4 citations · 6 across the 7 of their papers we have counts for
5 papers · 1 filter
A Reranker for Orchestrating Heterogeneous Speech and Text Retrievers
Inho Kim, Sumyeong Ahn
Retrieval-Augmented Generation (RAG) systems have attracted significant interest for their ability to mitigate hallucinations in Large Language Models (LLMs). Although knowledge da…
Where Does Robustness Live? Neuron-Guided Adaptation for Retrieval-Augmented Language Models
Jae O Lee, Jaemin Kim, Sumyeong Ahn +1
Retrieval-Augmented Language Models (RALMs) have shown strong potential in knowledge-intensive tasks, yet they remain vulnerable when retrieved contexts are noisy or irrelevant. Ro…
Dual Debiasing for Noisy In-Context Learning for Text Generation
Siqi Liang, Sumyeong Ahn, Paramveer S. Dhillon +1
In context learning (ICL) relies heavily on high quality demonstrations drawn from large annotated corpora. Existing approaches detect noisy annotations by ranking local perplexiti…
Distributed In-Context Learning under Non-IID Among Clients
Siqi Liang, Sumyeong Ahn, Jiayu Zhou
Advancements in large language models (LLMs) have shown their effectiveness in multiple complicated natural language reasoning tasks. A key challenge remains in adapting these mode…
Large Language Models in Medical Term Classification and Unexpected Misalignment Between Response and Reasoning
Xiaodan Zhang, Sandeep Vemulapalli, Nabasmita Talukdar +11
This study assesses the ability of state-of-the-art large language models (LLMs) including GPT-3.5, GPT-4, Falcon, and LLaMA 2 to identify patients with mild cognitive impairment (…