference supervision using feedback-driven improvement chains. Given a model response, an annotator provides…
Accelerating Language Model Workflows with Prompt Choreography
TJ Bai, Jason Eisner
Large language models are increasingly deployed in multi-agent workflows. We introduce Prompt Choreography, a framework that efficiently executes LLM workflows by maintaining a dyn…
Structure-Aware Path Inference for Neural Finite State Transducers
Weiting Tan, Chu-cheng Lin, Jason Eisner
Neural finite-state transducers (NFSTs) form an expressive family of neurosymbolic sequence transduction models. An NFST models each string pair as having been generated by a laten…
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…
The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process
Hongyuan Mei, Jason Eisner
Many events occur in the world. Some event types are stochastically excited or inhibited---in the sense of having their probabilities elevated or decreased---by patterns in the seq…