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Found 26 papers, 11 papers with code
Interpreting Graph Neural Networks with In-Distributed Proxies
A popular paradigm for the explainability of GNNs is to identify explainable subgraphs by comparing their labels with the ones of original graphs.
Learning to Trust Your Feelings: Leveraging Self-awareness in LLMs for Hallucination Mitigation
We evaluate the ability of Large Language Models (LLMs) to discern and express their internal knowledge state, a key factor in countering factual hallucination and ensuring reliable application of LLMs.
Taiyi-Diffusion-XL: Advancing Bilingual Text-to-Image Generation with Large Vision-Language Model Support
Recent advancements in text-to-image models have significantly enhanced image generation capabilities, yet a notable gap of open-source models persists in bilingual or Chinese language support.
Lyrics: Boosting Fine-grained Language-Vision Alignment and Comprehension via Semantic-aware Visual Objects
During the instruction fine-tuning stage, we introduce semantic-aware visual feature extraction, a crucial method that enables the model to extract informative features from concrete visual objects.
Ranked #1 on
Image Captioning
on nocaps entire
iDesigner: A High-Resolution and Complex-Prompt Following Text-to-Image Diffusion Model for Interior Design
However, there are few specialized models in certain domains, such as interior design, which is attributed to the complex textual descriptions and detailed visual elements inherent in design, alongside the necessity for adaptable resolution.
ChiMed-GPT: A Chinese Medical Large Language Model with Full Training Regime and Better Alignment to Human Preferences
Recently, the increasing demand for superior medical services has highlighted the discrepancies in the medical infrastructure.
Ziya2: Data-centric Learning is All LLMs Need
Although many such issues are addressed along the line of research on LLMs, an important yet practical limitation is that many studies overly pursue enlarging model sizes without comprehensively analyzing and optimizing the use of pre-training data in their learning process, as well as appropriate organization and leveraging of such data in training LLMs under cost-effective settings.
Ziya-Visual: Bilingual Large Vision-Language Model via Multi-Task Instruction Tuning
Recent advancements enlarge the capabilities of large language models (LLMs) in zero-shot image-to-text generation and understanding by integrating multi-modal inputs.
Improving Facade Parsing with Vision Transformers and Line Integration
We introduce a new pipeline known as Revision-based Transformer Facade Parsing (RTFP).
MixupExplainer: Generalizing Explanations for Graph Neural Networks with Data Augmentation
Driven by the generalized GIB, we propose a graph mixup method, MixupExplainer, with a theoretical guarantee to resolve the distribution shifting issue.
RegExplainer: Generating Explanations for Graph Neural Networks in Regression Task
Graph regression is a fundamental task and has received increasing attention in a wide range of graph learning tasks.
UniEX: An Effective and Efficient Framework for Unified Information Extraction via a Span-extractive Perspective
We propose a new paradigm for universal information extraction (IE) that is compatible with any schema format and applicable to a list of IE tasks, such as named entity recognition, relation extraction, event extraction and sentiment analysis.
Orca: A Few-shot Benchmark for Chinese Conversational Machine Reading Comprehension
Thus, model's comprehension ability towards real scenarios are hard to evaluate reasonably.
TCBERT: A Technical Report for Chinese Topic Classification BERT
Bidirectional Encoder Representations from Transformers or BERT~\cite{devlin-etal-2019-bert} has been one of the base models for various NLP tasks due to its remarkable performance.
Solving Math Word Problems via Cooperative Reasoning induced Language Models
This inspires us to develop a cooperative reasoning-induced PLM for solving MWPs, called Cooperative Reasoning (CoRe), resulting in a human-like reasoning architecture with system 1 as the generator and system 2 as the verifier.
Ranked #103 on
Arithmetic Reasoning
on GSM8K
Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice Perspective
We propose a new paradigm for zero-shot learners that is format agnostic, i. e., it is compatible with any format and applicable to a list of language tasks, such as text classification, commonsense reasoning, coreference resolution, and sentiment analysis.
MAP: Multimodal Uncertainty-Aware Vision-Language Pre-training Model
Multimodal semantic understanding often has to deal with uncertainty, which means the obtained messages tend to refer to multiple targets.
Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence
We hope that this project will be the foundation of Chinese cognitive intelligence.
Unified BERT for Few-shot Natural Language Understanding
Even as pre-trained language models share a semantic encoder, natural language understanding suffers from a diversity of output schemas.
BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model
Furthermore, we conduct ablation studies on the pretraining tasks for BioBART and find that sentence permutation has negative effects on downstream tasks.
Ranked #3 on
Entity Linking
on MedMentions
Long Short-Term Sample Distillation
The long-term teacher draws on snapshots from several epochs ago in order to provide steadfast guidance and to guarantee teacher--student differences, while the short-term one yields more up-to-date cues with the goal of enabling higher-quality updates.
Accelerating Reinforcement Learning with a Directional-Gaussian-Smoothing Evolution Strategy
Evolution strategy (ES) has been shown great promise in many challenging reinforcement learning (RL) tasks, rivaling other state-of-the-art deep RL methods.
The Application of Two-level Attention Models in Deep Convolutional Neural Network for Fine-grained Image Classification
Our pipeline integrates three types of attention: the bottom-up attention that propose candidate patches, the object-level top-down attention that selects relevant patches to a certain object, and the part-level top-down attention that localizes discriminative parts.
Scale-Invariant Convolutional Neural Networks
Even though convolutional neural networks (CNN) has achieved near-human performance in various computer vision tasks, its ability to tolerate scale variations is limited.
Attentional Neural Network: Feature Selection Using Cognitive Feedback
Attentional Neural Network is a new framework that integrates top-down cognitive bias and bottom-up feature extraction in one coherent architecture.
