Omer Goldman

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8ranked-venue papers

4as first author

7since 2021 · last 2026

0000-0001-5863-4856ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 8 · 4 first-author · 7 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence 6 papers		Language models and text generation · 44% Trustworthy machine learning · 40% Information extraction and text analysis · 14%
Databases, data mining, and information retrieval 1 paper		Information retrieval · 100%

Topics — the 12 heaviest of 14, each with the papers that count most for it

Topic	Weight	Papers	Last	Evidence papers
Machine learning › Trustworthy machine learning › fairness bias in language models	1.0	1	2026	Location Not Found: Exposing Implicit Local and Global Biases in Multilingual LLMs · ACL (1) 2026
Machine learning › Trustworthy machine learning fairness	1.0	1	2026	Location Not Found: Exposing Implicit Local and Global Biases in Multilingual LLMs · ACL (1) 2026
Natural language and speech › Language models and text generation large language model	1.0	1	2026	Location Not Found: Exposing Implicit Local and Global Biases in Multilingual LLMs · ACL (1) 2026
Natural language and speech › Language models and text generation multilingual language models	1.0	1	2026	Location Not Found: Exposing Implicit Local and Global Biases in Multilingual LLMs · ACL (1) 2026
Natural language and speech › Language models and text generation › language modeling › long-context language modeling › context utilization long-context modeling	0.8	1	2024	Is It Really Long Context if All You Need Is Retrieval? Towards Genuinely Difficult Long Context NLP · EMNLP 2024
Natural language and speech › Language models and text generation › large language model evaluation data contamination	0.7	1	2023	Stop Uploading Test Data in Plain Text: Practical Strategies for Mitigating Data Contamination by Evaluation Benchmarks · EMNLP 2023
Machine learning › Trustworthy machine learning hallucination	0.7	1	2023	The Curious Case of Hallucinatory (Un)answerability: Finding Truths in the Hidden States of Over-Confident Large Language Models · EMNLP 2023
Natural language and speech › Information extraction and text analysis › computational morphology morphological inflection	0.5	1	2021	Minimal Supervision for Morphological Inflection · EMNLP (1) 2021
Natural language and speech › Information extraction and text analysis semantic parsing	0.3	1	2018	Weakly Supervised Semantic Parsing with Abstract Examples · ACL (1) 2018
Natural language and speech › Information extraction and text analysis › semantic parsing weakly supervised semantic parsing	0.3	1	2018	Weakly Supervised Semantic Parsing with Abstract Examples · ACL (1) 2018
Information retrieval › document retrieval › text search long-context retrieval	0.2	1	2024	Is It Really Long Context if All You Need Is Retrieval? Towards Genuinely Difficult Long Context NLP · EMNLP 2024
Computer vision › Vision and language visual reasoning	0.1	1	2018	Weakly Supervised Semantic Parsing with Abstract Examples · ACL (1) 2018

Methods — techniques the papers use, named apart from their topics

probing hidden states · 0.7licensing · 0.7encryption · 0.7decoding analysis · 0.7distance-based word pairing · 0.5bootstrapping · 0.5analogy-based word tagging · 0.5lifting · 0.3abstract examples · 0.3

