Towards a small language model powered chain‐of‐reasoning for open‐domain question answering
Jihyeon Roh,
Minho Kim,
Kyoungman Bae
Abstract:We focus on open‐domain question‐answering tasks that involve a chain‐of‐reasoning, which are primarily implemented using large language models. With an emphasis on cost‐effectiveness, we designed EffiChainQA, an architecture centered on the use of small language models. We employed a retrieval‐based language model to address the limitations of large language models, such as the hallucination issue and the lack of updated knowledge. To enhance reasoning capabilities, we introduced a question decomposer that le… Show more
“…The first paper [1] "Towards a small language model powered chain-of-reasoning for open-domain question answering" by Roh and others focuses on open-domain question-answering tasks that involve a chain of reasoning primarily implemented using large language models. Emphasizing cost effectiveness, the authors introduce EffiChainQA, an architecture centered on the use of small language models.…”
Recent advancements in artificial intelligence (AI) have substantially improved applications that depend on human speech and language comprehension. Human speech, characterized by the articulation of thoughts and emotions through sounds, relies on language, a complex system that uses words and symbols for interpersonal communication. The rapid evolution of AI has amplified the demand for related solutions to swiftly and efficiently process extensive amounts of speech and language data. Speech and language technologies have emerged as major topics in AI research, improving the capacity of computers to comprehend text and spoken language by resembling human cognition. These technological breakthroughs have enabled computers to interpret human language, whether expressed in textual or spoken forms, unveiling the comprehensive meaning of the intentions, nuances, and emotional cues expressed by writers or speakers.
“…The first paper [1] "Towards a small language model powered chain-of-reasoning for open-domain question answering" by Roh and others focuses on open-domain question-answering tasks that involve a chain of reasoning primarily implemented using large language models. Emphasizing cost effectiveness, the authors introduce EffiChainQA, an architecture centered on the use of small language models.…”
Recent advancements in artificial intelligence (AI) have substantially improved applications that depend on human speech and language comprehension. Human speech, characterized by the articulation of thoughts and emotions through sounds, relies on language, a complex system that uses words and symbols for interpersonal communication. The rapid evolution of AI has amplified the demand for related solutions to swiftly and efficiently process extensive amounts of speech and language data. Speech and language technologies have emerged as major topics in AI research, improving the capacity of computers to comprehend text and spoken language by resembling human cognition. These technological breakthroughs have enabled computers to interpret human language, whether expressed in textual or spoken forms, unveiling the comprehensive meaning of the intentions, nuances, and emotional cues expressed by writers or speakers.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.