3 papers
cs.CL2025
Extending the Context of Pretrained LLMs by Dropping Their Positional Embeddings
Yoav Gelberg, Koshi Eguchi, Takuya Akiba +1
So far, expensive finetuning beyond the pretraining sequence length has been a requirement for effectively extending the context of language models (LM). In this work, we break thi…
cs.AI2025
String Seed of Thought: Prompting LLMs for Distribution-Faithful and Diverse Generation
Kou Misaki, Takuya Akiba
We introduce String Seed of Thought (SSoT), a novel prompting method for LLMs that improves Probabilistic Instruction Following (PIF). We define PIF as a task requiring an LLM to s…
cs.AI2025
ALE-Bench: A Benchmark for Long-Horizon Objective-Driven Algorithm Engineering
Yuki Imajuku, Kohki Horie, Yoichi Iwata +3
How well do AI systems perform in algorithm engineering for hard optimization problems in domains such as package-delivery routing, crew scheduling, factory production planning, an…