5 papers
Mitigating Scoring Bias in LLM-as-a-Judge via Random Number Generation
Yuma Asato, Kiyoaki Shirai, Natthawut Kertkeidkachorn
Large Language Models (LLMs) are often used as evaluators of text quality, known as LLM-as-a-Judge, which can outperform conventional automatic evaluation metrics that rely on refe…
Attention-Guided Layer Selection for Contrastive Decoding in Large Language Models
Yusuke Sakai, Natthawut Kertkeidkachorn, Kiyoaki Shirai
Contrastive decoding methods such as DoLa improve the factuality of Large Language Models (LLMs) by contrasting the output distributions of mature and premature layers. However, Do…
Improving Interpretability of Lexical Semantic Change with Neurobiological Features
Kohei Oda, Hiroya Takamura, Kiyoaki Shirai +1
Lexical Semantic Change (LSC) is the phenomenon in which the meaning of a word change over time. Most studies on LSC focus on improving the performance of estimating the degree of…
One Sentence, Two Embeddings: Contrastive Learning of Explicit and Implicit Semantic Representations
Kohei Oda, Po-Min Chuang, Kiyoaki Shirai +1
Sentence embedding methods have made remarkable progress, yet they still struggle to capture the implicit semantics within sentences. This can be attributed to the inherent limitat…
Discovering Highly Influential Shortcut Reasoning: An Automated Template-Free Approach
Daichi Haraguchi, Kiyoaki Shirai, Naoya Inoue +1
Shortcut reasoning is an irrational process of inference, which degrades the robustness of an NLP model. While a number of previous work has tackled the identification of shortcut…