3 citations · 3 across the 5 of their papers we have counts for
5 papers
EchoTrace: Diagnosing Recursive Risks in LLM-Powered Recommender Systems
Donguk Park, Dongwon Lee, Yeon-Chang Lee
Large language models (LLMs) are increasingly integrated into recommender systems as data augmenters, profile generators, and recommendation modules. While these roles can enhance…
Do LLMs Have Distinct and Consistent Personality? TRAIT: Personality Testset designed for LLMs with Psychometrics
Seungbeen Lee, Seungwon Lim, Seungju Han +9
Recent advancements in Large Language Models (LLMs) have led to their adaptation in various domains as conversational agents. We wonder: can personality tests be applied to these a…
Align-to-Distill: Trainable Attention Alignment for Knowledge Distillation in Neural Machine Translation
Heegon Jin, Seonil Son, Jemin Park +3
The advent of scalable deep models and large datasets has improved the performance of Neural Machine Translation. Knowledge Distillation (KD) enhances efficiency by transferring kn…
Structured Language Generation Model: Loss Calibration and Formatted Decoding for Robust Structure Prediction and Knowledge Retrieval
Minho Lee, Junghyun Min, Yerang Kim +2
Modern generative pre-trained language models excel at open-ended text generation, yet continue to underperform on structure-related tasks such as NER, relation extraction, and sem…
Punctuation Restoration Improves Structure Understanding Without Supervision
Junghyun Min, Minho Lee, Woochul Lee +1
Unsupervised learning objectives like autoregressive and masked language modeling constitute a significant part in producing pre-trained representations that perform various downst…