15 citations · 18 across the 8 of their papers we have counts for
8 papers
Parallel Key-Value Cache Fusion for Position Invariant RAG
Philhoon Oh, Jinwoo Shin, James Thorne
Recent advancements in Large Language Models (LLMs) underscore the necessity of Retrieval Augmented Generation (RAG) to leverage external information. However, LLMs are sensitive t…
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.…
eXplainable Bayesian Multi-Perspective Generative Retrieval
EuiYul Song, Philhoon Oh, Sangryul Kim +1
Modern deterministic retrieval pipelines prioritize achieving state-of-the-art performance but often lack interpretability in decision-making. These models face challenges in asses…
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…