Search Results for author:
Found 52 papers, 20 papers with code
Improving Embedding-based Large-scale Retrieval via Label Enhancement
Current embedding-based large-scale retrieval models are trained with 0-1 hard label that indicates whether a query is relevant to a document, ignoring rich information of the relevance degree.
Label Smoothing for Text Mining
This paper presents a novel keyword-based LS method to automatically generate soft labels from hard labels via exploiting the relevance between labels and text instances.
Stacking Networks Dynamically for Image Restoration Based on the Plug-and-Play Framework
Recently, stacked networks show powerful performance in Image Restoration, such as challenging motion deblurring problems.
Boosting Model Resilience via Implicit Adversarial Data Augmentation
This insight leads us to develop a meta-learning-based framework for optimizing classifiers with this novel loss, introducing the effects of augmentation while bypassing the explicit augmentation process.
FreeEval: A Modular Framework for Trustworthy and Efficient Evaluation of Large Language Models
The rapid development of large language model (LLM) evaluation methodologies and datasets has led to a profound challenge: integrating state-of-the-art evaluation techniques cost-effectively while ensuring reliability, reproducibility, and efficiency.
CodeShell Technical Report
Through this process, We have curated 100 billion high-quality pre-training data from GitHub.
NaturalSpeech 3: Zero-Shot Speech Synthesis with Factorized Codec and Diffusion Models
Specifically, 1) we design a neural codec with factorized vector quantization (FVQ) to disentangle speech waveform into subspaces of content, prosody, timbre, and acoustic details; 2) we propose a factorized diffusion model to generate attributes in each subspace following its corresponding prompt.
Hal-Eval: A Universal and Fine-grained Hallucination Evaluation Framework for Large Vision Language Models
Large Vision Language Models exhibit remarkable capabilities but struggle with hallucinations inconsistencies between images and their descriptions.
KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large Language Models
Automatic evaluation methods for large language models (LLMs) are hindered by data contamination, leading to inflated assessments of their effectiveness.
Efficient Vision-and-Language Pre-training with Text-Relevant Image Patch Selection
Vision Transformers (ViTs) have become increasingly popular in large-scale Vision and Language Pre-training (VLP) models.
When Parameter-efficient Tuning Meets General-purpose Vision-language Models
Instruction tuning has shown promising potential for developing general-purpose AI capabilities by using large-scale pre-trained models and boosts growing research to integrate multimodal information for creative applications.
Labels Need Prompts Too: Mask Matching for Natural Language Understanding Tasks
Textual label names (descriptions) are typically semantically rich in many natural language understanding (NLU) tasks.
TiMix: Text-aware Image Mixing for Effective Vision-Language Pre-training
Self-supervised Multi-modal Contrastive Learning (SMCL) remarkably advances modern Vision-Language Pre-training (VLP) models by aligning visual and linguistic modalities.
Hallucination Augmented Contrastive Learning for Multimodal Large Language Model
We first analyzed the representation distribution of textual and visual tokens in MLLM, revealing two important findings: 1) there is a significant gap between textual and visual representations, indicating unsatisfactory cross-modal representation alignment; 2) representations of texts that contain and do not contain hallucinations are entangled, making it challenging to distinguish them.
Ranked #78 on
Visual Question Answering
on MM-Vet
MusicAgent: An AI Agent for Music Understanding and Generation with Large Language Models
For developers and amateurs, it is very difficult to grasp all of these task to satisfy their requirements in music processing, especially considering the huge differences in the representations of music data and the model applicability across platforms among various tasks.
BUS:Efficient and Effective Vision-language Pre-training with Bottom-Up Patch Summarization
Specifically, We incorporate a Text-Semantics-Aware Patch Selector (TSPS) into the ViT backbone to perform a coarse-grained visual token extraction and then attach a flexible Transformer-based Patch Abstraction Decoder (PAD) upon the backbone for top-level visual abstraction.
EmoGen: Eliminating Subjective Bias in Emotional Music Generation
In this paper, we propose EmoGen, an emotional music generation system that leverages a set of emotion-related music attributes as the bridge between emotion and music, and divides the generation into two stages: emotion-to-attribute mapping with supervised clustering, and attribute-to-music generation with self-supervised learning.
PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization
To ensure the reliability of PandaLM, we collect a diverse human-annotated test dataset, where all contexts are generated by humans and labels are aligned with human preferences.
GETMusic: Generating Any Music Tracks with a Unified Representation and Diffusion Framework
Our proposed representation, coupled with the non-autoregressive generative model, empowers GETMusic to generate music with any arbitrary source-target track combinations.
3D Registration with Maximal Cliques
The key insight is to loosen the previous maximum clique constraint, and mine more local consensus information in a graph for accurate pose hypotheses generation: 1) A compatibility graph is constructed to render the affinity relationship between initial correspondences.
Exploiting Pseudo Image Captions for Multimodal Summarization
Cross-modal contrastive learning in vision language pretraining (VLP) faces the challenge of (partial) false negatives.
Vision Language Pre-training by Contrastive Learning with Cross-Modal Similarity Regulation
Cross-modal contrastive learning in vision language pretraining (VLP) faces the challenge of (partial) false negatives.
Evaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness
The capability of Large Language Models (LLMs) like ChatGPT to comprehend user intent and provide reasonable responses has made them extremely popular lately.
Exploring Vision-Language Models for Imbalanced Learning
However, their performance on imbalanced dataset is relatively poor, where the distribution of classes in the training dataset is skewed, leading to poor performance in predicting minority classes.
