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pro vyhledávání: '"Cattan, A"'
Autor:
Cattan, Arie, Roit, Paul, Zhang, Shiyue, Wan, David, Aharoni, Roee, Szpektor, Idan, Bansal, Mohit, Dagan, Ido
There has been an increasing interest in detecting hallucinations in model-generated texts, both manually and automatically, at varying levels of granularity. However, most existing methods fail to precisely pinpoint the errors. In this work, we intr
Externí odkaz:
http://arxiv.org/abs/2410.07473
Autor:
Roit, Paul, Slobodkin, Aviv, Hirsch, Eran, Cattan, Arie, Klein, Ayal, Pyatkin, Valentina, Dagan, Ido
Detecting semantic arguments of a predicate word has been conventionally modeled as a sentence-level task. The typical reader, however, perfectly interprets predicate-argument relations in a much wider context than just the sentence where the predica
Externí odkaz:
http://arxiv.org/abs/2408.04246
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
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
Recent efforts to address hallucinations in Large Language Models (LLMs) have focused on attributed text generation, which supplements generated texts with citations of supporting sources for post-generation fact-checking and corrections. Yet, these
Externí odkaz:
http://arxiv.org/abs/2403.17104
Various NLP tasks require a complex hierarchical structure over nodes, where each node is a cluster of items. Examples include generating entailment graphs, hierarchical cross-document coreference resolution, annotating event and subevent relations,
Externí odkaz:
http://arxiv.org/abs/2311.11301
Autor:
Andreev, Anton, Cattan, Grégoire
We present a new framework called KorraAI for conceiving and building embodied conversational agents (ECAs). Our framework models ECAs' behavior considering contextual information, for example, about environment and interaction time, and uncertain in
Externí odkaz:
http://arxiv.org/abs/2307.10693
Key Point Analysis (KPA) has been recently proposed for deriving fine-grained insights from collections of textual comments. KPA extracts the main points in the data as a list of concise sentences or phrases, termed key points, and quantifies their p
Externí odkaz:
http://arxiv.org/abs/2306.03853
Machine translation (MT) requires a wide range of linguistic capabilities, which current end-to-end models are expected to learn implicitly by observing aligned sentences in bilingual corpora. In this work, we ask: \emph{How well do MT models learn c
Externí odkaz:
http://arxiv.org/abs/2302.08464
Low information transfer rate is a major bottleneck for brain-computer interfaces based on non-invasive electroencephalography (EEG) for clinical applications. This led to the development of more robust and accurate classifiers. In this study, we inv
Externí odkaz:
http://arxiv.org/abs/2302.02648