6 citations · 11 across the 5 of their papers we have counts for
5 papers · 1 filter
Large Language Models Enable Few-Shot Clustering
Vijay Viswanathan, Kiril Gashteovski, Carolin Lawrence +2
Unlike traditional unsupervised clustering, semi-supervised clustering allows users to provide meaningful structure to the data, which helps the clustering algorithm to match the u…
Offline Reinforcement Learning from Human Feedback in Real-World Sequence-to-Sequence Tasks
Julia Kreutzer, Stefan Riezler, Carolin Lawrence
Large volumes of interaction logs can be collected from NLP systems that are deployed in the real world. How can this wealth of information be leveraged? Using such interaction log…
Learning Neural Sequence-to-Sequence Models from Weak Feedback with Bipolar Ramp Loss
Laura Jehl, Carolin Lawrence, Stefan Riezler
In many machine learning scenarios, supervision by gold labels is not available and consequently neural models cannot be trained directly by maximum likelihood estimation (MLE). In…
Counterfactual Learning from Human Proofreading Feedback for Semantic Parsing
Carolin Lawrence, Stefan Riezler
In semantic parsing for question-answering, it is often too expensive to collect gold parses or even gold answers as supervision signals. We propose to convert model outputs into a…
Improving a Neural Semantic Parser by Counterfactual Learning from Human Bandit Feedback
Carolin Lawrence, Stefan Riezler
Counterfactual learning from human bandit feedback describes a scenario where user feedback on the quality of outputs of a historic system is logged and used to improve a target sy…