Papers by Allen Lee
HAT: Hallucination Annotation for Translation (2026.acl-long)
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| Challenge: | Hallucinations in machine translation (MT) outputs are prone to hallucination, authors say . lack of high-quality benchmarks for halluciation detection has hindered MT deployments . |
| Approach: | They propose a dataset that provides annotated hallucination distributions and benchmarks . they use 350,959 span-level annotations across 38 language pairs to analyze hallucis a MT output . |
| Outcome: | The proposed dataset provides high-quality benchmarks for hallucination detection in machine translation . the dataset includes 350,959 span-level annotated samples across 38 language pairs . |
INDUS: Effective and Efficient Language Models for Scientific Applications (2024.emnlp-industry)
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Bishwaranjan Bhattacharjee, Aashka Trivedi, Masayasu Muraoka, Muthukumaran Ramasubramanian, Takuma Udagawa, Iksha Gurung, Nishan Pantha, Rong Zhang, Bharath Dandala, Rahul Ramachandran, Manil Maskey, Kaylin Bugbee, Michael Little, Elizabeth Fancher, Irina Gerasimov, Armin Mehrabian, Lauren Sanders, Sylvain Costes, Sergi Blanco-Cuaresma, Kelly Lockhart, Thomas Allen, Felix Grezes, Megan Ansdell, Alberto Accomazzi, Yousef El-Kurdi, Davis Wertheimer, Birgit Pfitzmann, Cesar Berrospi Ramis, Michele Dolfi, Rafael Lima, Panagiotis Vagenas, S. Mukkavilli, Peter Staar, Sanaz Vahidinia, Ryan McGranaghan, Tsengdar Lee
| Challenge: | Large language models trained on general domain corpora showed remarkable results on natural language processing tasks. |
| Approach: | They develop a suite of large language models trained on general domain corpora that address NLP tasks and smaller versions of them created using knowledge distillation. |
| Outcome: | The proposed models outperform general-purpose and domain-specific encoders on new and existing tasks and in industrial settings. |