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pro vyhledávání: '"Cohn, Anthony G"'
Autor:
Cohn, Anthony G, Blackwell, Robert E
Qualitative Spatial Reasoning is a well explored area of Knowledge Representation and Reasoning and has multiple applications ranging from Geographical Information Systems to Robotics and Computer Vision. Recently, many claims have been made for the
Externí odkaz:
http://arxiv.org/abs/2411.19589
Large language models (LLMs) are stochastic, and not all models give deterministic answers, even when setting temperature to zero with a fixed random seed. However, few benchmark studies attempt to quantify uncertainty, partly due to the time and cos
Externí odkaz:
http://arxiv.org/abs/2410.03492
Autor:
Cohn, Anthony G, Blackwell, Robert E
We investigate the abilities of a representative set of Large language Models (LLMs) to reason about cardinal directions (CDs). To do so, we create two datasets: the first, co-created with ChatGPT, focuses largely on recall of world knowledge about C
Externí odkaz:
http://arxiv.org/abs/2406.16528
Navigating historical narratives poses a challenge in unveiling the spatial intricacies of past landscapes. The proposed work addresses this challenge within the context of the English Lake District, employing the Corpus of the Lake District Writing.
Externí odkaz:
http://arxiv.org/abs/2406.14336
Autor:
Huang, Youcheng, Tang, Jingkun, Feng, Duanyu, Zhang, Zheng, Lei, Wenqiang, Lv, Jiancheng, Cohn, Anthony G.
People tell lies when seeking rewards. Large language models (LLMs) are aligned to human values with reinforcement learning where they get rewards if they satisfy human preference. We find that this also induces dishonesty in helpful and harmless ali
Externí odkaz:
http://arxiv.org/abs/2406.01931
Spatial reasoning plays a vital role in both human cognition and machine intelligence, prompting new research into language models' (LMs) capabilities in this regard. However, existing benchmarks reveal shortcomings in evaluating qualitative spatial
Externí odkaz:
http://arxiv.org/abs/2405.15064
Artificial intelligence (AI) has made remarkable progress across various domains, with large language models like ChatGPT gaining substantial attention for their human-like text-generation capabilities. Despite these achievements, spatial reasoning r
Externí odkaz:
http://arxiv.org/abs/2401.03991
Autor:
La Malfa, Emanuele, Petrov, Aleksandar, Frieder, Simon, Weinhuber, Christoph, Burnell, Ryan, Cohn, Anthony G., Shadbolt, Nigel, Wooldridge, Michael
Some of the most powerful language models currently are proprietary systems, accessible only via (typically restrictive) web or software programming interfaces. This is the Language-Models-as-a-Service (LMaaS) paradigm. Contrasting with scenarios whe
Externí odkaz:
http://arxiv.org/abs/2309.16573
Autor:
Cohn, Anthony G
Qualitative Spatial Reasoning (QSR) is well explored area of Commonsense Reasoning and has multiple applications ranging from Geographical Information Systems to Robotics and Computer Vision. Recently many claims have been made for the capabilities o
Externí odkaz:
http://arxiv.org/abs/2309.15577
Language models have become very popular recently and many claims have been made about their abilities, including for commonsense reasoning. Given the increasingly better results of current language models on previous static benchmarks for commonsens
Externí odkaz:
http://arxiv.org/abs/2304.11164