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20202026
most citedCan LLMs Recognize Toxicity? A Structured Investigation Framework and Toxicity Metric

2 citations · 3 across the 14 of their papers we have counts for

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cs.CL2026

K-EXAONE 2.0 Technical Report

Eunbi Choi, Kibong Choi, Sehyun Chun +74

This technical report presents K-EXAONE 2.0, an open-weight multilingual foundation model developed by LG AI Research as a step in our effort toward global frontier-scale foundatio…

cs.CV2026

OPRO: Orthogonal Panel-Relative Operators for Panel-Aware In-Context Image Generation

Sanghyeon Lee, Minwoo Lee, Euijin Shin +3

We introduce a parameter-efficient adaptation method for panel-aware in-context image generation with pre-trained diffusion transformers. The key idea is to compose learnable, pane…

cs.SD2026

SNAP: Speaker Nulling for Artifact Projection in Speech Deepfake Detection

Kyudan Jung, Jihwan Kim, Minwoo Lee +4

Recent advancements in text-to-speech technologies enable generating high-fidelity synthetic speech nearly indistinguishable from real human voices. While recent studies show the e…

cs.CL2026

When Wording Steers the Evaluation: Framing Bias in LLM judges

Yerin Hwang, Dongryeol Lee, Taegwan Kang +2

Large language models (LLMs) are known to produce varying responses depending on prompt phrasing, indicating that subtle guidance in phrasing can steer their answers. However, the…

cs.CL2026

Judging Against the Reference: Uncovering Knowledge-Driven Failures in LLM-Judges on QA Evaluation

Dongryeol Lee, Yerin Hwang, Taegwan Kang +3

While large language models (LLMs) are increasingly used as automatic judges for question answering (QA) and other reference-conditioned evaluation tasks, little is known about the…