Zobrazeno 1 - 10
of 208
pro vyhledávání: '"A. Zorik"'
Autor:
Orgad, Hadas, Toker, Michael, Gekhman, Zorik, Reichart, Roi, Szpektor, Idan, Kotek, Hadas, Belinkov, Yonatan
Large language models (LLMs) often produce errors, including factual inaccuracies, biases, and reasoning failures, collectively referred to as "hallucinations". Recent studies have demonstrated that LLMs' internal states encode information regarding
Externí odkaz:
http://arxiv.org/abs/2410.02707
Autor:
Ventura, Mor, Toker, Michael, Calderon, Nitay, Gekhman, Zorik, Bitton, Yonatan, Reichart, Roi
Will a Visual Language Model (VLM)-based bot warn us about slipping if it detects a wet floor? Recent VLMs have demonstrated impressive capabilities, yet their ability to infer outcomes and causes remains underexplored. To address this, we introduce
Externí odkaz:
http://arxiv.org/abs/2410.02613
This study empirically tests the $\textit{Narrative Economics}$ hypothesis, which posits that narratives (ideas that are spread virally and affect public beliefs) can influence economic fluctuations. We introduce two curated datasets containing posts
Externí odkaz:
http://arxiv.org/abs/2406.12109
Autor:
Gekhman, Zorik, Yona, Gal, Aharoni, Roee, Eyal, Matan, Feder, Amir, Reichart, Roi, Herzig, Jonathan
When large language models are aligned via supervised fine-tuning, they may encounter new factual information that was not acquired through pre-training. It is often conjectured that this can teach the model the behavior of hallucinating factually in
Externí odkaz:
http://arxiv.org/abs/2405.05904
Autor:
Blau, Yochai, Agrawal, Rohan, Madmony, Lior, Wang, Gary, Rosenberg, Andrew, Chen, Zhehuai, Gekhman, Zorik, Beryozkin, Genady, Haghani, Parisa, Ramabhadran, Bhuvana
Accurate recognition of specific categories, such as persons' names, dates or other identifiers is critical in many Automatic Speech Recognition (ASR) applications. As these categories represent personal information, ethical use of this data includin
Externí odkaz:
http://arxiv.org/abs/2308.07393
Autor:
Calderon, Nitay, Porat, Naveh, Ben-David, Eyal, Chapanin, Alexander, Gekhman, Zorik, Oved, Nadav, Shalumov, Vitaly, Reichart, Roi
Existing research on Domain Robustness (DR) suffers from disparate setups, limited task variety, and scarce research on recent capabilities such as in-context learning. Furthermore, the common practice of measuring DR might not be fully accurate. Cur
Externí odkaz:
http://arxiv.org/abs/2306.00168
Factual consistency evaluation is often conducted using Natural Language Inference (NLI) models, yet these models exhibit limited success in evaluating summaries. Previous work improved such models with synthetic training data. However, the data is t
Externí odkaz:
http://arxiv.org/abs/2305.11171
Most works on modeling the conversation history in Conversational Question Answering (CQA) report a single main result on a common CQA benchmark. While existing models show impressive results on CQA leaderboards, it remains unclear whether they are r
Externí odkaz:
http://arxiv.org/abs/2206.14796
ASR Error Detection (AED) models aim to post-process the output of Automatic Speech Recognition (ASR) systems, in order to detect transcription errors. Modern approaches usually use text-based input, comprised solely of the ASR transcription hypothes
Externí odkaz:
http://arxiv.org/abs/2203.07172
Publikováno v:
iScience, Vol 27, Iss 4, Pp 109551- (2024)
Summary: Polyoxometalates (POMs) have been well studied and explored in electro/photochemical water oxidation catalysis for over a decade. The high solubility of POMs in water has limited its use in homogeneous conditions. Over the last decade, diffe
Externí odkaz:
https://doaj.org/article/25a91203dcfd49e59ee68eb52285758d
