Papers by Omer Goldman
Stop Uploading Test Data in Plain Text: Practical Strategies for Mitigating Data Contamination by Evaluation Benchmarks (2023.emnlp-main)
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| Challenge: | Common NLP models are trained on data crawled from the internet, and it is difficult to audit at scale. |
| Approach: | They propose three strategies to prevent data contamination by encrypting test data and preventing it from being released on the internet. |
| Outcome: | The proposed strategies can make a difference in preventing data contamination. |
Morphological Inflection with Phonological Features (2023.acl-short)
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| Challenge: | Recent advances in morphological tasks can be difficult to solve when little training data is available or when generalizing to previously unseen lemmas. |
| Approach: | They propose two methods to manipulate phonemic data to include phonological features instead of characters. |
| Outcome: | The proposed methods yield comparable results to baseline models, with minor improvements in some languages. |
Morphological Reinflection with Multiple Arguments: An Extended Annotation schema and a Georgian Case Study (2022.acl-short)
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| Challenge: | morphological annotations are a common problem in some languages, but the flat structure of the current schema makes it impossible to treat them. |
| Approach: | They propose a general solution for polypersonal agreement in Georgian language . they extend the existing UniMorph annotation schema to address this problem . |
| Outcome: | The proposed framework covers all possible variants of argument marking, and is accurate and balanced. |
(Un)solving Morphological Inflection: Lemma Overlap Artificially Inflates Models’ Performance (2022.acl-short)
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| Challenge: | Inflection tasks have gained a lot of traction in recent years, mostly via SIGMORPHON's shared-tasks. |
| Approach: | They propose to use split-by-lemma to challenge the generalization capacity of morphological inflection models by employing harder train-test splits. |
| Outcome: | The proposed method is based on a split-by-lemma method that challenges the generalization capacity of the models. |
The Curious Case of Hallucinatory (Un)answerability: Finding Truths in the Hidden States of Over-Confident Large Language Models (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) have been shown to possess impressive capabilities, but they are not problem-free. |
| Approach: | They explore the behavior of large language models when presented with (un)answerable queries. |
| Outcome: | The proposed models encode the answerability of an input query, the authors show . they also show that the first decoded token is a strong indicator . |
Morphology Without Borders: Clause-Level Morphology (2022.tacl-1)
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| Challenge: | Morphological tasks use large multi-lingual datasets that organize words into inflection tables . lack of a clear linguistic and operational definition of what is a word impairs universality of tasks . |
| Approach: | They propose to view morphology as a clause-level phenomenon, rather than word-level . they propose to use a dataset for clause- level morphological tasks in 4 different languages . |
| Outcome: | The proposed dataset for clause-level morphology covers 4 typologically different languages: English, German, Turkish, and Hebrew. |
Weakly Supervised Semantic Parsing with Abstract Examples (P18-1)
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| Challenge: | training semantic parsers from weak supervision complicates training in two ways . spurious programs that accidentally lead to a correct denotation add noise to training . |
| Approach: | They propose to use tokens in both language utterance and program to map denotations to executable programs. |
| Outcome: | The proposed method improves performance and reaches 82.5% accuracy compared to the best reported accuracy so far. |
Is Probing All You Need? Indicator Tasks as an Alternative to Probing Embedding Spaces (2023.findings-emnlp)
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| Challenge: | Existing probing tasks are designed to evaluate the information existing in representations by training a simple classification model. |
| Approach: | They propose to use indicators to query embedding spaces for the existence of certain properties to determine whether a property exists in an embeddable space. |
