7 papers
Learning from Mistakes: Negative Reasoning Samples Enhance Out-of-Domain Generalization
Xueyun Tian, Minghua Ma, Bingbing Xu +6
Supervised fine-tuning (SFT) on chain-of-thought (CoT) trajectories demonstrations is a common approach for enabling reasoning in large language models. Standard practices typicall…
EPD-Serve: A Flexible Multimodal EPD Disaggregation Inference Serving System On Ascend
Fan Bai, Pai Peng, Zhengzhi Tang +8
With the widespread adoption of large multimodal models, efficient inference across text, image, audio, and video modalities has become critical. However, existing multimodal infer…
Enhancing LLM Language Adaption through Cross-lingual In-Context Pre-training
Linjuan Wu, Haoran Wei, Huan Lin +4
Large language models (LLMs) exhibit remarkable multilingual capabilities despite English-dominated pre-training, attributed to cross-lingual mechanisms during pre-training. Existi…
InvestAlign: Overcoming Data Scarcity in Aligning Large Language Models with Investor Decision-Making Processes under Herd Behavior
Huisheng Wang, Zhuoshi Pan, Hangjing Zhang +3
Aligning Large Language Models (LLMs) with investor decision-making processes under herd behavior is a critical challenge in behavioral finance, which grapples with a fundamental l…
Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models
Yanzhao Zhang, Mingxin Li, Dingkun Long +9
In this work, we introduce the Qwen3 Embedding series, a significant advancement over its predecessor, the GTE-Qwen series, in text embedding and reranking capabilities, built upon…
Qwen3 Technical Report
An Yang, Anfeng Li, Baosong Yang +57
In this work, we present Qwen3, the latest version of the Qwen model family. Qwen3 comprises a series of large language models (LLMs) designed to advance performance, efficiency, a…