5 papers
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…
TRUEBench: Can LLM Response Meet Real-world Constraints as Productivity Assistant?
Jiho Park, Jongyoon Song, Minjin Choi +3
Large language models (LLMs) are increasingly integral as productivity assistants, but existing benchmarks fall short in rigorously evaluating their real-world instruction-followin…
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…
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…
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…