activity
20212026
most citedCorrecting Negative Bias in Large Language Models through Negative Attention Score Alignment

2 citations · 3 across the 12 of their papers we have counts for

collaborators

13 papers

cs.CL2026

Solar Open 2 Technical Report

Sungrae Park, Sanghoon Kim, Gyoungjin Gim +50

We present Solar Open 2, a 250B-A15B Mixture-of-Experts language model built for long-horizon agentic tasks, scaled up from Solar Open 1 (Solar Open 100B). To hold entire agent tra…

cs.CL2026

Knowledge Integration Decay in Search-Augmented Reasoning of Large Language Models

Sangwon Yu, Ik-hwan Kim, Donghun Kang +6

Modern Large Language Models (LLMs) have demonstrated remarkable capabilities in complex tasks by employing search-augmented reasoning to incorporate external knowledge into long c…

cs.AI2026

Metacognitive Behavioral Tuning of Large Language Models for Multi-Hop Question Answering

Ik-hwan Kim, Hyeongrok Han, Mingi Jung +5

Large Language Models (LLMs) often produce incorrect answers on multi-hop question answering even when the reasoning trace already contains a correct intermediate conclusion. We at…

cs.CV2026

Contextualized Visual Personalization in Vision-Language Models

Yeongtak Oh, Sangwon Yu, Junsung Park +3

Despite recent progress in vision-language models (VLMs), existing approaches often fail to generate personalized responses based on the user's specific experiences, as they lack t…

cs.CL2025

A Multifaceted Analysis of Negative Bias in Large Language Models through the Lens of Parametric Knowledge

Jongyoon Song, Sangwon Yu, Sungroh Yoon

Negative bias refers to the tendency of large language models (LLMs) to excessively generate negative responses in binary decision tasks (e.g., yes-no question answering). Previous…

cs.SD2025

Does Your Voice Assistant Remember? Analyzing Conversational Context Recall and Utilization in Voice Interaction Models

Heeseung Kim, Che Hyun Lee, Sangkwon Park +4

Recent advancements in multi-turn voice interaction models have improved user-model communication. However, while closed-source models effectively retain and recall past utterances…

cs.CV2025

Know "No" Better: A Data-Driven Approach for Enhancing Negation Awareness in CLIP

Junsung Park, Jungbeom Lee, Jongyoon Song +3

While CLIP has significantly advanced multimodal understanding by bridging vision and language, the inability to grasp negation - such as failing to differentiate concepts like "pa…

cs.CL2024

Unleashing Multi-Hop Reasoning Potential in Large Language Models through Repetition of Misordered Context

Sangwon Yu, Ik-hwan Kim, Jongyoon Song +3

Multi-hop reasoning, which requires multi-step reasoning based on the supporting documents within a given context, remains challenging for large language models (LLMs). LLMs often…

cs.CL2024★ 2 cited

Correcting Negative Bias in Large Language Models through Negative Attention Score Alignment

Sangwon Yu, Jongyoon Song, Bongkyu Hwang +7

A binary decision task, like yes-no questions or answer verification, reflects a significant real-world scenario such as where users look for confirmation about the correctness of…

cs.CV2024

Interactive Text-to-Image Retrieval with Large Language Models: A Plug-and-Play Approach

Saehyung Lee, Sangwon Yu, Junsung Park +2

In this paper, we primarily address the issue of dialogue-form context query within the interactive text-to-image retrieval task. Our methodology, PlugIR, actively utilizes the gen…

cs.CL2024★ 1 cited

Large Language Models are Skeptics: False Negative Problem of Input-conflicting Hallucination

Jongyoon Song, Sangwon Yu, Sungroh Yoon

In this paper, we identify a new category of bias that induces input-conflicting hallucinations, where large language models (LLMs) generate responses inconsistent with the content…

cs.CL2023

Controlled Text Generation for Black-box Language Models via Score-based Progressive Editor

Sangwon Yu, Changmin Lee, Hojin Lee +1

Controlled text generation is very important for the practical use of language models because it ensures that the produced text includes only the desired attributes from a specific…

cs.CL2021

Rare Tokens Degenerate All Tokens: Improving Neural Text Generation via Adaptive Gradient Gating for Rare Token Embeddings

Sangwon Yu, Jongyoon Song, Heeseung Kim +3

Recent studies have determined that the learned token embeddings of large-scale neural language models are degenerated to be anisotropic with a narrow-cone shape. This phenomenon,…