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Found 45 papers, 18 papers with code
Learn to Copy from the Copying History: Correlational Copy Network for Abstractive Summarization
It thereby takes advantage of prior copying distributions and, at each time step, explicitly encourages the model to copy the input word that is relevant to the previously copied one.
Ranked #11 on
Abstractive Text Summarization
on CNN / Daily Mail
(using extra training data)
Advancing LLM Reasoning Generalists with Preference Trees
We introduce Eurus, a suite of large language models (LLMs) optimized for reasoning.
LAKE-RED: Camouflaged Images Generation by Latent Background Knowledge Retrieval-Augmented Diffusion
Camouflaged vision perception is an important vision task with numerous practical applications.
On Large Language Models' Hallucination with Regard to Known Facts
In hallucinated cases, the output token's information rarely demonstrates abrupt increases and consistent superiority in the later stages of the model.
Mastering Text, Code and Math Simultaneously via Fusing Highly Specialized Language Models
Underlying data distributions of natural language, programming code, and mathematical symbols vary vastly, presenting a complex challenge for large language models (LLMs) that strive to achieve high performance across all three domains simultaneously.
Contrastive Augmented Graph2Graph Memory Interaction for Few Shot Continual Learning
However, current EM-based methods retrieves memory globally by performing Vector-to-Vector (V2V) interaction between features corresponding to the input and prototypes stored in EM, neglecting the geometric structure of local features.
CoGenesis: A Framework Collaborating Large and Small Language Models for Secure Context-Aware Instruction Following
With the advancement of language models (LMs), their exposure to private data is increasingly inevitable, and their deployment (especially for smaller ones) on personal devices, such as PCs and smartphones, has become a prevailing trend.
Interactive Continual Learning: Fast and Slow Thinking
Drawing on Complementary Learning System theory, this paper presents a novel Interactive Continual Learning (ICL) framework, enabled by collaborative interactions among models of various sizes.
Investigating Deep Watermark Security: An Adversarial Transferability Perspective
The rise of generative neural networks has triggered an increased demand for intellectual property (IP) protection in generated content.
Critical Data Size of Language Models from a Grokking Perspective
We formalize the phase transition under the grokking configuration into the Data Efficiency Hypothesis and identify data insufficiency, sufficiency, and surplus regimes in language models training dynamics.
Generative Multi-Modal Knowledge Retrieval with Large Language Models
Knowledge retrieval with multi-modal queries plays a crucial role in supporting knowledge-intensive multi-modal applications.
AdapEdit: Spatio-Temporal Guided Adaptive Editing Algorithm for Text-Based Continuity-Sensitive Image Editing
With the great success of text-conditioned diffusion models in creative text-to-image generation, various text-driven image editing approaches have attracted the attentions of many researchers.
LMD: Faster Image Reconstruction with Latent Masking Diffusion
Then, we combine the advantages of MAEs and DPMs to design a progressive masking diffusion model, which gradually increases the masking proportion by three different schedulers and reconstructs the latent features from simple to difficult, without sequentially performing denoising diffusion as in DPMs or using fixed high masking ratio as in MAEs, so as to alleviate the high training time-consumption predicament.
Large Model Based Referring Camouflaged Object Detection
Our motivation is to make full use of the semantic intelligence and intrinsic knowledge of recent Multimodal Large Language Models (MLLMs) to decompose this complex task in a human-like way.
Sparse Low-rank Adaptation of Pre-trained Language Models
Recognizing the need for more flexible adaptation, we extend the methodology of LoRA to an innovative approach we call sparse low-rank adaptation (SoRA) that enables dynamic adjustments to the intrinsic rank during the adaptation process.
Large Language Models are Zero Shot Hypothesis Proposers
We subsequently evaluate the hypothesis generation capabilities of various top-tier instructed models in zero-shot, few-shot, and fine-tuning settings, including both closed and open-source LLMs.
CRaSh: Clustering, Removing, and Sharing Enhance Fine-tuning without Full Large Language Model
Instruction tuning has recently been recognized as an effective way of aligning Large Language Models (LLMs) to enhance their generalization ability across various tasks.
When Super-Resolution Meets Camouflaged Object Detection: A Comparison Study
Super Resolution (SR) and Camouflaged Object Detection (COD) are two hot topics in computer vision with various joint applications.
Enhancing Chat Language Models by Scaling High-quality Instructional Conversations
Fine-tuning on instruction data has been widely validated as an effective practice for implementing chat language models like ChatGPT.
PaD: Program-aided Distillation Can Teach Small Models Reasoning Better than Chain-of-thought Fine-tuning
While large language models (LLMs) excel in various natural language processing tasks, their huge size and the inaccessibility of parameters present challenges for practical deployment.
SAM Struggles in Concealed Scenes -- Empirical Study on "Segment Anything"
Segmenting anything is a ground-breaking step toward artificial general intelligence, and the Segment Anything Model (SAM) greatly fosters the foundation models for computer vision.
PetsGAN: Rethinking Priors for Single Image Generation
Moreover, we apply our method to other image manipulation tasks (e. g., style transfer, harmonization), and the results further prove the effectiveness and efficiency of our method.
On the Convergence and Robustness of Adversarial Training
In this paper, we propose such a criterion, namely First-Order Stationary Condition for constrained optimization (FOSC), to quantitatively evaluate the convergence quality of adversarial examples found in the inner maximization.
