Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Sadler, Philipp"'
This work analyses the text memorization behavior of large language models (LLMs) when subjected to nucleus sampling. Stochastic decoding methods like nucleus sampling are typically applied to overcome issues such as monotonous and repetitive text ge
Externí odkaz:
http://arxiv.org/abs/2408.16345
Autor:
Beyer, Anne, Chalamalasetti, Kranti, Hakimov, Sherzod, Madureira, Brielen, Sadler, Philipp, Schlangen, David
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 following, strategic goal orientation, language understanding abilities)
Externí odkaz:
http://arxiv.org/abs/2405.20859
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 interaction (by adapting to each other). In this work, we propose a cha
Externí odkaz:
http://arxiv.org/abs/2403.17497
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 we evaluate the adaptability of neural artificial agents towards assum
Externí odkaz:
http://arxiv.org/abs/2402.04824
Autor:
Sadler, Philipp, Schlangen, David
NLP tasks are typically defined extensionally through datasets containing example instantiations (e.g., pairs of image i and text t), but motivated intensionally through capabilities invoked in verbal descriptions of the task (e.g., "t is a descripti
Externí odkaz:
http://arxiv.org/abs/2305.15087
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, we present an initial study that evaluates intra-episodic feedback gi
Externí odkaz:
http://arxiv.org/abs/2305.12880
Autor:
Chalamalasetti, Kranti, Götze, Jana, Hakimov, Sherzod, Madureira, Brielen, Sadler, Philipp, Schlangen, David
Recent work has proposed a methodology for the systematic evaluation of "Situated Language Understanding Agents"-agents that operate in rich linguistic and non-linguistic contexts-through testing them in carefully constructed interactive settings. Ot
Externí odkaz:
http://arxiv.org/abs/2305.13455
Autor:
Sadler, Philipp
The internal workings of modern deep learning models stay often unclear to an external observer, although spatial attention mechanisms are involved. The idea of this work is to translate these spatial attentions into natural language to provide a sim
Externí odkaz:
http://arxiv.org/abs/2010.11701
Learned dynamic weighting of the conditioning signal (attention) has been shown to improve neural language generation in a variety of settings. The weights applied when generating a particular output sequence have also been viewed as providing a pote
Externí odkaz:
http://arxiv.org/abs/1911.03936
Autor:
Sadler, Philipp
This paper examines, if it is possible to learn structural invariants of city images by using only a single reference picture when producing transformations along the variants in the dataset. Previous work explored the problem of learning from only a
Externí odkaz:
http://arxiv.org/abs/1810.08597