14 citations · 37 across the 8 of their papers we have counts for
10 papers
Will it Merge? On The Causes of Model Mergeability
Adir Rahamim, Asaf Yehudai, Boaz Carmeli +3
Model merging has emerged as a promising technique for combining multiple fine-tuned models into a single multitask model without retraining. However, the factors that determine wh…
Fine-Grained Detection of Context-Grounded Hallucinations Using LLMs
Yehonatan Peisakhovsky, Zorik Gekhman, Yosi Mass +2
Context-grounded hallucinations are cases where model outputs contain information not verifiable against the source text. We study the applicability of LLMs for localizing such hal…
Genie: Achieving Human Parity in Content-Grounded Datasets Generation
Asaf Yehudai, Boaz Carmeli, Yosi Mass +5
The lack of high-quality data for content-grounded generation tasks has been identified as a major obstacle to advancing these tasks. To address this gap, we propose Genie, a novel…
QAID: Question Answering Inspired Few-shot Intent Detection
Asaf Yehudai, Matan Vetzler, Yosi Mass +3
Intent detection with semantically similar fine-grained intents is a challenging task. To address it, we reformulate intent detection as a question-answering retrieval task by trea…
Conversational Search with Mixed-Initiative -- Asking Good Clarification Questions backed-up by Passage Retrieval
Yosi Mass, Doron Cohen, Asaf Yehudai +1
We deal with the scenario of conversational search, where user queries are under-specified or ambiguous. This calls for a mixed-initiative setup. User-asks (queries) and system-ans…
Conversational Document Prediction to Assist Customer Care Agents
Jatin Ganhotra, Haggai Roitman, Doron Cohen +6
A frequent pattern in customer care conversations is the agents responding with appropriate webpage URLs that address users' needs. We study the task of predicting the documents th…