9 papers
Distilling Many-Shot In-Context Learning into a Cheat Sheet
Ukyo Honda, Soichiro Murakami, Peinan Zhang
Recent advances in large language models (LLMs) enable effective in-context learning (ICL) with many-shot examples, but at the cost of high computational demand due to longer input…
Annotation-Efficient Language Model Alignment via Diverse and Representative Response Texts
Yuu Jinnai, Ukyo Honda
Preference optimization is a standard approach to fine-tuning large language models to align with human preferences. The quantity, diversity, and representativeness of the preferen…
Exploring Explanations Improves the Robustness of In-Context Learning
Ukyo Honda, Tatsushi Oka
In-context learning (ICL) has emerged as a successful paradigm for leveraging large language models (LLMs). However, it often struggles to generalize beyond the distribution of the…
Exploring the Relationship Between Diversity and Quality in Ad Text Generation
Yoichi Aoki, Soichiro Murakami, Ukyo Honda +1
In natural language generation for advertising, creating diverse and engaging ad texts is crucial for capturing a broad audience and avoiding advertising fatigue. Regardless of the…
Not Eliminate but Aggregate: Post-Hoc Control over Mixture-of-Experts to Address Shortcut Shifts in Natural Language Understanding
Ukyo Honda, Tatsushi Oka, Peinan Zhang +1
Recent models for natural language understanding are inclined to exploit simple patterns in datasets, commonly known as shortcuts. These shortcuts hinge on spurious correlations be…
FaithCAMERA: Construction of a Faithful Dataset for Ad Text Generation
Akihiko Kato, Masato Mita, Soichiro Murakami +3
In ad text generation (ATG), desirable ad text is both faithful and informative. That is, it should be faithful to the input document, while at the same time containing important i…