16 citations · 18 across the 10 of their papers we have counts for
8 papers · 1 filter
Where Does Retrieval-Based Open-Ended Evaluation Fail? Automatic Taxonomy Induction from Long-Form Medical Answer Factuality Verification
Heyuan Huang, Jirui Dai, Alexandra DeLucia +5
Retrieval-based factuality evaluation, where LLM-generated claims are verified against evidence from authoritative medical corpora, has become the dominant paradigm for scalable ha…
Demo: Statistically Significant Results On Biases and Errors of LLMs Do Not Guarantee Generalizable Results
Jonathan Liu, Haoling Qiu, Jonathan Lasko +3
Recent research has shown that hallucinations, omissions, and biases are prevalent in everyday use-cases of LLMs. However, chatbots used in medical contexts must provide consistent…
MultiMUC: Multilingual Template Filling on MUC-4
William Gantt, Shabnam Behzad, Hannah YoungEun An +4
We introduce MultiMUC, the first multilingual parallel corpus for template filling, comprising translations of the classic MUC-4 template filling benchmark into five languages: Ara…
MegaWika: Millions of reports and their sources across 50 diverse languages
Samuel Barham, Orion Weller, Michelle Yuan +9
To foster the development of new models for collaborative AI-assisted report generation, we introduce MegaWika, consisting of 13 million Wikipedia articles in 50 diverse languages,…
Multilingual Coreference Resolution in Multiparty Dialogue
Boyuan Zheng, Patrick Xia, Mahsa Yarmohammadi +1
Existing multiparty dialogue datasets for entity coreference resolution are nascent, and many challenges are still unaddressed. We create a large-scale dataset, Multilingual Multip…
Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction
Mahsa Yarmohammadi, Shijie Wu, Marc Marone +10
Zero-shot cross-lingual information extraction (IE) describes the construction of an IE model for some target language, given existing annotations exclusively in some other languag…
Gradual Fine-Tuning for Low-Resource Domain Adaptation
Haoran Xu, Seth Ebner, Mahsa Yarmohammadi +3
Fine-tuning is known to improve NLP models by adapting an initial model trained on more plentiful but less domain-salient examples to data in a target domain. Such domain adaptatio…
CopyNext: Explicit Span Copying and Alignment in Sequence to Sequence Models
Abhinav Singh, Patrick Xia, Guanghui Qin +2
Copy mechanisms are employed in sequence to sequence models (seq2seq) to generate reproductions of words from the input to the output. These frameworks, operating at the lexical ty…