Papers by Koren Lazar
Filling the Gaps in Ancient Akkadian Texts: A Masked Language Modelling Approach (2021.emnlp-main)
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| Challenge: | cuneiform clay tablets were written in 2500 BCE - 100 CE and are a target of extensive transcription and transliteration efforts due to their deterioration. |
| Approach: | They propose to use a masked language modelling task to complete missing text given cuneiform clay tablets written on cuniform signswedges (2500 BCE - 100 CE) they develop models which automatically complete these missing signs based on contextual cues and greedy decoding schemes. |
| Outcome: | The proposed models perform well on missing token prediction (89% hit@5) despite data scarcity (1M tokens), and human evaluations show that they are able to transcribe texts in extinct languages. |
Collecting a Large-Scale Gender Bias Dataset for Coreference Resolution and Machine Translation (2021.findings-emnlp)
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| Challenge: | Recent studies have found evidence of gender bias in machine translation and coreference resolution models using mostly synthetic diagnostic datasets. |
| Approach: | They propose a semi-automatic method to vastly extend synthetic, small diagnostic datasets to include grammatical patterns indicating stereotypical and non-stereotypical gender-role assignments. |
| Outcome: | The proposed method extends the existing dataset to 108K diverse English sentences. |
Generating OpenAPI Specifications from Online API Documentation with Large Language Models (2025.acl-industry)
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Koren Lazar, Matan Vetzler, Kiran Kate, Jason Tsay, David Boaz, Himanshu Gupta, Avraham Shinnar, Rohith D Vallam, David Amid, Esther Goldbraich, Jim Laredo, Ateret Anaby Tavor
| Challenge: | API specifications are often presented as unstructured HTML pages, requiring external users to manually convert it into a structured format. |
| Approach: | They propose a framework that transforms long API documentation pages into consistent, machine-readable API specifications. |
| Outcome: | The proposed framework generalizes well across hundreds of APIs and produces valid OpenAPI specifications that encapsulate most of the information from the original documentation. |
Effective Red-Teaming of Policy-Adherent Agents (2025.emnlp-main)
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| Challenge: | Large Language Model (LLM)-based agents are increasingly used in domains with strict policies, such as refund eligibility or cancellation rules. |
| Approach: | They propose a multi-agent red-teaming system that leverages policy-aware persuasive strategies to undermine a policy-adherence agent in a customer-service scenario. |
| Outcome: | The proposed model outperforms jailbreak methods and tau-break to assess agent's robustness against manipulative user behavior. |