15 citations · 18 across the 8 of their papers we have counts for
6 papers · 1 filter
Context Filtering with Reward Modeling in Question Answering
Sangryul Kim, James Thorne
Question Answering (QA) in NLP is the task of finding answers to a query within a relevant context retrieved by a retrieval system. Yet, the mix of relevant and irrelevant informat…
CLIcK: A Benchmark Dataset of Cultural and Linguistic Intelligence in Korean
Eunsu Kim, Juyoung Suk, Philhoon Oh +3
Despite the rapid development of large language models (LLMs) for the Korean language, there remains an obvious lack of benchmark datasets that test the requisite Korean cultural a…
Retrieval-Augmented Data Augmentation for Low-Resource Domain Tasks
Minju Seo, Jinheon Baek, James Thorne +1
Despite large successes of recent language models on diverse tasks, they suffer from severe performance degeneration in low-resource settings with limited training data available.…
Detrimental Contexts in Open-Domain Question Answering
Philhoon Oh, James Thorne
For knowledge intensive NLP tasks, it has been widely accepted that accessing more information is a contributing factor to improvements in the model's end-to-end performance. Howev…
Knowledge Corpus Error in Question Answering
Yejoon Lee, Philhoon Oh, James Thorne
Recent works in open-domain question answering (QA) have explored generating context passages from large language models (LLMs), replacing the traditional retrieval step in the QA…
Prometheus: Inducing Fine-grained Evaluation Capability in Language Models
Seungone Kim, Jamin Shin, Yejin Cho +8
Recently, using a powerful proprietary Large Language Model (LLM) (e.g., GPT-4) as an evaluator for long-form responses has become the de facto standard. However, for practitioners…