2 citations · 2 across the 13 of their papers we have counts for
14 papers
PRAGMA: Evaluating Personalized Guidance with Memory Alignment in Lifelong Conversations
Hyojeong Yu, Hyukhun Koh, Minsung Kim +2
Large language models (LLMs) are increasingly deployed as personalized assistants that interact with users over extended periods of time. As conversations grow longer, relying on f…
Casual as an Anchor: Resolving Supervision Misalignment in Formality Transfer Dataset
Hyojeong Yu, Hyukhun Koh, Minsung Kim +1
Formality transfer is commonly framed as a symmetric bidirectional task between informal and formal registers. We argue that this framing conceals a supervision design flaw in exis…
Confidence-Guided Stepwise Model Routing for Cost-Efficient Reasoning
Sangmook Lee, Dohyung Kim, Hyukhun Koh +2
Recent advances in Large Language Models (LLMs) - particularly model scaling and test-time techniques - have greatly enhanced the reasoning capabilities of language models at the e…
Program Synthesis via Test-Time Transduction
Kang-il Lee, Jahyun Koo, Seunghyun Yoon +4
We introduce transductive program synthesis, a new formulation of the program synthesis task that explicitly leverages test inputs during synthesis. While prior approaches to progr…
Public Data Assisted Differentially Private In-Context Learning
Seongho Joo, Hyukhun Koh, Kyomin Jung
In-context learning (ICL) in Large Language Models (LLMs) has shown remarkable performance across various tasks without requiring fine-tuning. However, recent studies have highligh…
Harmful Prompt Laundering: Jailbreaking LLMs with Abductive Styles and Symbolic Encoding
Seongho Joo, Hyukhun Koh, Kyomin Jung
Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks, but their potential misuse for harmful purposes remains a significant concern. To stren…
Generating Diverse Hypotheses for Inductive Reasoning
Kang-il Lee, Hyukhun Koh, Dongryeol Lee +3
Inductive reasoning - the process of inferring general rules from a small number of observations - is a fundamental aspect of human intelligence. Recent works suggest that large la…
Conditional [MASK] Discrete Diffusion Language Model
Hyukhun Koh, Minha Jhang, Dohyung Kim +2
Although auto-regressive models excel in natural language processing, they often struggle to generate diverse text and provide limited controllability. Non-auto-regressive methods…
Fine-grained Gender Control in Machine Translation with Large Language Models
Minwoo Lee, Hyukhun Koh, Minsung Kim +1
In machine translation, the problem of ambiguously gendered input has been pointed out, where the gender of an entity is not available in the source sentence. To address this ambig…
VLind-Bench: Measuring Language Priors in Large Vision-Language Models
Kang-il Lee, Minbeom Kim, Seunghyun Yoon +4
Large Vision-Language Models (LVLMs) have demonstrated outstanding performance across various multimodal tasks. However, they suffer from a problem known as language prior, where r…
Can LLMs Recognize Toxicity? A Structured Investigation Framework and Toxicity Metric
Hyukhun Koh, Dohyung Kim, Minwoo Lee +1
In the pursuit of developing Large Language Models (LLMs) that adhere to societal standards, it is imperative to detect the toxicity in the generated text. The majority of existing…
DPP-TTS: Diversifying prosodic features of speech via determinantal point processes
Seongho Joo, Hyukhun Koh, Kyomin Jung
With the rapid advancement in deep generative models, recent neural Text-To-Speech(TTS) models have succeeded in synthesizing human-like speech. There have been some efforts to gen…
Target-Agnostic Gender-Aware Contrastive Learning for Mitigating Bias in Multilingual Machine Translation
Minwoo Lee, Hyukhun Koh, Kang-il Lee +3
Gender bias is a significant issue in machine translation, leading to ongoing research efforts in developing bias mitigation techniques. However, most works focus on debiasing bili…
Multi-View Zero-Shot Open Intent Induction from Dialogues: Multi Domain Batch and Proxy Gradient Transfer
Hyukhun Koh, Haesung Pyun, Nakyeong Yang +1
In Task Oriented Dialogue (TOD) system, detecting and inducing new intents are two main challenges to apply the system in the real world. In this paper, we suggest the semantic mul…