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pro vyhledávání: '"Laban A"'
Evaluating retrieval-augmented generation (RAG) systems remains challenging, particularly for open-ended questions that lack definitive answers and require coverage of multiple sub-topics. In this paper, we introduce a novel evaluation framework base
Externí odkaz:
http://arxiv.org/abs/2410.15531
Custom Non-Linear Model Predictive Control for Obstacle Avoidance in Indoor and Outdoor Environments
Navigating complex environments requires Unmanned Aerial Vehicles (UAVs) and autonomous systems to perform trajectory tracking and obstacle avoidance in real-time. While many control strategies have effectively utilized linear approximations, address
Externí odkaz:
http://arxiv.org/abs/2410.02732
LLM-based applications are helping people write, and LLM-generated text is making its way into social media, journalism, and our classrooms. However, the differences between LLM-generated and human-written text remain unclear. To explore this, we hir
Externí odkaz:
http://arxiv.org/abs/2409.14509
The recent developments in Large Language Models (LLM), mark a significant moment in the research and development of social interactions with artificial agents. These agents are widely deployed in a variety of settings, with potential impact on users
Externí odkaz:
http://arxiv.org/abs/2407.01488
Autor:
Spitale, Micol, Axelsson, Minja, Jeong, Sooyeon, Tuttosı, Paige, Stamatis, Caitlin A., Laban, Guy, Lim, Angelica, Gunes, Hatice
Recent research in affective robots has recognized their potential in supporting human well-being. Due to rapidly developing affective and artificial intelligence technologies, this field of research has undergone explosive expansion and advancement
Externí odkaz:
http://arxiv.org/abs/2407.02957
LLMs and RAG systems are now capable of handling millions of input tokens or more. However, evaluating the output quality of such systems on long-context tasks remains challenging, as tasks like Needle-in-a-Haystack lack complexity. In this work, we
Externí odkaz:
http://arxiv.org/abs/2407.01370
Autor:
Agarwal, Divyansh, Fabbri, Alexander R., Risher, Ben, Laban, Philippe, Joty, Shafiq, Wu, Chien-Sheng
Prompt leakage poses a compelling security and privacy threat in LLM applications. Leakage of system prompts may compromise intellectual property, and act as adversarial reconnaissance for an attacker. A systematic evaluation of prompt leakage threat
Externí odkaz:
http://arxiv.org/abs/2404.16251
Recognizing if LLM output can be grounded in evidence is central to many tasks in NLP: retrieval-augmented generation, summarization, document-grounded dialogue, and more. Current approaches to this kind of fact-checking are based on verifying each p
Externí odkaz:
http://arxiv.org/abs/2404.10774
Socially Assistive Robots are studied in different Child-Robot Interaction settings. However, logistical constraints limit accessibility, particularly affecting timely support for mental wellbeing. In this work, we have investigated whether online in
Externí odkaz:
http://arxiv.org/abs/2404.10593
The interdisciplinary nature of Child-Robot Interaction (CRI) fosters incorporating measures and methodologies from many established domains. However, when employing CRI approaches to sensitive avenues of health and wellbeing, caution is critical in
Externí odkaz:
http://arxiv.org/abs/2402.18325