2 citations · 2 across the 11 of their papers we have counts for
4 papers · 2 filters
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…
clembench-2024: A Challenging, Dynamic, Complementary, Multilingual Benchmark and Underlying Flexible Framework for LLMs as Multi-Action Agents
Anne Beyer, Kranti Chalamalasetti, Sherzod Hakimov +3
It has been established in recent work that Large Language Models (LLMs) can be prompted to "self-play" conversational games that probe certain capabilities (general instruction fo…
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…