22 citations · 32 across the 9 of their papers we have counts for
9 papers
Evaluating Compositional Scene Understanding in Multimodal Generative Models
Shuhao Fu, Andrew Jun Lee, Anna Wang +4
The visual world is fundamentally compositional. Visual scenes are defined by the composition of objects and their relations. Hence, it is essential for computer vision systems to…
Collective Innovation in Groups of Large Language Models
Eleni Nisioti, Sebastian Risi, Ida Momennejad +2
Human culture relies on collective innovation: our ability to continuously explore how existing elements in our environment can be combined to create new ones. Language is hypothes…
Are LLMs Good Cryptic Crossword Solvers?
Abdelrahman Sadallah, Daria Kotova, Ekaterina Kochmar
Cryptic crosswords are puzzles that rely not only on general knowledge but also on the solver's ability to manipulate language on different levels and deal with various types of wo…
Decoding In-Context Learning: Neuroscience-inspired Analysis of Representations in Large Language Models
Safoora Yousefi, Leo Betthauser, Hosein Hasanbeig +2
Large language models (LLMs) exhibit remarkable performance improvement through in-context learning (ICL) by leveraging task-specific examples in the input. However, the mechanisms…
ALLURE: Auditing and Improving LLM-based Evaluation of Text using Iterative In-Context-Learning
Hosein Hasanbeig, Hiteshi Sharma, Leo Betthauser +2
From grading papers to summarizing medical documents, large language models (LLMs) are evermore used for evaluation of text generated by humans and AI alike. However, despite their…
Evaluating Cognitive Maps and Planning in Large Language Models with CogEval
Ida Momennejad, Hosein Hasanbeig, Felipe Vieira +5
Recently an influx of studies claim emergent cognitive abilities in large language models (LLMs). Yet, most rely on anecdotes, overlook contamination of training sets, or lack syst…
Interaction-Grounded Learning with Action-inclusive Feedback
Tengyang Xie, Akanksha Saran, Dylan J. Foster +5
Consider the problem setting of Interaction-Grounded Learning (IGL), in which a learner's goal is to optimally interact with the environment with no explicit reward to ground its p…
Social Network Structure Shapes Innovation: Experience-sharing in RL with SAPIENS
Eleni Nisioti, Mateo Mahaut, Pierre-Yves Oudeyer +2
Human culture relies on innovation: our ability to continuously explore how existing elements can be combined to create new ones. Innovation is not solitary, it relies on collectiv…
Interaction-Grounded Learning
Tengyang Xie, John Langford, Paul Mineiro +1
Consider a prosthetic arm, learning to adapt to its user's control signals. We propose Interaction-Grounded Learning for this novel setting, in which a learner's goal is to interac…