Zobrazeno 1 - 10
of 60
pro vyhledávání: '"Caciularu, Avi"'
Autor:
Jacovi, Alon, Ambar, Moran, Ben-David, Eyal, Shaham, Uri, Feder, Amir, Geva, Mor, Marcus, Dror, Caciularu, Avi
There is a growing line of research on verifying the correctness of language models' outputs. At the same time, LMs are being used to tackle complex queries that require reasoning. We introduce CoverBench, a challenging benchmark focused on verifying
Externí odkaz:
http://arxiv.org/abs/2408.03325
Various tasks, such as summarization, multi-hop question answering, or coreference resolution, are naturally phrased over collections of real-world documents. Such tasks present a unique set of challenges, revolving around the lack of coherent narrat
Externí odkaz:
http://arxiv.org/abs/2406.16086
Autonomous agents that interact with graphical user interfaces (GUIs) hold significant potential for enhancing user experiences. To further improve these experiences, agents need to be personalized and proactive. By effectively comprehending user int
Externí odkaz:
http://arxiv.org/abs/2406.14314
Autor:
Cattan, Arie, Jacovi, Alon, Fabrikant, Alex, Herzig, Jonathan, Aharoni, Roee, Rashkin, Hannah, Marcus, Dror, Hassidim, Avinatan, Matias, Yossi, Szpektor, Idan, Caciularu, Avi
Despite recent advancements in Large Language Models (LLMs), their performance on tasks involving long contexts remains sub-optimal. In-Context Learning (ICL) with few-shot examples may be an appealing solution to enhance LLM performance in this scen
Externí odkaz:
http://arxiv.org/abs/2406.13632
Autor:
Caciularu, Avi, Jacovi, Alon, Ben-David, Eyal, Goldshtein, Sasha, Schuster, Tal, Herzig, Jonathan, Elidan, Gal, Globerson, Amir
Large Language Models (LLMs) often do not perform well on queries that require the aggregation of information across texts. To better evaluate this setting and facilitate modeling efforts, we introduce TACT - Text And Calculations through Tables, a d
Externí odkaz:
http://arxiv.org/abs/2406.03618
Autor:
Jurenka, Irina, Kunesch, Markus, McKee, Kevin R., Gillick, Daniel, Zhu, Shaojian, Wiltberger, Sara, Phal, Shubham Milind, Hermann, Katherine, Kasenberg, Daniel, Bhoopchand, Avishkar, Anand, Ankit, Pîslar, Miruna, Chan, Stephanie, Wang, Lisa, She, Jennifer, Mahmoudieh, Parsa, Rysbek, Aliya, Ko, Wei-Jen, Huber, Andrea, Wiltshire, Brett, Elidan, Gal, Rabin, Roni, Rubinovitz, Jasmin, Pitaru, Amit, McAllister, Mac, Wilkowski, Julia, Choi, David, Engelberg, Roee, Hackmon, Lidan, Levin, Adva, Griffin, Rachel, Sears, Michael, Bar, Filip, Mesar, Mia, Jabbour, Mana, Chaudhry, Arslan, Cohan, James, Thiagarajan, Sridhar, Levine, Nir, Brown, Ben, Gorur, Dilan, Grant, Svetlana, Hashimshoni, Rachel, Weidinger, Laura, Hu, Jieru, Chen, Dawn, Dolecki, Kuba, Akbulut, Canfer, Bileschi, Maxwell, Culp, Laura, Dong, Wen-Xin, Marchal, Nahema, Van Deman, Kelsie, Misra, Hema Bajaj, Duah, Michael, Ambar, Moran, Caciularu, Avi, Lefdal, Sandra, Summerfield, Chris, An, James, Kamienny, Pierre-Alexandre, Mohdi, Abhinit, Strinopoulous, Theofilos, Hale, Annie, Anderson, Wayne, Cobo, Luis C., Efron, Niv, Ananda, Muktha, Mohamed, Shakir, Heymans, Maureen, Ghahramani, Zoubin, Matias, Yossi, Gomes, Ben, Ibrahim, Lila
A major challenge facing the world is the provision of equitable and universal access to quality education. Recent advances in generative AI (gen AI) have created excitement about the potential of new technologies to offer a personal tutor for every
Externí odkaz:
http://arxiv.org/abs/2407.12687
Despite it being the cornerstone of BPE, the most common tokenization algorithm, the importance of compression in the tokenization process is still unclear. In this paper, we argue for the theoretical importance of compression, that can be viewed as
Externí odkaz:
http://arxiv.org/abs/2403.06265
Understanding the internal representations of large language models (LLMs) can help explain models' behavior and verify their alignment with human values. Given the capabilities of LLMs in generating human-understandable text, we propose leveraging t
Externí odkaz:
http://arxiv.org/abs/2401.06102
Fusion-in-Decoder (FiD) is an effective retrieval-augmented language model applied across a variety of open-domain tasks, such as question answering, fact checking, etc. In FiD, supporting passages are first retrieved and then processed using a gener
Externí odkaz:
http://arxiv.org/abs/2310.13682
Large language models (LLMs) have been shown to possess impressive capabilities, while also raising crucial concerns about the faithfulness of their responses. A primary issue arising in this context is the management of (un)answerable queries by LLM
Externí odkaz:
http://arxiv.org/abs/2310.11877