| Outcome: | The proposed indicators provide a more accurate picture of the information captured and removed compared to probes. |
Minimal Supervision for Morphological Inflection (2021.emnlp-main)
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| Challenge: | Neural models for morphological reinflection tasks have proved to be extremely accurate given ample labeled data, yet labele d data may be slow and costly to obtain. |
| Approach: | They exploit orthographic and semantic regularities in morphological systems to exploit the orthographic regularities on their own to achieve respectable accuracy. |
| Outcome: | The bootstrapping method outperforms hallucination-based methods for morphological reinflection tasks. |
Is It Really Long Context if All You Need Is Retrieval? Towards Genuinely Difficult Long Context NLP (2024.emnlp-main)
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| Challenge: | Improvements in language models’ capabilities have pushed their applications towards longer contexts, making long-context evaluation and development an active research area. |
| Approach: | They propose to unpack the taxonomy of long-context based on the properties that make them more difficult with longer contexts. |
| Outcome: | The proposed taxonomy is based on the properties that make them more difficult with longer contexts. |
UniMorph 4.0: Universal Morphology (2022.lrec-1)
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Khuyagbaatar Batsuren, Omer Goldman, Salam Khalifa, Nizar Habash, Witold Kieraś, Gábor Bella, Brian Leonard, Garrett Nicolai, Kyle Gorman, Yustinus Ghanggo Ate, Maria Ryskina, Sabrina Mielke, Elena Budianskaya, Charbel El-Khaissi, Tiago Pimentel, Michael Gasser, William Abbott Lane, 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 Karahóǧa, 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 White, Rowan Hall Maudslay, Josef Valvoda, Ran Zmigrod, Paula Czarnowska, Irene Nikkarinen, Aelita Salchak, Brijesh Bhatt, Christopher Straughn, Zoey Liu, Jonathan North Washington, Yuval Pinter, Duygu Ataman, Marcin Wolinski, Totok Suhardijanto, Anna Yablonskaya, Niklas Stoehr, Hossep Dolatian, Zahroh Nuriah, Shyam Ratan, Francis M. Tyers, Edoardo M. Ponti, Grant Aiton, Aryaman Arora, Richard J. Hatcher, Ritesh Kumar, Jeremiah Young, Daria Rodionova, Anastasia Yemelina, Taras Andrushko, Igor Marchenko, Polina Mashkovtseva, Alexandra Serova, Emily Prud’hommeaux, Maria Nepomniashchaya, Fausto Giunchiglia, Eleanor Chodroff, Mans Hulden, Miikka Silfverberg, Arya D. McCarthy, David Yarowsky, Ryan Cotterell, Reut Tsarfaty, Ekaterina Vylomova
| Challenge: | The Universal Morphology project provides broad-coverage instantiated morphological inflection tables for hundreds of diverse languages. |
| Approach: | They propose a language-independent feature schema for rich morphological annotation and a type-level resource of annotated data in diverse languages realizing that schema. |
| Outcome: | The proposed schema has added 66 new languages, including 24 endangered languages. |
Location Not Found: Exposing Implicit Local and Global Biases in Multilingual LLMs (2026.acl-long)
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Guy Mor-Lan, Omer Goldman, Matan Eyal, Adi Mayrav Gilady, Sivan Eiger, Idan Szpektor, Avinatan Hassidim, Yossi Matias, Reut Tsarfaty
| Challenge: | Multilingual large language models have minimized the fluency gap between languages, but they are exposed to the risk of biases as knowledge and norms may propagate across languages. |
| Approach: | They propose a test set with 2,156 questions in 12 languages to quantify models' biases . they show a global bias towards answers relevant to the US-locale . |
| Outcome: | The proposed model can answer locale-ambiguous questions in 12 languages. |
Unpacking Tokenization: Evaluating Text Compression and its Correlation with Model Performance (2024.findings-acl)
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| Challenge: | Despite being the cornerstone of BPE, the importance of compression in the tokenization process is still unclear. |
| Approach: | They argue for the theoretical importance of compression in the tokenization process . they also demonstrate the empirical importance of compressing tokenizers for downstream success of pre-trained language models. |
| Outcome: | The proposed method can be viewed as 0-gram language modeling where equal probability is assigned to all tokens. |