Papers by Terra Blevins
Translate to Disambiguate: Zero-shot Multilingual Word Sense Disambiguation with Pretrained Language Models (2024.eacl-long)
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| Challenge: | Pretrained language models learn cross-lingual knowledge and perform well on diverse tasks when finetuned. |
| Approach: | They propose a zero-shot prompting approach that captures cross-lingual word sense with a contextual prompt. |
| Outcome: | The proposed approach outperforms baselines on recall in many evaluation languages without additional training or finetuning. |
Analyzing the Mono- and Cross-Lingual Pretraining Dynamics of Multilingual Language Models (2022.emnlp-main)
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| Challenge: | Existing studies on multilingual models have focused on their cross-lingual transfer behavior . a recent study examined multilingual model learning from the multilingual pretraining signal . |
| Approach: | They analyze checkpoints during multilingual pretraining to identify when models acquire in-language and cross-lingual abilities. |
| Outcome: | The proposed model achieves high in-language performance early on, with lower-level linguistic skills acquired before more complex ones. |
Universal NER: A Gold-Standard Multilingual Named Entity Recognition Benchmark (2024.naacl-long)
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Stephen Mayhew, Terra Blevins, Shuheng Liu, Marek Suppa, Hila Gonen, Joseph Marvin Imperial, Börje Karlsson, Peiqin Lin, Nikola Ljubešić, Lester James Miranda, Barbara Plank, Arij Riabi, Yuval Pinter
| Challenge: | In named entity recognition, the majority of annotation efforts are centered on English, and cross-lingual transfer performance remains brittle. |
| Approach: | They propose to develop gold-standard named entity recognition benchmarks in many languages using a cross-lingual consistent schema. |
| Outcome: | The proposed benchmarks will be released to the public in 2022 . they will provide baselines on in-language and cross-lingual learning settings. |
Prompting Language Models for Linguistic Structure (2023.acl-long)
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| Challenge: | Existing prompting methods can test this hypothesis on autoregressive PLMs. |
| Approach: | They propose a structured prompting approach for linguistic structured prediction tasks that performs zero- and few-shot sequence tagging with autoregressive PLMs. |
| Outcome: | The proposed approach shows that the model can perform few-shot sequence tagging on part-of-speech taging, named entity recognition, and sentence chunking tasks. |
BUFFET: Benchmarking Large Language Models for Few-shot Cross-lingual Transfer (2024.naacl-long)
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Akari Asai, Sneha Kudugunta, Xinyan Yu, Terra Blevins, Hila Gonen, Machel Reid, Yulia Tsvetkov, Sebastian Ruder, Hannaneh Hajishirzi
| Challenge: | Recent advances in few-shot generalization in natural language processing focus on English. |
| Approach: | They propose a benchmark that unifies 15 diverse tasks across 54 languages in a sequence-to-sequence format and provides a fixed set of few-shot examples and instructions. |
| Outcome: | The proposed framework unifies 15 diverse tasks across 54 languages in a sequence-to-sequence format and provides a fixed set of few-shot examples and instructions. |
Language Contamination Helps Explains the Cross-lingual Capabilities of English Pretrained Models (2022.emnlp-main)
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| Challenge: | a large number of pretraining corpora are not publicly available, and it is unclear how much foreign language data exists in monolingual models. |
| Approach: | They propose to use English pretraining corpora to analyze their language composition . they find that even when less than 1% of data is not English, it facilitates cross-lingual transfer . |
| Outcome: | The proposed model is not truly monolingual when pretrained at scale, the authors show . they show that even when less than 1% of data is not English, it facilitates cross-lingual transfer . |
FEWS: Large-Scale, Low-Shot Word Sense Disambiguation with the Dictionary (2021.eacl-main)
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| Challenge: | Existing models for Word Sense Disambiguation struggle to disambiguate rare senses . current models struggle to learn senses with few training examples . |
| Approach: | They introduce a low-shot WSD dataset automatically extracted from example sentences in Wiktionary. |
| Outcome: | The proposed dataset outperforms baseline models on rare senses in existing datasets. |
Does Liking Yellow Imply Driving a School Bus? Semantic Leakage in Language Models (2025.naacl-long)
