14 papers
Orienteering Problem with Uncertain Time-Varying Rewards: Framework and Benchmark for Everyday Service Robotics
Masafumi Endo, Kohei Honda, Yuu Jinnai +1
We present the orienteering problem with uncertain time-varying rewards (OP-UTVR), a novel variant of the orienteering problem (OP). While most existing OP formulations assume rewa…
JOR-Bench: Japanese Operations Research Benchmarks for Large Language Models
Yuu Jinnai
We present JOR-Bench, a collection of five Japanese-language benchmarks for evaluating the ability of large language models (LLMs) to formulate and solve operations research (OR) p…
Cost of Reasoning in non-English Languages: A Case Study on Japanese
Yuu Jinnai
Reasoning Language Models (RLMs) achieve their strongest performance when they reason in English, the language for which reasoning-oriented training data is most abundant. However,…
CAT-Translate: Building Compact Open-Source Models for Japanese-English Translation
Yuu Jinnai
Nowadays, large multilingual translation models demonstrate impressive translation capabilities in the machine translation benchmarks. This raises a practical question to the devel…
Re-evaluating Minimum Bayes Risk Decoding for Automatic Speech Recognition
Yuu Jinnai
Recent work has shown that sample-based Minimum Bayes Risk (MBR) decoding outperforms beam search in text-to-text generation tasks, such as machine translation, text summarization,…
Consensus Group Relative Policy Optimization for Text Generation
Yuki Ichihara, Yuu Jinnai, Kaito Ariu +1
Many strong decoding methods for text generation follow a sample-and-rerank paradigm: they draw multiple candidates, score each under a utility (reward) function using consensus ac…