Zobrazeno 1 - 10
of 42
pro vyhledávání: '"Jacovi, Alon"'
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
Autor:
Sainz, Oscar, García-Ferrero, Iker, Jacovi, Alon, Campos, Jon Ander, Elazar, Yanai, Agirre, Eneko, Goldberg, Yoav, Chen, Wei-Lin, Chim, Jenny, Choshen, Leshem, D'Amico-Wong, Luca, Dell, Melissa, Fan, Run-Ze, Golchin, Shahriar, Li, Yucheng, Liu, Pengfei, Pahwa, Bhavish, Prabhu, Ameya, Sharma, Suryansh, Silcock, Emily, Solonko, Kateryna, Stap, David, Surdeanu, Mihai, Tseng, Yu-Min, Udandarao, Vishaal, Wang, Zengzhi, Xu, Ruijie, Yang, Jinglin
The 1st Workshop on Data Contamination (CONDA 2024) focuses on all relevant aspects of data contamination in natural language processing, where data contamination is understood as situations where evaluation data is included in pre-training corpora u
Externí odkaz:
http://arxiv.org/abs/2407.21530
Is It Really Long Context if All You Need Is Retrieval? Towards Genuinely Difficult Long Context NLP
Improvements in language models' capabilities have pushed their applications towards longer contexts, making long-context evaluation and development an active research area. However, many disparate use-cases are grouped together under the umbrella te
Externí odkaz:
http://arxiv.org/abs/2407.00402
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:
Jacovi, Alon, Bitton, Yonatan, Bohnet, Bernd, Herzig, Jonathan, Honovich, Or, Tseng, Michael, Collins, Michael, Aharoni, Roee, Geva, Mor
Prompting language models to provide step-by-step answers (e.g., "Chain-of-Thought") is the prominent approach for complex reasoning tasks, where more accurate reasoning chains typically improve downstream task performance. Recent literature discusse
Externí odkaz:
http://arxiv.org/abs/2402.00559
A growing area of research investigates augmenting language models with tools (e.g., search engines, calculators) to overcome their shortcomings (e.g., missing or incorrect knowledge, incorrect logical inferences). Various few-shot tool-usage strateg
Externí odkaz:
http://arxiv.org/abs/2310.10062
Publikováno v:
Access-2024-19782
Ensuring quality human-AI interaction (HAII) in safety-critical industries is essential. Failure to do so can lead to catastrophic and deadly consequences. Despite this urgency, existing research on HAII is limited, fragmented, and inconsistent. We p
Externí odkaz:
http://arxiv.org/abs/2310.03392
Data contamination has become prevalent and challenging with the rise of models pretrained on large automatically-crawled corpora. For closed models, the training data becomes a trade secret, and even for open models, it is not trivial to detect cont
Externí odkaz:
http://arxiv.org/abs/2305.10160
Word-level saliency explanations ("heat maps over words") are often used to communicate feature-attribution in text-based models. Recent studies found that superficial factors such as word length can distort human interpretation of the communicated s
Externí odkaz:
http://arxiv.org/abs/2305.02679