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Found 20 papers, 7 papers with code
KiPT: Knowledge-injected Prompt Tuning for Event Detection
Then, knowledge-injected prompts are constructed using external knowledge bases, and a prompt tuning strategy is leveraged to optimize the prompts.
DESED: Dialogue-based Explanation for Sentence-level Event Detection
Many recent sentence-level event detection efforts focus on enriching sentence semantics, e. g., via multi-task or prompt-based learning.
GuiLoMo: Allocating Expert Number and Rank for LoRA-MoE via Bilevel Optimization with GuidedSelection Vectors
Parameter-efficient fine-tuning (PEFT) methods, particularly Low-Rank Adaptation (LoRA), offer an efficient way to adapt large language models with reduced computational costs.
Qwen Look Again: Guiding Vision-Language Reasoning Models to Re-attention Visual Information
Inference time scaling drives extended reasoning to enhance the performance of Vision-Language Models (VLMs), thus forming powerful Vision-Language Reasoning Models (VLRMs).
Domaino1s: Guiding LLM Reasoning for Explainable Answers in High-Stakes Domains
However, current LLMs for high-stakes domain tasks, such as financial investment and legal QA, typically generate brief answers without reasoning processes and explanations.
GraphSOS: Graph Sampling and Order Selection to Help LLMs Understand Graphs Better
The success of Large Language Models (LLMs) in various domains has led researchers to apply them to graph-related problems by converting graph data into natural language text.
Mitigating Hallucinations on Object Attributes using Multiview Images and Negative Instructions
Consequently, we have devised Multiview Image Augmented VLM (MIAVLM), incorporating a Multiview Attributes Perceiver (MAP) submodule capable of simultaneously eliminating the influence of input image order and aligning visual information from multiview images with Large Language Models (LLMs).
Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning
Dynamic graph augmentation is used to improve the performance of dynamic GNNs.
Order Matters: Exploring Order Sensitivity in Multimodal Large Language Models
Furthermore, we demonstrate that popular MLLMs pay special attention to certain multimodal context positions, particularly the beginning and end.
Aeroengine performance prediction using a physical-embedded data-driven method
Accurate and efficient prediction of aeroengine performance is of paramount importance for engine design, maintenance, and optimization endeavours.
Supportiveness-based Knowledge Rewriting for Retrieval-augmented Language Modeling
Retrieval-augmented language models (RALMs) have recently shown great potential in mitigating the limitations of implicit knowledge in LLMs, such as untimely updating of the latest expertise and unreliable retention of long-tail knowledge.
Exploiting Pseudo Future Contexts for Emotion Recognition in Conversations
Specifically, for an utterance, we generate its future context with pre-trained language models, potentially containing extra beneficial knowledge in a conversational form homogeneous with the historical ones.
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.
Eliciting Knowledge from Pretrained Language Models for Prototypical Prompt Verbalizer
The distances between the embedding at the masked position of input and prototypical embeddings are used as classification criterion.
Encoder-Decoder Neural Architecture Optimization for Keyword Spotting
Keyword spotting aims to identify specific keyword audio utterances.
Enhancing Neural Models with Vulnerability via Adversarial Attack
2) There is no previous work considering adversarial attack to improve the performance of NLSM tasks.
Neural Architecture Search For Keyword Spotting
Deep neural networks have recently become a popular solution to keyword spotting systems, which enable the control of smart devices via voice.
Ranked #1 on
Keyword Spotting
on Google Speech Commands
(Google Speech Commands V1 6 metric)
What Do Questions Exactly Ask? MFAE: Duplicate Question Identification with Multi-Fusion Asking Emphasis
We firstly propose to explore the asking emphasis of a question as a key factor in DQI.
Ranked #1 on
Community Question Answering
on Quora Question Pairs
Review of Deep Learning
In recent years, China, the United States and other countries, Google and other high-tech companies have increased investment in artificial intelligence.
An influence-based fast preceding questionnaire model for elderly assessments
The practice in an elderly-care company shows that the FPQM can reduce the number of attributes by 90. 56% with a prediction accuracy of 98. 39%.
