Zobrazeno 1 - 10
of 111
pro vyhledávání: '"Majumder, Navonil"'
Autor:
Song, Maojia, Sim, Shang Hong, Bhardwaj, Rishabh, Chieu, Hai Leong, Majumder, Navonil, Poria, Soujanya
LLMs are an integral component of retrieval-augmented generation (RAG) systems. While many studies focus on evaluating the overall quality of end-to-end RAG systems, there is a gap in understanding the appropriateness of LLMs for the RAG task. To add
Externí odkaz:
http://arxiv.org/abs/2409.11242
The widespread applicability and increasing omnipresence of LLMs have instigated a need to align LLM responses to user and stakeholder preferences. Many preference optimization approaches have been proposed that fine-tune LLM parameters to achieve go
Externí odkaz:
http://arxiv.org/abs/2406.15193
Autor:
Kong, Zhifeng, Lee, Sang-gil, Ghosal, Deepanway, Majumder, Navonil, Mehrish, Ambuj, Valle, Rafael, Poria, Soujanya, Catanzaro, Bryan
It is an open challenge to obtain high quality training data, especially captions, for text-to-audio models. Although prior methods have leveraged \textit{text-only language models} to augment and improve captions, such methods have limitations relat
Externí odkaz:
http://arxiv.org/abs/2406.15487
Autor:
Majumder, Navonil, Hung, Chia-Yu, Ghosal, Deepanway, Hsu, Wei-Ning, Mihalcea, Rada, Poria, Soujanya
Generative multimodal content is increasingly prevalent in much of the content creation arena, as it has the potential to allow artists and media personnel to create pre-production mockups by quickly bringing their ideas to life. The generation of au
Externí odkaz:
http://arxiv.org/abs/2404.09956
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:
Melechovsky, Jan, Guo, Zixun, Ghosal, Deepanway, Majumder, Navonil, Herremans, Dorien, Poria, Soujanya
The quality of the text-to-music models has reached new heights due to recent advancements in diffusion models. The controllability of various musical aspects, however, has barely been explored. In this paper, we propose Mustango: a music-domain-know
Externí odkaz:
http://arxiv.org/abs/2311.08355
Visual question answering (VQA) is the task of answering questions about an image. The task assumes an understanding of both the image and the question to provide a natural language answer. VQA has gained popularity in recent years due to its potenti
Externí odkaz:
http://arxiv.org/abs/2310.20159
Recently, the release of INSTRUCTEVAL has provided valuable insights into the performance of large language models (LLMs) that utilize encoder-decoder or decoder-only architecture. Interestingly, despite being introduced four years ago, T5-based LLMs
Externí odkaz:
http://arxiv.org/abs/2307.02053
There are significant challenges for speaker adaptation in text-to-speech for languages that are not widely spoken or for speakers with accents or dialects that are not well-represented in the training data. To address this issue, we propose the use
Externí odkaz:
http://arxiv.org/abs/2305.18028
The pre-trained speech encoder wav2vec 2.0 performs very well on various spoken language understanding (SLU) tasks. However, on many tasks, it trails behind text encoders with textual input. To improve the understanding capability of SLU encoders, va
Externí odkaz:
http://arxiv.org/abs/2305.12301