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cs.CL2025
FOSSIL: Harnessing Feedback on Suboptimal Samples for Data-Efficient Generalisation with Imitation Learning for Embodied Vision-and-Language Tasks
Sabrina McCallum, Amit Parekh, Alessandro Suglia
Current approaches to embodied AI tend to learn policies from expert demonstrations. However, without a mechanism to evaluate the quality of demonstrated actions, they are limited…
cs.CL2025
Triangulating LLM Progress through Benchmarks, Games, and Cognitive Tests
Filippo Momentè, Alessandro Suglia, Mario Giulianelli +6
We examine three evaluation paradigms: standard benchmarks (e.g., MMLU and BBH), interactive games (e.g., Signalling Games or Taboo), and cognitive tests (e.g., for working memory…