Papers by Beliz Gunel
Self-training Improves Pre-training for Natural Language Understanding (2021.naacl-main)
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Jingfei Du, Edouard Grave, Beliz Gunel, Vishrav Chaudhary, Onur Celebi, Michael Auli, Veselin Stoyanov, Alexis Conneau
| Challenge: | Unsupervised pretraining has led to improvements in natural language understanding . a data augmentation method can be used to generate labels for unlabeled examples . |
| Approach: | They propose a semi-supervised method which uses unlabeled data to retrieve sentences from a database of billions of unlabed sentences crawled from the web. |
| Outcome: | The proposed method improves on standard text classification benchmarks by 2.6% and knowledge distillation by few shots. |
PRISM: Efficient Long-Range Reasoning With Short-Context LLMs (2025.emnlp-main)
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| Challenge: | Existing solutions to long-range language tasks require large compute budgets and complex task-specific design choices. |
| Approach: | They propose an in-context method that uses structured schemas to generate short-contemporary outputs. |
| Outcome: | a new in-context method outperforms baselines on diverse tasks with 4x shorter contexts . it scales down to tiny contexts without increasing costs or sacrificing quality . |
SUMIE: A Synthetic Benchmark for Incremental Entity Summarization (2025.coling-main)
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| Challenge: | Existing datasets that test incrementally update entity summaries are lacking. |
| Approach: | They propose a fully synthetic dataset that exposes real-world IES challenges by generating diverse attributes, summaries, and unstructured paragraphs with 99% alignment accuracy. |
| Outcome: | The proposed dataset shows that state-of-the-art LLMs struggle to update summaries with an F1 higher than 80.4%. |
Enhancing Incremental Summarization with Structured Representations (2024.findings-emnlp)
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| Challenge: | Large language models struggle with processing extensive input contexts, leading to redundancy or incoherency. |
| Approach: | They propose a chain-of-key update based on JSON structured memory representations to improve summarization performance by 40% and 14% on two public datasets. |
| Outcome: | The proposed method improves summarization performance by 40% and 14% on two datasets. |