5 papers
Formalize, Don't Optimize: The Heuristic Trap in LLM-Generated Combinatorial Solvers
Haoyu Wang, Yuliang Song, Tao Li +5
Large Language Models (LLMs) struggle to solve complex combinatorial problems through direct reasoning, so recent neuro-symbolic systems increasingly use them to synthesize executa…
Evaluating Large Language Models in Crisis Detection: A Real-World Benchmark from Psychological Support Hotlines
Guifeng Deng, Shuyin Rao, Tianyu Lin +9
Psychological support hotlines serve as critical lifelines for crisis intervention but encounter significant challenges due to rising demand and limited resources. Large language m…
Seed-X: Building Strong Multilingual Translation LLM with 7B Parameters
Shanbo Cheng, Yu Bao, Qian Cao +23
Multilingual translation stands as a challenging task for large language models (LLMs) to handle intricate language patterns and stilted translations that arise in automated transl…
DuPO: Enabling Reliable LLM Self-Verification via Dual Preference Optimization
Shuaijie She, Yu Bao, Yu Lu +7
We present DuPO, a dual learning-based preference optimization framework that generates annotation-free feedback via a generalized duality. DuPO addresses two key limitations: Rein…
PromptIntern: Saving Inference Costs by Internalizing Recurrent Prompt during Large Language Model Fine-tuning
Jiaru Zou, Mengyu Zhou, Tao Li +2
Recent advances in fine-tuning large language models (LLMs) have greatly enhanced their usage in domain-specific tasks. Despite the success, fine-tuning continues to rely on repeat…