2 citations · 4 across the 4 of their papers we have counts for
6 papers
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…
Cluster-Guided Label Generation in Extreme Multi-Label Classification
Taehee Jung, Joo-Kyung Kim, Sungjin Lee +1
For extreme multi-label classification (XMC), existing classification-based models poorly perform for tail labels and often ignore the semantic relations among labels, like treatin…
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.…