Chain-nighting reasoning, where a large language model (LLM) is asked not only to perform multistep actions, but to explain its reasons for taking the steps it makes has been shown to improve LLMS ‘reasoning. A promising use of chain-of-tank (COT) Reasoning is to ensure that LLMS complies with the responsible-IA police. Using COT to optimize … Read more
Asy is everywhere, big language models are an important topic of conversation at this year’s meeting in Association for Computational Linguistics (ACL). Yang Liu, a senior main scientist with Alexa AI and generally meat at this year’s meeting in Association for Computational Linguistic. “We have several sessions about large language models that were not a … Read more
Between the main conference and the recently inaugurated ACL ProceedingsAmazon researchers have more than 65 articles at this year’s meeting in Association for Computational Linguistics (ACL). Automatic speech recognition Masked sound text coders are effective multimodal rescorers*Jason Cai, Monica Sunkara, Xilai Li, Anshu Bhatia, Xiao Pan, Sravan Bodapati Generation code A static evaluation of code … Read more
Teaching large language models (LLMS) to reason is an active research topic in natural linguistic treatment, and a popular approach to this problem is the so-called chain-of-tank paradigm, where a model is not only asked to give an end to giving rational For its answer. The structure of the type of prompt used to induce … Read more
Knowledgeillation (KD) is one of the most effective ways to insert large language models around how low latency is important. KD involves the transfer of knowledge contained in large models (“teachers”) to smaller models (“students”). Sorry about their size, student models are typically more effective than teacher models, but they are often less powerful. In … Read more
Like the area of ​​conversation AI generally, Amazon’s papers are dominated at this year’s meeting in Association for Computational Linguistics (ACL) of working with large language models (LLMS). The properties that make LLMS ‘output so extraordinary – such as linguistic flowering and semantic context – are also notorious difficult to quantify; As such, model evaluation … Read more