Year	Publication	Venue	Position
2026	Location Not Found: Exposing Implicit Local and Global Biases in Multilingual LLMs abstract Guy Mor-Lan, Omer Goldman, Matan Eyal, Adi Mayrav Gilady, Sivan Eiger, Idan Szpektor, Avinatan Hassidim, Yossi Matias, Reut Tsarfaty. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026. Guy Mor, Omer Goldman, Matan Eyal, Adi Mayrav Gilady, Sivan Eiger, Idan Szpektor, Avinatan Hassidim, Yossi Matias, Reut Tsarfaty	ACL (1)	2
2024	Is It Really Long Context if All You Need Is Retrieval? Towards Genuinely Difficult Long Context NLP abstract Improvements in language models' capabilities have pushed their applications towards longer contexts, making long-context evaluation and development an active research area.However, many disparate use cases are grouped together under the umbrella term of "long-context", defined simply by the total length of the model's input, including -for example -Needle-in-a-Haystack tasks, book summarization, and information aggregation.Given their varied difficulty, in this position paper we argue that conflating different tasks by their context length is unproductive.As a community, we require a more precise vocabulary to understand what makes long-context tasks similar or different.We propose to unpack the taxonomy of longcontext based on the properties that make them more difficult with longer contexts.We propose two orthogonal axes of difficulty: (I) Dispersion: How hard is it to find the necessary information in the context?(II) Scope: How much necessary information is there to find?We survey the literature on long context, provide justification for this taxonomy as an informative descriptor, and situate the literature with respect to it.We conclude that the most difficult and interesting settings, whose necessary information is very long and highly dispersed within the input, is severely under-explored.By using a descriptive vocabulary and discussing the relevant properties of difficulty in long context, we can implement more informed research in this area.We call for a careful design of tasks and benchmarks with distinctly long context, taking into account the characteristics that make it qualitatively different from shorter context. Omer Goldman, Alon Jacovi, Aviv Slobodkin, Aviya Maimon, Ido Dagan, Reut Tsarfaty	EMNLP	1
2023	Stop Uploading Test Data in Plain Text: Practical Strategies for Mitigating Data Contamination by Evaluation Benchmarks abstract Data contamination has become prevalent and challenging with the rise of models pretrained on large automatically-crawled corpora.For closed models, the training data becomes a trade secret, and even for open models, it is not trivial to detect contamination.Strategies such as leaderboards with hidden answers, or using test data which is guaranteed to be unseen, are expensive and become fragile with time.Assuming that all relevant actors value clean test data and will cooperate to mitigate data contamination, what can be done?We propose three strategies that can make a difference: (1) Test data made public should be encrypted with a public key and licensed to disallow derivative distribution; (2) demand training exclusion controls from closed API holders, and protect your test data by refusing to evaluate without them; (3) avoid data which appears with its solution on the internet, and release the web-page context of internet-derived data along with the data.These strategies are practical and can be effective in preventing data contamination. Alon Jacovi, Avi Caciularu, Omer Goldman, Yoav Goldberg	EMNLP	3
2023	The Curious Case of Hallucinatory (Un)answerability: Finding Truths in the Hidden States of Over-Confident Large Language Models abstract Large language models (LLMs) have been shown to possess impressive capabilities, while also raising crucial concerns about the faithfulness of their responses.A primary issue arising in this context is the management of (un)answerable queries by LLMs, which often results in hallucinatory behavior due to overconfidence.In this paper, we explore the behavior of LLMs when presented with (un)answerable queries.We ask: do models represent the fact that the question is (un)answerable when generating a hallucinatory answer?Our results show strong indications that such models encode the answerability of an input query, with the representation of the first decoded token often being a strong indicator.These findings shed new light on the spatial organization within the latent representations of LLMs, unveiling previously unexplored facets of these models.Moreover, they pave the way for the development of improved decoding techniques with better adherence to factual generation, particularly in scenarios where query (un)answerability is a concern. 1 Aviv Slobodkin, Omer Goldman, Avi Caciularu, Ido Dagan, Shauli Ravfogel	EMNLP	2
2022	UniMorph 4.0: Universal Morphology abstract The Universal Morphology (UniMorph) project is a collaborative effort providing broad-coverage instantiated normalized morphological inflection tables for hundreds of diverse world languages. The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation, and a type-level resource of annotated data in diverse languages realizing that schema. This paper presents the expansions and improvements on several fronts that were made in the last couple of years (since McCarthy et al. (2020)). Collaborative efforts by numerous linguists have added 66 new languages, including 24 endangered languages. We have implemented several improvements to the extraction pipeline to tackle some issues, e.g., missing gender and macrons information. We have amended the schema to use a hierarchical structure that is needed for morphological phenomena like multiple-argument agreement and case stacking, while adding some missing morphological features to make the schema more inclusive. In light of the last UniMorph release, we also augmented the database with morpheme segmentation for 16 languages. Lastly, this new release makes a push towards inclusion of derivational morphology