Zobrazeno 1 - 10
of 36
pro vyhledávání: '"Aditya, Somak"'
ERVQA: A Dataset to Benchmark the Readiness of Large Vision Language Models in Hospital Environments
Autor:
Ray, Sourjyadip, Gupta, Kushal, Kundu, Soumi, Kasat, Payal Arvind, Aditya, Somak, Goyal, Pawan
The global shortage of healthcare workers has demanded the development of smart healthcare assistants, which can help monitor and alert healthcare workers when necessary. We examine the healthcare knowledge of existing Large Vision Language Models (L
Externí odkaz:
http://arxiv.org/abs/2410.06420
We introduce two paradoxes concerning jailbreak of foundation models: First, it is impossible to construct a perfect jailbreak classifier, and second, a weaker model cannot consistently detect whether a stronger (in a pareto-dominant sense) model is
Externí odkaz:
http://arxiv.org/abs/2406.12702
Tool-augmented Large Language Models (TALMs) are known to enhance the skillset of large language models (LLMs), thereby, leading to their improved reasoning abilities across many tasks. While, TALMs have been successfully employed in different questi
Externí odkaz:
http://arxiv.org/abs/2402.17231
We investigate the knowledge of object affordances in pre-trained language models (LMs) and pre-trained Vision-Language models (VLMs). A growing body of literature shows that PTLMs fail inconsistently and non-intuitively, demonstrating a lack of reas
Externí odkaz:
http://arxiv.org/abs/2402.12881
Reasoning is a fundamental component of language understanding. Recent prompting techniques, such as chain of thought, have consistently improved LLMs' performance on various reasoning tasks. Nevertheless, there is still little understanding of what
Externí odkaz:
http://arxiv.org/abs/2401.10065
Autor:
Hong, Pengfei, Majumder, Navonil, Ghosal, Deepanway, Aditya, Somak, Mihalcea, Rada, Poria, Soujanya
Recent advancements in Large Language Models (LLMs) have showcased striking results on existing logical reasoning benchmarks, with some models even surpassing human performance. However, the true depth of their competencies and robustness in reasonin
Externí odkaz:
http://arxiv.org/abs/2401.09395
Autor:
Luo, Man, Kumbhar, Shrinidhi, shen, Ming, Parmar, Mihir, Varshney, Neeraj, Banerjee, Pratyay, Aditya, Somak, Baral, Chitta
Logical reasoning is fundamental for humans yet presents a substantial challenge in the domain of Artificial Intelligence. Initially, researchers used Knowledge Representation and Reasoning (KR) systems that did not scale and required non-trivial man
Externí odkaz:
http://arxiv.org/abs/2310.00836
Recent explorations with commercial Large Language Models (LLMs) have shown that non-expert users can jailbreak LLMs by simply manipulating their prompts; resulting in degenerate output behavior, privacy and security breaches, offensive outputs, and
Externí odkaz:
http://arxiv.org/abs/2305.14965
Domain shift is a big challenge in NLP, thus, many approaches resort to learning domain-invariant features to mitigate the inference phase domain shift. Such methods, however, fail to leverage the domain-specific nuances relevant to the task at hand.
Externí odkaz:
http://arxiv.org/abs/2305.02858
The Natural Language Inference (NLI) task often requires reasoning over multiple steps to reach the conclusion. While the necessity of generating such intermediate steps (instead of a summary explanation) has gained popular support, it is unclear how
Externí odkaz:
http://arxiv.org/abs/2208.14641