Výsledky vyhledávání

Report

Jamba-1.5: Hybrid Transformer-Mamba Models at Scale

Autor: Jamba Team, Lenz, Barak, Arazi, Alan, Bergman, Amir, Manevich, Avshalom, Peleg, Barak, Aviram, Ben, Almagor, Chen, Fridman, Clara, Padnos, Dan, Gissin, Daniel, Jannai, Daniel, Muhlgay, Dor, Zimberg, Dor, Gerber, Edden M, Dolev, Elad, Krakovsky, Eran, Safahi, Erez, Schwartz, Erez, Cohen, Gal, Shachaf, Gal, Rozenblum, Haim, Bata, Hofit, Blass, Ido, Magar, Inbal, Dalmedigos, Itay, Osin, Jhonathan, Fadlon, Julie, Rozman, Maria, Danos, Matan, Gokhman, Michael, Zusman, Mor, Gidron, Naama, Ratner, Nir, Gat, Noam, Rozen, Noam, Fried, Oded, Leshno, Ohad, Antverg, Omer, Abend, Omri, Lieber, Opher, Dagan, Or, Cohavi, Orit, Alon, Raz, Belson, Ro'i, Cohen, Roi, Gilad, Rom, Glozman, Roman, Lev, Shahar, Meirom, Shaked, Delbari, Tal, Ness, Tal, Asida, Tomer, Gal, Tom Ben, Braude, Tom, Pumerantz, Uriya, Cohen, Yehoshua, Belinkov, Yonatan, Globerson, Yuval, Levy, Yuval Peleg, Shoham, Yoav

We present Jamba-1.5, new instruction-tuned large language models based on our Jamba architecture. Jamba is a hybrid Transformer-Mamba mixture of experts architecture, providing high throughput and low memory usage across context lengths, while retai

Externí odkaz: http://arxiv.org/abs/2408.12570

Zobrazit plný text záznamu

Report

Evaluating the Ripple Effects of Knowledge Editing in Language Models

Autor: Cohen, Roi, Biran, Eden, Yoran, Ori, Globerson, Amir, Geva, Mor

Modern language models capture a large body of factual knowledge. However, some facts can be incorrectly induced or become obsolete over time, resulting in factually incorrect generations. This has led to the development of various editing methods th

Externí odkaz: http://arxiv.org/abs/2307.12976

Zobrazit plný text záznamu

Report

LM vs LM: Detecting Factual Errors via Cross Examination

Autor: Cohen, Roi, Hamri, May, Geva, Mor, Globerson, Amir

A prominent weakness of modern language models (LMs) is their tendency to generate factually incorrect text, which hinders their usability. A natural question is whether such factual errors can be detected automatically. Inspired by truth-seeking mec

Externí odkaz: http://arxiv.org/abs/2305.13281

Zobrazit plný text záznamu

Report

Crawling the Internal Knowledge-Base of Language Models

Autor: Cohen, Roi, Geva, Mor, Berant, Jonathan, Globerson, Amir

Language models are trained on large volumes of text, and as a result their parameters might contain a significant body of factual knowledge. Any downstream task performed by these models implicitly builds on these facts, and thus it is highly desira

Externí odkaz: http://arxiv.org/abs/2301.12810

Zobrazit plný text záznamu