4 papers
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…
Can You Trick the Grader? Adversarial Persuasion of LLM Judges
Yerin Hwang, Dongryeol Lee, Taegwan Kang +2
As large language models take on growing roles as automated evaluators in practical settings, a critical question arises: Can individuals persuade an LLM judge to assign unfairly h…
Fooling the LVLM Judges: Visual Biases in LVLM-Based Evaluation
Yerin Hwang, Dongryeol Lee, Kyungmin Min +3
Recently, large vision-language models (LVLMs) have emerged as the preferred tools for judging text-image alignment, yet their robustness along the visual modality remains underexp…
LLMs can be easily Confused by Instructional Distractions
Yerin Hwang, Yongil Kim, Jahyun Koo +3
Despite the fact that large language models (LLMs) show exceptional skill in instruction following tasks, this strength can turn into a vulnerability when the models are required t…