1 citations · 1 across the 6 of their papers we have counts for
6 papers
Subtraction Gets You More: Gap-Aware Retrieval for Multimodal Multi-Hop QA
Sunah O, Jay-Yoon Lee
In multimodal multi-hop question answering, we focus on the initial retrieval stage via two distinct tasks: (1) evidence set completion, retrieving missing evidence given context,…
RARE: Redundancy-Aware Retrieval Evaluation Framework for High-Similarity Corpora
Hanjun Cho, Jay-Yoon Lee
Existing QA benchmarks typically assume distinct documents with minimal overlap, yet real-world retrieval-augmented generation (RAG) systems operate on corpora such as financial re…
Toward Robust RALMs: Revealing the Impact of Imperfect Retrieval on Retrieval-Augmented Language Models
Seong-Il Park, Jay-Yoon Lee
Retrieval Augmented Language Models (RALMs) have gained significant attention for their ability to generate accurate answer and improve efficiency. However, RALMs are inherently vu…
BridG MT: Enhancing LLMs' Machine Translation Capabilities with Sentence Bridging and Gradual MT
Seung-Woo Choi, Ga-Hyun Yoo, Jay-Yoon Lee
Recent Large Language Models (LLMs) have demonstrated impressive translation performance without requiring fine-tuning on additional parallel corpora. However, they still face sign…
Enhancing Robustness of Retrieval-Augmented Language Models with In-Context Learning
Seong-Il Park, Seung-Woo Choi, Na-Hyun Kim +1
Retrieval-Augmented Language Models (RALMs) have significantly improved performance in open-domain question answering (QA) by leveraging external knowledge. However, RALMs still st…
RE-RAG: Improving Open-Domain QA Performance and Interpretability with Relevance Estimator in Retrieval-Augmented Generation
Kiseung Kim, Jay-Yoon Lee
The Retrieval Augmented Generation (RAG) framework utilizes a combination of parametric knowledge and external knowledge to demonstrate state-of-the-art performance on open-domain…