Trustworthy AI: From Principles to Practices
In this review, we provide AI practitioners with a comprehensive guide for building trustworthy AI systems.
The JDDC 2.0 Corpus: A Large-Scale Multimodal Multi-Turn Chinese Dialogue Dataset for E-commerce Customer Service
We present the solutions of top-5 teams participating in the JDDC multimodal dialogue challenge based on this dataset, which provides valuable insights for further researches on the multimodal dialogue task.
RoR: Read-over-Read for Long Document Machine Reading Comprehension
To address this problem, we propose RoR, a read-over-read method, which expands the reading field from chunk to document.
EviDR: Evidence-Emphasized Discrete Reasoning for Reasoning Machine Reading Comprehension
Reasoning machine reading comprehension (R-MRC) aims to answer complex questions that require discrete reasoning based on text.
CUSTOM: Aspect-Oriented Product Summarization for E-Commerce
To address the problem, we propose CUSTOM, aspect-oriented product summarization for e-commerce, which generates diverse and controllable summaries towards different product aspects.
Augmentation Pathways Network for Visual Recognition
Experimental results on ImageNet demonstrate the compatibility and effectiveness on a much wider range of augmentations, while consuming fewer parameters and lower computational costs at inference time.
Open Temporal Relation Extraction for Question Answering
Understanding the temporal relations among events in text is a critical aspect of reading comprehension, which can be evaluated in the form of temporal question answering (TQA).
Joint System-Wise Optimization for Pipeline Goal-Oriented Dialog System
Recent work (Takanobu et al., 2020) proposed the system-wise evaluation on dialog systems and found that improvement on individual components (e. g., NLU, policy) in prior work may not necessarily bring benefit to pipeline systems in system-wise evaluation.
Conversational AI Systems for Social Good: Opportunities and Challenges
Conversational artificial intelligence (ConvAI) systems have attracted much academic and commercial attention recently, making significant progress on both fronts.
SGG: Learning to Select, Guide, and Generate for Keyphrase Generation
Keyphrases, that concisely summarize the high-level topics discussed in a document, can be categorized into present keyphrase which explicitly appears in the source text, and absent keyphrase which does not match any contiguous subsequence but is highly semantically related to the source.
K-PLUG: Knowledge-injected Pre-trained Language Model for Natural Language Understanding and Generation in E-Commerce
K-PLUG achieves new state-of-the-art results on a suite of domain-specific NLP tasks, including product knowledge base completion, abstractive product summarization, and multi-turn dialogue, significantly outperforms baselines across the board, which demonstrates that the proposed method effectively learns a diverse set of domain-specific knowledge for both language understanding and generation tasks.
Graph Ensemble Learning over Multiple Dependency Trees for Aspect-level Sentiment Classification
Recent work on aspect-level sentiment classification has demonstrated the efficacy of incorporating syntactic structures such as dependency trees with graph neural networks(GNN), but these approaches are usually vulnerable to parsing errors.
HAIR: Head-mounted AR Intention Recognition
In this paper we propose an intention recognition system that is based purely on a portable head-mounted display.
Intent Detection
Robotics
Human-Computer Interaction
Conversational Query Rewriting with Self-supervised Learning
Conversational Query Rewriting (CQR) aims to simplify the multi-turn dialogue modeling into a single-turn problem by explicitly rewriting the conversational query into a self-contained utterance.
K-PLUG: KNOWLEDGE-INJECTED PRE-TRAINED LANGUAGE MODEL FOR NATURAL LANGUAGE UNDERSTANDING AND GENERATION
K-PLUG achieves new state-of-the-art results on a suite of domain-specific NLP tasks, including product knowledge base completion, abstractive product summarization, and multi-turn dialogue, significantly outperforms baselines across the board, which demonstrates that the proposed method effectively learns a diverse set of domain-specific knowledge for both language understanding and generation tasks.
On the Faithfulness for E-commerce Product Summarization
In this work, we present a model to generate e-commerce product summaries.
Improving Prosody Modelling with Cross-Utterance BERT Embeddings for End-to-end Speech Synthesis
Despite prosody is related to the linguistic information up to the discourse structure, most text-to-speech (TTS) systems only take into account that within each sentence, which makes it challenging when converting a paragraph of texts into natural and expressive speech.
Learning to Decouple Relations: Few-Shot Relation Classification with Entity-Guided Attention and Confusion-Aware Training
This paper aims to enhance the few-shot relation classification especially for sentences that jointly describe multiple relations.
Neural Kalman Filtering for Speech Enhancement
Statistical signal processing based speech enhancement methods adopt expert knowledge to design the statistical models and linear filters, which is complementary to the deep neural network (DNN) based methods which are data-driven.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Incremental Learning for End-to-End Automatic Speech Recognition
In this paper, we propose an incremental learning method for end-to-end Automatic Speech Recognition (ASR) which enables an ASR system to perform well on new tasks while maintaining the performance on its originally learned ones.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Selective Attention Based Graph Convolutional Networks for Aspect-Level Sentiment Classification
Aspect-level sentiment classification aims to identify the sentiment polarity towards a specific aspect term in a sentence.
Finding Needle in a Million Metrics: Anomaly Detection in a Large-scale Computational Advertising Platform
Online media offers opportunities to marketers to deliver brand messages to a large audience.
