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Amnon Shashua

Amnon Shashua has 4 papers in our library, in 2020–2024, most of them on Language models & pretraining, Evaluation & benchmarks; often with Yoav Levine, Ori Ram, Yoav Shoham.

4 papers in our library · 2020–2024

OpenAlex · h-index 52 · 179 works · 11,107 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 305 citations in our library · 1 more in our library without a page yet · ⭐ Paper Star rank #3202

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓Hebrew University of JerusalemJerusalem · Israel1994–2026 · 99 paperscurrent🎓Massachusetts Institute of TechnologyCambridge · United States1990–2004 · 16 papers🎓Technion – Israel Institute of TechnologyHaifa · Israel1995–2002 · 7 papers🎓Stanford UniversityStanford · United States2002–2005 · 5 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 more institutions on single bylines are not listed.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 7 Artificial Intelligence · 4 works Arts and Humanities · 4 works Computer Science · 4 works Language and Linguistics · 4 works Social Sciences · 2 works Safety Research · 1 work Sociology and Political Science · 1 work

🧭 Topics 2 Language models & pretraining · 3 works Evaluation & benchmarks · 1 work

 Works by year

2020s

20241 paper20232 papers20201 paper

 Papers

The most cited first (OpenAlex and our library).

  1. SenseBERT: Driving Some Sense into BERT (2020 · 259 citations)
    Self-supervised language models such as BERT learn from word forms, which are only an ambiguous stand-in for meaning.
  2. Fundamental Limitations of Alignment in Large Language Models (2023 · 110 citations)
    Wolf et al. ask whether aligning a large language model, via RLHF finetuning or aligning prompts, can make it robust to adversarial users. To analyze this, they develop Behavior Expectation Bounds (BEB), a framework grounded in probability theory. Each behavior vertical (e.g., helpfulness, politeness) assigns…
  3. Parallel Context Windows for Large Language Models (2023 · 102 citations)
    Off-the-shelf large language models can only read a fixed number of tokens at once. This limits in-context learning, multi-document question answering and other long-input uses. Earlier fixes usually needed specially trained architectures. The authors propose Parallel Context Windows (PCW), which works with any…
  4. Generating Benchmarks for Factuality Evaluation of Language Models (2024 · 79 citations)
    Measuring how often a language model produces false statements in a given domain is hard. Methods that fact-check a model's own samples cannot choose which facts get tested, and they favour common knowledge over rare or domain-specific facts. Perplexity is often used as a stand-in, but it reflects many linguistic…

All works

5 works of theirs in our library, the most cited first; 4 with a page here, the others link their original until we summarise them.

  1. SenseBERT: Driving Some Sense into BERTYoav Levine, Barak Lenz, Or Dagan, et al.Annual Meeting of the Association for… · 259 citations
  2. Fundamental Limitations of Alignment in Large Language ModelsYotam Wolf, Noam Wies, Oshri Avnery, et al.arXiv:2304.11082 · 110 citations
  3. Parallel Context Windows for Large Language ModelsNir Ratner, Yoav Levine, Yonatan Belinkov, et al.Annual Meeting of the Association for… · 102 citations
  4. Generating Benchmarks for Factuality Evaluation of Language ModelsDor Muhlgay, Ori Ram, Inbal Magar, et al.Conference of the European Chapter of the… · 79 citations
  5. The Learnability of In-Context Learning (original ↗)Noam Wies, Yoav Levine, Amnon Shashua2023 · arXiv:2303.07895 · 49 citations · no summary yet

 Co-authors here

8 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 305 times (of the 141,840 papers whose bibliographies we read); 36 citing papers with a page here →.

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