Search Results for author:
Found 17 papers, 9 papers with code
ChinaTravel: A Real-World Benchmark for Language Agents in Chinese Travel Planning
Recent advances in LLMs, particularly in language reasoning and tool integration, have rapidly sparked the real-world development of Language Agents.
Predictable Emergent Abilities of LLMs: Proxy Tasks Are All You Need
While scaling laws optimize training configurations for large language models (LLMs) through experiments on smaller or early-stage models, they fail to predict emergent abilities due to the absence of such capabilities in these models.
Infinity-MM: Scaling Multimodal Performance with Large-Scale and High-Quality Instruction Data
Despite the availability of several open-source multimodal datasets, limitations in the scale and quality of open-source instruction data hinder the performance of VLMs trained on these datasets, leading to a significant gap compared to models trained on closed-source data.
Ranked #4 on
Image Generation
on TextAtlasEval
CCI3.0-HQ: a large-scale Chinese dataset of high quality designed for pre-training large language models
We present CCI3. 0-HQ (https://huggingface. co/datasets/BAAI/CCI3-HQ), a high-quality 500GB subset of the Chinese Corpora Internet 3. 0 (CCI3. 0)(https://huggingface. co/datasets/BAAI/CCI3-Data), developed using a novel two-stage hybrid filtering pipeline that significantly enhances data quality.
Aquila2 Technical Report
This paper introduces the Aquila2 series, which comprises a wide range of bilingual models with parameter sizes of 7, 34, and 70 billion.
AquilaMoE: Efficient Training for MoE Models with Scale-Up and Scale-Out Strategies
In this paper, we present AquilaMoE, a cutting-edge bilingual 8*16B Mixture of Experts (MoE) language model that has 8 experts with 16 billion parameters each and is developed using an innovative training methodology called EfficientScale.
TACO: Topics in Algorithmic COde generation dataset
Moreover, each TACO problem includes several fine-grained labels such as task topics, algorithms, programming skills, and difficulty levels, providing a more precise reference for the training and evaluation of code generation models.
Ranked #1 on
Code Generation
on TACO-Code
CoIE: Chain-of-Instruct Editing for Multi-Attribute Face Manipulation
Current text-to-image editing models often encounter challenges with smoothly manipulating multiple attributes using a single instruction.
AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities
In this work, we present a conceptually simple and effective method to train a strong bilingual/multilingual multimodal representation model.
Gaze Preserving CycleGANs for Eyeglass Removal & Persistent Gaze Estimation
GPCycleGAN is based on the well-known CycleGAN approach - with the addition of a gaze classifier and a gaze consistency loss for additional supervision.
Effectiveness of random deep feature selection for securing image manipulation detectors against adversarial examples
We investigate if the random feature selection approach proposed in [1] to improve the robustness of forensic detectors to targeted attacks, can be extended to detectors based on deep learning features.
Effective Domain Knowledge Transfer with Soft Fine-tuning
However, the datasets from source domain are simply discarded in the fine-tuning process.
Topic Augmented Generator for Abstractive Summarization
In this paper, we propose a new decoder where the output summary is generated by conditioning on both the input text and the latent topics of the document.
A Probabilistic Model for Joint Learning of Word Embeddings from Texts and Images
Our approach learns textual and visual representations jointly: latent visual factors couple together a skip-gram model for co-occurrence in linguistic data and a generative latent variable model for visual data.
A Multi-strategy Query Processing Approach for Biomedical Question Answering: USTB\_PRIR at BioASQ 2017 Task 5B
This paper describes the participation of USTB{\_}PRIR team in the 2017 BioASQ 5B on question answering, including document retrieval, snippet retrieval, and concept retrieval task.
Weakly Supervised PatchNets: Describing and Aggregating Local Patches for Scene Recognition
In this paper, we propose a hybrid representation, which leverages the discriminative capacity of CNNs and the simplicity of descriptor encoding schema for image recognition, with a focus on scene recognition.
CUHK & ETHZ & SIAT Submission to ActivityNet Challenge 2016
This paper presents the method that underlies our submission to the untrimmed video classification task of ActivityNet Challenge 2016.
