Papers by Yoav Levine
Generating Benchmarks for Factuality Evaluation of Language Models (2024.eacl-long)
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Dor Muhlgay, Ori Ram, Inbal Magar, Yoav Levine, Nir Ratner, Yonatan Belinkov, Omri Abend, Kevin Leyton-Brown, Amnon Shashua, Yoav Shoham
| Challenge: | Existing methods for factuality evaluation of LLM generation focus on facts sampled from the LM itself and might under-represent domain specific or rare facts. |
| Approach: | They propose a method that transforms a factual corpus into a benchmark evaluating an LM's propensity to generate true facts from the corpus . |
| Outcome: | The proposed framework transforms a factual corpus of interest into a benchmark evaluating an LM's propensity to generate true facts from the corpus vs. similar but incorrect statements. |
SenseBERT: Driving Some Sense into BERT (2020.acl-main)
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Yoav Levine, Barak Lenz, Or Dagan, Ori Ram, Dan Padnos, Or Sharir, Shai Shalev-Shwartz, Amnon Shashua, Yoav Shoham
| Challenge: | Existing approaches for self-supervision operate at word form level, which serves as a surrogate for the underlying semantic content. |
| Approach: | They propose a method to employ weak-supervision directly at the word sense level, without the use of human annotation. |
| Outcome: | The proposed model achieves significantly improved lexical understanding without human annotation on the ‘Word in Context’ task. |
In-Context Retrieval-Augmented Language Models (2023.tacl-1)
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| Challenge: | Existing RALM methods focus on modifying the LM architecture to facilitate incorporation of external information, complicating deployment. |
| Approach: | They propose to condition a language model on relevant documents from a grounding corpus during generation by conditioning on external knowledge sources. |
| Outcome: | The proposed method significantly improves language modeling performance and provides natural source attribution mechanism. |
Parallel Context Windows for Large Language Models (2023.acl-long)
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Nir Ratner, Yoav Levine, Yonatan Belinkov, Ori Ram, Inbal Magar, Omri Abend, Ehud Karpas, Amnon Shashua, Kevin Leyton-Brown, Yoav Shoham
| Challenge: | Existing efforts to address context window limitation for off-the-shelf LLMs involve training specialized architectures. |
| Approach: | They propose a method that carves a long context into chunks and restricts attention to apply only within each window. |
| Outcome: | The proposed method shows significant improvements on in-context learning tasks with diverse input and output spaces. |