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Found 10 papers, 6 papers with code
Unsupervised Preference-Aware Language Identification
Recognizing the language of ambiguous texts has become a main challenge in language identification (LID).
Qwen2.5 Technical Report
In addition, for hosted solutions, the proprietary models currently include two mixture-of-experts (MoE) variants: Qwen2. 5-Turbo and Qwen2. 5-Plus, both available from Alibaba Cloud Model Studio.
Ranked #4 on
Mathematical Reasoning
on AIME24
Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement
This RM is then applied to the iterative evolution of data in supervised fine-tuning (SFT).
Ranked #2 on
Math Word Problem Solving
on MATH
(using extra training data)
Qwen2.5-Coder Technical Report
In this report, we introduce the Qwen2. 5-Coder series, a significant upgrade from its predecessor, CodeQwen1. 5.
Qwen2 Technical Report
This report introduces the Qwen2 series, the latest addition to our large language models and large multimodal models.
Ranked #1 on
Arithmetic Reasoning
on GSM8K
(using extra training data)
An Empirical Study of Parameter Efficient Fine-tuning on Vision-Language Pre-train Model
A natural expectation for PEFTs is that the performance of various PEFTs is positively related to the data size and fine-tunable parameter size.
Qwen Technical Report
Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to humans.
Ranked #3 on
Multi-Label Text Classification
on CC3M-TagMask
PolyLM: An Open Source Polyglot Large Language Model
Large language models (LLMs) demonstrate remarkable ability to comprehend, reason, and generate following nature language instructions.
Frequency-Aware Contrastive Learning for Neural Machine Translation
Inspired by the observation that low-frequency words form a more compact embedding space, we tackle this challenge from a representation learning perspective.
Deep Dynamic Boosted Forest
Specically, we propose to measure the quality of each leaf node of every decision tree in the random forest to determine hard examples.