in UniMorph by enriching the data and annotation schema with instances representing derivational processes from MorphyNet. Khuyagbaatar Batsuren, Omer Goldman, Salam Khalifa, Nizar Habash, Witold Kieras, Gábor Bella, Brian Leonard, Garrett Nicolai, Kyle Gorman, Yustinus Ghanggo Ate, Maria Ryskina, Sabrina J. Mielke, Elena Budianskaya, Charbel El-Khaissi, Tiago Pimentel, Michael Gasser, William Lane 0002, Mohit Raj, Matt Coler, Jaime Rafael Montoya Samame, Delio Siticonatzi Camaiteri, Esaú Zumaeta Rojas, Didier López Francis, Arturo Oncevay, Juan López Bautista, Gema Celeste Silva Villegas, Lucas Torroba Hennigen, Adam Ek, David Guriel, Peter Dirix, Jean-Philippe Bernardy, Andrey Scherbakov, Aziyana Bayyr-ool, Antonios Anastasopoulos, Roberto Zariquiey, Karina Sheifer, Sofya Ganieva, Hilaria Cruz, Ritván Karahóga, Stella Markantonatou, George Pavlidis, Matvey Plugaryov, Elena Klyachko, Ali Salehi, Candy Angulo, Jatayu Baxi, Andrew Krizhanovsky, Natalia Krizhanovskaya, Elizabeth Salesky, Clara Vania, Sardana Ivanova, Jennifer C. White, Rowan Hall Maudslay, Josef Valvoda, Ran Zmigrod, Paula Czarnowska, Irene Nikkarinen, Aelita Salchak, Brijesh Bhatt, Christopher Straughn, Zoey Liu, Jonathan Washington, Yuval Pinter, Duygu Ataman, Marcin Wolinski, Totok Suhardijanto, Anna Yablonskaya, Niklas Stoehr, Hossep Dolatian, Zahroh Nuriah, Shyam Ratan, Francis M. Tyers, Edoardo Maria Ponti, Grant Aiton, Aryaman Arora, Richard J. Hatcher, Ritesh Kumar 0002, Jeremiah Young, Daria Rodionova, Anastasia Yemelina, Taras Andrushko, Igor Marchenko, Polina Mashkovtseva, Alexandra Serova, Emily Tucker Prud'hommeaux, Maria Nepomniashchaya, Fausto Giunchiglia, Eleanor Chodroff, Mans Hulden, Miikka Silfverberg, Arya McCarthy, David Yarowsky, Ryan Cotterell, Reut Tsarfaty, Ekaterina Vylomova	LREC	2
2022	Morphology Without Borders: Clause-Level Morphology abstract Abstract Morphological tasks use large multi-lingual datasets that organize words into inflection tables, which then serve as training and evaluation data for various tasks. However, a closer inspection of these data reveals profound cross-linguistic inconsistencies, which arise from the lack of a clear linguistic and operational definition of what is a word, and which severely impair the universality of the derived tasks. To overcome this deficiency, we propose to view morphology as a clause-level phenomenon, rather than word-level. It is anchored in a fixed yet inclusive set of features, that encapsulates all functions realized in a saturated clause. We deliver MightyMorph, a novel dataset for clause-level morphology covering 4 typologically different languages: English, German, Turkish, and Hebrew. We use this dataset to derive 3 clause-level morphological tasks: inflection, reinflection and analysis. Our experiments show that the clause-level tasks are substantially harder than the respective word-level tasks, while having comparable complexity across languages. Furthermore, redefining morphology to the clause-level provides a neat interface with contextualized language models (LMs) and allows assessing the morphological knowledge encoded in these models and their usability for morphological tasks. Taken together, this work opens up new horizons in the study of computational morphology, leaving ample space for studying neural morphology cross-linguistically. Omer Goldman, Reut Tsarfaty	Trans. Assoc. Comput. Linguistics	1
2021	Minimal Supervision for Morphological Inflection abstract Neural models for the various flavours of morphological reinflection tasks have proven to be extremely accurate given ample labeled data, yet labeled data may be slow and costly to obtain.In this work we aim to overcome this annotation bottleneck by bootstrapping labeled data from a seed as small as five labeled inflection tables, accompanied by a large bulk of unlabeled text.Our bootstrapping method exploits the orthographic and semantic regularities in morphological systems in a two-phased setup, where word tagging based on analogies is followed by word pairing based on distances.Our experiments with the Paradigm Cell Filling Problem over eight typologically different languages show that in languages with relatively simple morphology, orthographic regularities on their own allow inflection models to achieve respectable accuracy.Combined orthographic and semantic regularities alleviate difficulties with particularly complex morpho-phonological systems.We further show that our bootstrapping methods substantially outperform hallucination-based methods commonly used for overcoming the annotation bottleneck in morphological reinflection tasks. Omer Goldman, Reut Tsarfaty	EMNLP (1)	1
2018	Weakly Supervised Semantic Parsing with Abstract Examples abstract Training semantic parsers from weak supervision (denotations) rather than strong supervision (programs) complicates training in two ways.First, a large search space of potential programs needs to be explored at training time to find a correct program.Second, spurious programs that accidentally lead to a correct denotation add noise to training.In this work we propose that in closed worlds with clear semantic types, one can substantially alleviate these problems by utilizing an abstract representation, where tokens in both the language utterance and program are lifted to an abstract form.We show that these abstractions can be defined with a handful of lexical rules and that they result in sharing between different examples that alleviates the difficulties in training.To test our approach, we develop the first semantic parser for CNLVR, a challenging visual reasoning dataset, where the search space is large and overcoming spuriousness is critical, because denotations are either TRUE or FALSE, and thus random programs are likely to lead to a correct denotation.Our method substantially improves performance, and reaches 82.5% accuracy, a 14.7% absolute accuracy improvement compared to the best reported accuracy so far. Omer Goldman, Veronica Latcinnik, Ehud Nave, Amir Globerson, Jonathan Berant	ACL (1)	1