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pro vyhledávání: '"Lee, Jay P."'
Conventional methods for Bayesian optimization (BO) primarily involve one-step optimal decisions (e.g., maximizing expected improvement of the next step). To avoid myopic behavior, multi-step lookahead BO algorithms such as rollout strategies conside
Externí odkaz:
http://arxiv.org/abs/2411.00171
Autor:
Park, Seong-Il, Lee, Jay-Yoon
Retrieval Augmented Language Models (RALMs) have gained significant attention for their ability to generate accurate answer and improve efficiency. However, RALMs are inherently vulnerable to imperfect information due to their reliance on the imperfe
Externí odkaz:
http://arxiv.org/abs/2410.15107
Recent Large Language Models (LLMs) have demonstrated strong performance in translation without needing to be finetuned on additional parallel corpora. However, they still underperform for low-resource language pairs. Previous works have focused on m
Externí odkaz:
http://arxiv.org/abs/2410.11693
Autor:
Yoo, Gahyun, Lee, Jay Yoon
Reinforcement learning has shown great promise in aligning language models with human preferences in a variety of text generation tasks, including machine translation. For translation tasks, rewards can easily be obtained from quality estimation (QE)
Externí odkaz:
http://arxiv.org/abs/2410.10228
Recent advancements in large language models have demonstrated enhanced capabilities in visual reasoning tasks by employing additional encoders for aligning different modalities. While the Q-Former has been widely used as a general encoder for aligni
Externí odkaz:
http://arxiv.org/abs/2410.09489
Publikováno v:
KnowledgeNLP@ACL 2024
Retrieval-Augmented Language Models (RALMs) have significantly improved performance in open-domain question answering (QA) by leveraging external knowledge. However, RALMs still struggle with unanswerable queries, where the retrieved contexts do not
Externí odkaz:
http://arxiv.org/abs/2408.04414
Autor:
Son, Hye Ryung, Lee, Jay-Yoon
Recent approaches to controlled text generation (CTG) often involve manipulating the weights or logits of base language models (LMs) at decoding time. However, these methods are inapplicable to latest black-box LMs and ineffective at preserving the c
Externí odkaz:
http://arxiv.org/abs/2407.00740
In the era of Industry 4.0, artificial intelligence (AI) is assuming an increasingly pivotal role within industrial systems. Despite the recent trend within various industries to adopt AI, the actual adoption of AI is not as developed as perceived. A
Externí odkaz:
http://arxiv.org/abs/2406.15784
Autor:
Kim, Kiseung, Lee, Jay-Yoon
The Retrieval Augmented Generation (RAG) framework utilizes a combination of parametric knowledge and external knowledge to demonstrate state-of-the-art performance on open-domain question answering tasks. However, the RAG framework suffers from perf
Externí odkaz:
http://arxiv.org/abs/2406.05794
Autor:
Song, Mooho, Lee, Jay-Yoon
Neural networks (NN) perform well in diverse tasks, but sometimes produce nonsensical results to humans. Most NN models "solely" learn from (input, output) pairs, occasionally conflicting with human knowledge. Many studies indicate injecting human kn
Externí odkaz:
http://arxiv.org/abs/2406.01647