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pro vyhledávání: '"P. Eshghi"'
Evaluating Large Language Models (LLMs) on reasoning benchmarks demonstrates their ability to solve compositional questions. However, little is known of whether these models engage in genuine logical reasoning or simply rely on implicit cues to gener
Externí odkaz:
http://arxiv.org/abs/2410.20200
In dialogue, the addressee may initially misunderstand the speaker and respond erroneously, often prompting the speaker to correct the misunderstanding in the next turn with a Third Position Repair (TPR). The ability to process and respond appropriat
Externí odkaz:
http://arxiv.org/abs/2409.14247
Autor:
Pantazopoulos, Georgios, Nikandrou, Malvina, Suglia, Alessandro, Lemon, Oliver, Eshghi, Arash
This study explores replacing Transformers in Visual Language Models (VLMs) with Mamba, a recent structured state space model (SSM) that demonstrates promising performance in sequence modeling. We test models up to 3B parameters under controlled cond
Externí odkaz:
http://arxiv.org/abs/2409.05395
Autor:
Suglia, Alessandro, Greco, Claudio, Baker, Katie, Part, Jose L., Papaioannou, Ioannis, Eshghi, Arash, Konstas, Ioannis, Lemon, Oliver
AI personal assistants deployed via robots or wearables require embodied understanding to collaborate with humans effectively. However, current Vision-Language Models (VLMs) primarily focus on third-person view videos, neglecting the richness of egoc
Externí odkaz:
http://arxiv.org/abs/2406.13807
An effective method for combining frozen large language models (LLM) and visual encoders involves a resampler module that creates a `visual prompt' which is provided to the LLM, along with the textual prompt. While this approach has enabled impressiv
Externí odkaz:
http://arxiv.org/abs/2404.13594
Autor:
Pantazopoulos, Georgios, Nikandrou, Malvina, Parekh, Amit, Hemanthage, Bhathiya, Eshghi, Arash, Konstas, Ioannis, Rieser, Verena, Lemon, Oliver, Suglia, Alessandro
Interactive and embodied tasks pose at least two fundamental challenges to existing Vision & Language (VL) models, including 1) grounding language in trajectories of actions and observations, and 2) referential disambiguation. To tackle these challen
Externí odkaz:
http://arxiv.org/abs/2311.04067
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-13 (2024)
Abstract Cu immobilized onto N-doped carbon spheres (Cu/N-doped CS) derived from soybean flour was synthesized via the hydrothermal method and certified as a green high-efficiency catalyst for the regioselective synthesis of 1,4-disubstituted 1H-1,2,
Externí odkaz:
https://doaj.org/article/2397c200447a43c0a4777c1d0c6e5e37
Autor:
Eshghi, Arash, Ashrafzadeh, Arash
In conversation, speakers produce language incrementally, word by word, while continuously monitoring the appropriateness of their own contribution in the dynamically unfolding context of the conversation; and this often leads them to repair their ow
Externí odkaz:
http://arxiv.org/abs/2308.11683
The ability to handle miscommunication is crucial to robust and faithful conversational AI. People usually deal with miscommunication immediately as they detect it, using highly systematic interactional mechanisms called repair. One important type of
Externí odkaz:
http://arxiv.org/abs/2307.16689
Referential ambiguities arise in dialogue when a referring expression does not uniquely identify the intended referent for the addressee. Addressees usually detect such ambiguities immediately and work with the speaker to repair it using meta-communi
Externí odkaz:
http://arxiv.org/abs/2307.15554