Hierarchical Prior Mining for Non-local Multi-View Stereo
3) A Hierarchical Prior Mining (HPM) framework, which is used to mine extensive non-local prior information at different scales to assist 3D model recovery, this strategy can achieve a considerable balance between the reconstruction of details and low-textured areas.
BUS: Efficient and Effective Vision-Language Pre-Training with Bottom-Up Patch Summarization.
In this paper, we propose a Bottom-Up Patch Summarization approach named BUS which is inspired by the Document Summarization Task in NLP to learn a concise visual summary of lengthy visual token sequences, guided by textual semantics.
Prototypical Mixing and Retrieval-Based Refinement for Label Noise-Resistant Image Retrieval
This paper investigates a realistic but understudied problem of image retrieval under label noise, which could lead to severe overfitting or memorization of noisy samples during optimization.
Reviewing Labels: Label Graph Network with Top-k Prediction Set for Relation Extraction
Specifically, for a given sample, we build a label graph to review candidate labels in the Top-k prediction set and learn the connections between them.
Ranked #1 on
Relation Extraction
on TACRED-Revisited
Sequence Generation with Label Augmentation for Relation Extraction
Sequence generation demonstrates promising performance in recent information extraction efforts, by incorporating large-scale pre-trained Seq2Seq models.
Ranked #1 on
Relation Extraction
on sciERC-sent
Museformer: Transformer with Fine- and Coarse-Grained Attention for Music Generation
A recent trend is to use Transformer or its variants in music generation, which is, however, suboptimal, because the full attention cannot efficiently model the typically long music sequences (e. g., over 10, 000 tokens), and the existing models have shortcomings in generating musical repetition structures.
Exploiting Hybrid Semantics of Relation Paths for Multi-hop Question Answering Over Knowledge Graphs
Answering natural language questions on knowledge graphs (KGQA) remains a great challenge in terms of understanding complex questions via multi-hop reasoning.
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.
Cross-document Event Identity via Dense Annotation
To this end, we design a new annotation workflow with careful quality control and an easy-to-use annotation interface.
A Survey on Machine Learning Techniques for Auto Labeling of Video, Audio, and Text Data
As a result, researchers started to focus on reducing data annotation and labeling costs.
Point, Disambiguate and Copy: Incorporating Bilingual Dictionaries for Neural Machine Translation
This paper proposes a sophisticated neural architecture to incorporate bilingual dictionaries into Neural Machine Translation (NMT) models.
Legal Judgment Prediction with Multi-Stage CaseRepresentation Learning in the Real Court Setting
Legal judgment prediction(LJP) is an essential task for legal AI.
Capturing Event Argument Interaction via A Bi-Directional Entity-Level Recurrent Decoder
Capturing interactions among event arguments is an essential step towards robust event argument extraction (EAE).
Unsupervised Out-of-Domain Detection via Pre-trained Transformers
Deployed real-world machine learning applications are often subject to uncontrolled and even potentially malicious inputs.
QuadrupletBERT: An Efficient Model For Embedding-Based Large-Scale Retrieval
The embedding-based large-scale query-document retrieval problem is a hot topic in the information retrieval (IR) field.
Multi-Hop Transformer for Document-Level Machine Translation
Document-level neural machine translation (NMT) has proven to be of profound value for its effectiveness on capturing contextual information.
Multi-view Inference for Relation Extraction with Uncertain Knowledge
Knowledge graphs (KGs) are widely used to facilitate relation extraction (RE) tasks.
Ranked #32 on
Relation Extraction
on DocRED
(using extra training data)
Exploiting Method Names to Improve Code Summarization: A Deliberation Multi-Task Learning Approach
Code summaries are brief natural language descriptions of source code pieces.
A Data-Centric Framework for Composable NLP Workflows
Empirical natural language processing (NLP) systems in application domains (e. g., healthcare, finance, education) involve interoperation among multiple components, ranging from data ingestion, human annotation, to text retrieval, analysis, generation, and visualization.
Expectation Synchronization Synthesis in Non-Markovian Open Quantum Systems
In this article, we investigate the problem of engineering synchronization in non-Markovian quantum systems.
Quantum Physics
FSV: Learning to Factorize Soft Value Function for Cooperative Multi-Agent Reinforcement Learning
We explore energy-based solutions for cooperative multi-agent reinforcement learning (MARL) using the idea of function factorization in centralized training with decentralized execution (CTDE).
Multi-agent Reinforcement Learning
reinforcement-learning
+3
SongMASS: Automatic Song Writing with Pre-training and Alignment Constraint
Automatic song writing aims to compose a song (lyric and/or melody) by machine, which is an interesting topic in both academia and industry.
Graph Enhanced Dual Attention Network for Document-Level Relation Extraction
Document-level relation extraction requires inter-sentence reasoning capabilities to capture local and global contextual information for multiple relational facts.
Leveraging Code Generation to Improve Code Retrieval and Summarization via Dual Learning
Since both tasks aim to model the association between natural language and programming language, recent studies have combined these two tasks to improve their performance.
Exploiting Entity BIO Tag Embeddings and Multi-task Learning for Relation Extraction with Imbalanced Data
In practical scenario, relation extraction needs to first identify entity pairs that have relation and then assign a correct relation class.
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.
Dual Subtitles as Parallel Corpora
In this paper, we leverage the existence of dual subtitles as a source of parallel data.
