Papers by Orgad Keller
Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback (2023.acl-long)
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Paul Roit, Johan Ferret, Lior Shani, Roee Aharoni, Geoffrey Cideron, Robert Dadashi, Matthieu Geist, Sertan Girgin, Leonard Hussenot, Orgad Keller, Nikola Momchev, Sabela Ramos Garea, Piotr Stanczyk, Nino Vieillard, Olivier Bachem, Gal Elidan, Avinatan Hassidim, Olivier Pietquin, Idan Szpektor
| Challenge: | Recent advances in abstractive summarization systems produce factually inconsistent text . this is emphasized in tasks like summarizing, which often produce inconsistent text with no input article . |
| Approach: | They use reinforcement learning to optimize for factual consistency and explore trade-offs . they use textual-entailment rewards to optimize the accuracy of the generated summaries . |
| Outcome: | The proposed method improves faithfulness, salience and conciseness of the generated summaries. |
On the Robustness of Dialogue History Representation in Conversational Question Answering: A Comprehensive Study and a New Prompt-based Method (2023.tacl-1)
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| Challenge: | Existing models show impressive results on a common CQA benchmark, but are they robust to domain, setting and domain? |
| Approach: | They propose a prompt-based history modeling approach that adds textual prompts directly to the text of a passage. |
| Outcome: | The proposed model is simple, easy to plug into practically any model and highly effective. |
Are LLMs Better than Reported? Detecting Label Errors and Mitigating Their Effect on Model Performance (2025.emnlp-main)
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| Challenge: | Recent advances in large language models (LLMs) offer new opportunities to enhance the annotation process, particularly for detecting label errors in existing datasets. |
| Approach: | They propose to use an ensemble of large language models to flag mislabeled examples by using an LLM-as-a-judge approach to detect label errors in existing datasets. |
| Outcome: | The proposed method improves label accuracy and consistency in large language models. |
Semantically Driven Sentence Fusion: Modeling and Evaluation (2020.findings-emnlp)
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| Challenge: | Sentence fusion is the task of joining related sentences into coherent text. |
| Approach: | They propose a method where ground-truth solutions are automatically expanded into multiple references via curated equivalence classes of connective phrases. |
| Outcome: | The proposed approach improves on state-of-the-art models by expanding ground-truth solutions into multiple references. |