5 papers · 1 filter
Evolution Fine-Tuning: Learning to Discover Across 371 Optimization Tasks
Young-Jun Lee, Seungone Kim, Minki Kang +5
Would experience designing faster GPU kernels also help close in on a long-standing open mathematical conjecture? Large Language Models (LLMs) integrated into evolutionary search h…
When Thoughts Meet Facts: Reusable Reasoning for Long-Context LMs
Soyeong Jeong, Taehee Jung, Sung Ju Hwang +2
Recent Long-Context Language Models (LCLMs) can process hundreds of thousands of tokens in a single prompt, enabling new opportunities for knowledge-intensive multi-hop reasoning b…
Chain-of-Instructions: Compositional Instruction Tuning on Large Language Models
Shirley Anugrah Hayati, Taehee Jung, Tristan Bodding-Long +4
Fine-tuning large language models (LLMs) with a collection of large and diverse instructions has improved the model's generalization to different tasks, even for unseen tasks. Howe…
Posterior Calibrated Training on Sentence Classification Tasks
Taehee Jung, Dongyeop Kang, Hua Cheng +2
Most classification models work by first predicting a posterior probability distribution over all classes and then selecting that class with the largest estimated probability. In m…
Earlier Isn't Always Better: Sub-aspect Analysis on Corpus and System Biases in Summarization
Taehee Jung, Dongyeop Kang, Lucas Mentch +1
Despite the recent developments on neural summarization systems, the underlying logic behind the improvements from the systems and its corpus-dependency remains largely unexplored.…