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pro vyhledávání: '"Yen, Howard"'
Autor:
Yen, Howard, Gao, Tianyu, Hou, Minmin, Ding, Ke, Fleischer, Daniel, Izsak, Peter, Wasserblat, Moshe, Chen, Danqi
There have been many benchmarks for evaluating long-context language models (LCLMs), but developers often rely on synthetic tasks like needle-in-a-haystack (NIAH) or arbitrary subsets of tasks. It remains unclear whether they translate to the diverse
Externí odkaz:
http://arxiv.org/abs/2410.02694
We study continued training and supervised fine-tuning (SFT) of a language model (LM) to make effective use of long-context information. We first establish a reliable evaluation protocol to guide model development -- Instead of perplexity or simple n
Externí odkaz:
http://arxiv.org/abs/2410.02660
Autor:
Su, Hongjin, Yen, Howard, Xia, Mengzhou, Shi, Weijia, Muennighoff, Niklas, Wang, Han-yu, Liu, Haisu, Shi, Quan, Siegel, Zachary S., Tang, Michael, Sun, Ruoxi, Yoon, Jinsung, Arik, Sercan O., Chen, Danqi, Yu, Tao
Existing retrieval benchmarks primarily consist of information-seeking queries (e.g., aggregated questions from search engines) where keyword or semantic-based retrieval is usually sufficient. However, many complex real-world queries require in-depth
Externí odkaz:
http://arxiv.org/abs/2407.12883
Extending large language models (LLMs) to process longer inputs is crucial for a wide range of applications. However, the substantial computational cost of transformers and limited generalization of positional encoding restrict the size of their cont
Externí odkaz:
http://arxiv.org/abs/2402.16617
How do we communicate with others to achieve our goals? We use our prior experience or advice from others, or construct a candidate utterance by predicting how it will be received. However, our experiences are limited and biased, and reasoning about
Externí odkaz:
http://arxiv.org/abs/2311.00687
Large language models (LLMs) have emerged as a widely-used tool for information seeking, but their generated outputs are prone to hallucination. In this work, our aim is to allow LLMs to generate text with citations, improving their factual correctne
Externí odkaz:
http://arxiv.org/abs/2305.14627
Akademický článek
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Publikováno v:
Current Oral Health Reports; Jun2018, Vol. 5 Issue 2, p127-132, 6p