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| Challenge: | Despite their wide adoption, the biases and unintended behaviors of language models remain poorly understood. |
| Approach: | They propose an evaluation setting to detect semantic leakage by humans and automatically . they also curate a diverse test suite for diagnosing this behavior in 13 flagship models . |
| Outcome: | The proposed evaluation setting detects semantic leakage by humans and automatically, and measures it in 13 flagship models. |
Deep RNNs Encode Soft Hierarchical Syntax (P18-2)
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| Challenge: | Existing studies show that syntactic information is useful for a wide variety of NLP tasks. |
| Approach: | They propose to use word-level representations to learn internal representations that capture soft hierarchical notions of syntax from highly varied supervision. |
| Outcome: | The proposed model encodes significant amounts of syntax even without explicit supervision. |
Targeted Multilingual Adaptation for Low-resource Language Families (2024.findings-emnlp)
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| Challenge: | Massively multilingual models are known to have limited utility in any one language, and to perform poorly on low-resource languages. |
| Approach: | They propose to adapt a pre-trained multilingual model to a language family and evaluate its performance on two downstream tasks and 11 evaluation languages. |
| Outcome: | The proposed model outperforms mono- and multilingual models on two downstream tasks and 11 evaluation languages. |
Breaking the Curse of Multilinguality with Cross-lingual Expert Language Models (2024.emnlp-main)
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Terra Blevins, Tomasz Limisiewicz, Suchin Gururangan, Margaret Li, Hila Gonen, Noah Smith, Luke Zettlemoyer
| Challenge: | Multilingual language models often underperform monolingual ones due to inter-language competition for model parameters. |
| Approach: | They propose Cross-lingual Expert Language Models (X-ELM) which mitigates inter-language competition by independently training language models on subsets of the multilingual corpus. |
| Outcome: | The proposed model outperforms jointly trained multilingual models across all 16 considered languages and transfer the gains to downstream tasks. |
Better Character Language Modeling through Morphology (P19-1)
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| Challenge: | Inflected words benefit more from explicitly modeling morphology than uninflectes . morphological supervision is also used to augment character language models in low-resource languages . |
| Approach: | They add morphological supervision to character language models via multitasking to improve BPC performance across 24 languages even when morphology data and language modeling data are disjointed. |
| Outcome: | The addition improves performance even when morphology data and language modeling data are disjointed. |
Moving Down the Long Tail of Word Sense Disambiguation with Gloss Informed Bi-encoders (2020.acl-main)
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| Challenge: | Existing models for Word Sense Disambiguation are not uniformly distributed on rare or unseen senses. |
| Approach: | They propose a bi-encoder model that embeds the target word with its context and the dictionary definition, or gloss, of each sense. |
| Outcome: | The proposed model outperforms previous state-of-the-art models on English all-words WSD, with a 31.1% error reduction on less frequent senses over prior work. |
Do language models accommodate their users? A study of linguistic convergence (2026.eacl-long)
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| Challenge: | In this paper, we examine how large language models adapt their language use to the linguistic patterns of their user. |
| Approach: | They examine whether large language models exhibit linguistic convergence, a pragmatic element of human language communication, and compare their results to original human responses. |
| Outcome: | The proposed model language use is significantly different from that of humans. |
Demystifying Prompts in Language Models via Perplexity Estimation (2023.findings-emnlp)
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| Challenge: | Language models can be prompted to perform a wide variety of tasks with zero- and few-shot learning. |
| Approach: | They propose a method to automatically extend a small seed set of manually written prompts by paraphrasing with GPT3 and backtranslation. |
| Outcome: | The proposed method extends a small seed set of manually written prompts by paraphrasing with GPT3 and backtranslation. |