Zobrazeno 1 - 10
of 3 766
pro vyhledávání: '"Kevin CHEN"'
We introduce the Extract-Refine-Retrieve-Read (ERRR) framework, a novel approach designed to bridge the pre-retrieval information gap in Retrieval-Augmented Generation (RAG) systems through query optimization tailored to meet the specific knowledge r
Externí odkaz:
http://arxiv.org/abs/2411.07820
Open-domain long-form text generation requires generating coherent, comprehensive responses that address complex queries with both breadth and depth. This task is challenging due to the need to accurately capture diverse facets of input queries. Exis
Externí odkaz:
http://arxiv.org/abs/2410.15511
Topic modeling is a powerful technique for uncovering hidden themes within a collection of documents. However, the effectiveness of traditional topic models often relies on sufficient word co-occurrence, which is lacking in short texts. Therefore, ex
Externí odkaz:
http://arxiv.org/abs/2410.03071
Introduced to enhance the efficiency of large language model (LLM) inference, speculative decoding operates by having a smaller model generate a draft. A larger target model then reviews this draft to align with its output, and any acceptance by the
Externí odkaz:
http://arxiv.org/abs/2312.11462
Long-form question answering (LFQA) poses a challenge as it involves generating detailed answers in the form of paragraphs, which go beyond simple yes/no responses or short factual answers. While existing QA models excel in questions with concise ans
Externí odkaz:
http://arxiv.org/abs/2311.09383
Topic models are one of the compelling methods for discovering latent semantics in a document collection. However, it assumes that a document has sufficient co-occurrence information to be effective. However, in short texts, co-occurrence information
Externí odkaz:
http://arxiv.org/abs/2310.15420
Text style transfer is a prominent task that aims to control the style of text without inherently changing its factual content. To cover more text modification applications, such as adapting past news for current events and repurposing educational ma
Externí odkaz:
http://arxiv.org/abs/2310.14486
We introduce a new task called *entity-centric question generation* (ECQG), motivated by real-world applications such as topic-specific learning, assisted reading, and fact-checking. The task aims to generate questions from an entity perspective. To
Externí odkaz:
http://arxiv.org/abs/2310.14126
We present a novel system that automatically extracts and generates informative and descriptive sentences from the biomedical corpus and facilitates the efficient search for relational knowledge. Unlike previous search engines or exploration systems
Externí odkaz:
http://arxiv.org/abs/2310.11681
Topic models are popular statistical tools for detecting latent semantic topics in a text corpus. They have been utilized in various applications across different fields. However, traditional topic models have some limitations, including insensitivit
Externí odkaz:
http://arxiv.org/abs/2310.04978