5 papers
The Unreasonable Ineffectiveness of Nucleus Sampling on Mitigating Text Memorization
Luka Borec, Philipp Sadler, David Schlangen
This work analyses the text memorization behavior of large language models (LLMs) when subjected to nucleus sampling. Stochastic decoding methods like nucleus sampling are typicall…
Sharing the Cost of Success: A Game for Evaluating and Learning Collaborative Multi-Agent Instruction Giving and Following Policies
Philipp Sadler, Sherzod Hakimov, David Schlangen
In collaborative goal-oriented settings, the participants are not only interested in achieving a successful outcome, but do also implicitly negotiate the effort they put into the i…
Learning Communication Policies for Different Follower Behaviors in a Collaborative Reference Game
Philipp Sadler, Sherzod Hakimov, David Schlangen
Albrecht and Stone (2018) state that modeling of changing behaviors remains an open problem "due to the essentially unconstrained nature of what other agents may do". In this work…
Pento-DIARef: A Diagnostic Dataset for Learning the Incremental Algorithm for Referring Expression Generation from Examples
Philipp Sadler, David Schlangen
NLP tasks are typically defined extensionally through datasets containing example instantiations (e.g., pairs of image i and text t), but motivated intensionally through capabiliti…
Yes, this Way! Learning to Ground Referring Expressions into Actions with Intra-episodic Feedback from Supportive Teachers
Philipp Sadler, Sherzod Hakimov, David Schlangen
The ability to pick up on language signals in an ongoing interaction is crucial for future machine learning models to collaborate and interact with humans naturally. In this paper,…