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Found 25 papers, 10 papers with code
mixSeq: A Simple Data Augmentation Methodfor Neural Machine Translation
Data augmentation, which refers to manipulating the inputs (e. g., adding random noise, masking specific parts) to enlarge the dataset, has been widely adopted in machine learning.
WorldPM: Scaling Human Preference Modeling
Motivated by scaling laws in language modeling that demonstrate how test loss scales as a power law with model and dataset sizes, we find that similar laws exist in preference modeling.
CrossFormer: Cross-Segment Semantic Fusion for Document Segmentation
Text semantic segmentation involves partitioning a document into multiple paragraphs with continuous semantics based on the subject matter, contextual information, and document structure.
Qwen2.5-Omni Technical Report
In this framework, Thinker functions as a large language model tasked with text generation, while Talker is a dual-track autoregressive model that directly utilizes the hidden representations from the Thinker to produce audio tokens as output.
Ranked #3 on
Zero-Shot Video Question Answer
on EgoSchema (fullset)
(using extra training data)
AdEval: Alignment-based Dynamic Evaluation to Mitigate Data Contamination in Large Language Models
AdEval extracts key knowledge points and main ideas to align dynamically generated questions with static data's core concepts.
CodeElo: Benchmarking Competition-level Code Generation of LLMs with Human-comparable Elo Ratings
CodeElo benchmark is mainly based on the official CodeForces platform and tries to align with the platform as much as possible.
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 #7 on
on GPQA
Qwen2.5-Coder Technical Report
In this report, we introduce the Qwen2. 5-Coder series, a significant upgrade from its predecessor, CodeQwen1. 5.
Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
We present the Qwen2-VL Series, an advanced upgrade of the previous Qwen-VL models that redefines the conventional predetermined-resolution approach in visual processing.
Ranked #3 on
Video Question Answering
on TVBench
Natural Language Visual Grounding
Temporal Relation Extraction
+2
Qwen2 Technical Report
This report introduces the Qwen2 series, the latest addition to our large language models and large multimodal models.
Ranked #3 on
Arithmetic Reasoning
on GSM8K
(using extra training data)
Online Merging Optimizers for Boosting Rewards and Mitigating Tax in Alignment
Effectively aligning Large Language Models (LLMs) with human-centric values while preventing the degradation of abilities acquired through Pre-training and Supervised Fine-tuning (SFT) poses a central challenge in Reinforcement Learning from Human Feedback (RLHF).
TDeLTA: A Light-weight and Robust Table Detection Method based on Learning Text Arrangement
The diversity of tables makes table detection a great challenge, leading to existing models becoming more tedious and complex.
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
Structure-Unified M-Tree Coding Solver for MathWord Problem
As one of the challenging NLP tasks, designing math word problem (MWP) solvers has attracted increasing research attention for the past few years.
Tailoring Molecules for Protein Pockets: a Transformer-based Generative Solution for Structured-based Drug Design
Structure-based drug design is drawing growing attentions in computer-aided drug discovery.
Discovering Drug-Target Interaction Knowledge from Biomedical Literature
We regard the DTI triplets as a sequence and use a Transformer-based model to directly generate them without using the detailed annotations of entities and relations.
Contextual Domain Classification with Temporal Representations
In commercial dialogue systems, the Spoken Language Understanding (SLU) component tends to have numerous domains thus context is needed to help resolve ambiguities.
CN-HIT-IT.NLP at SemEval-2020 Task 4: Enhanced Language Representation with Multiple Knowledge Triples
For this task, it is obvious that external knowledge, such as Knowledge graph, can help the model understand commonsense in natural language statements.
Learning to Reweight with Deep Interactions
Recently, the concept of teaching has been introduced into machine learning, in which a teacher model is used to guide the training of a student model (which will be used in real tasks) through data selection, loss function design, etc.
Multi-branch Attentive Transformer
While the multi-branch architecture is one of the key ingredients to the success of computer vision tasks, it has not been well investigated in natural language processing, especially sequence learning tasks.
Ranked #4 on
Machine Translation
on WMT2014 English-German
(SacreBLEU metric)
Microsoft Research Asia's Systems for WMT19
We Microsoft Research Asia made submissions to 11 language directions in the WMT19 news translation tasks.
Learning to Teach with Dynamic Loss Functions
Different from typical learning settings in which the loss function of a machine learning model is predefined and fixed, in our framework, the loss function of a machine learning model (we call it student) is defined by another machine learning model (we call it teacher).
Learning to Teach
Teaching plays a very important role in our society, by spreading human knowledge and educating our next generations.
