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Found 25 papers, 11 papers with code
LangGPT: Rethinking Structured Reusable Prompt Design Framework for LLMs from the Programming Language
We have built a community on LangGPT to facilitate the tuition and sharing of prompt design.
Is Mamba Effective for Time Series Forecasting?
Furthermore, we conduct extensive experiments to delve deeper into the potential of Mamba compared to the Transformer in the TSF.
Multimodal Sentiment Detection Based on Multi-channel Graph Neural Networks
In this paper, we propose Multi-channel Graph Neural Networks with Sentiment-awareness (MGNNS) for image-text sentiment detection.
MM-BigBench: Evaluating Multimodal Models on Multimodal Content Comprehension Tasks
Consequently, our work complements research on the performance of MLLMs in multimodal comprehension tasks, achieving a more comprehensive and holistic evaluation of MLLMs.
Few-shot Multimodal Sentiment Analysis based on Multimodal Probabilistic Fusion Prompts
To tackle this problem, we propose a novel method called Multimodal Probabilistic Fusion Prompts (MultiPoint) that leverages diverse cues from different modalities for multimodal sentiment detection in the few-shot scenario.
STICKERCONV: Generating Multimodal Empathetic Responses from Scratch
Stickers, while widely recognized for enhancing empathetic communication in online interactions, remain underexplored in current empathetic dialogue research, notably due to the challenge of a lack of comprehensive datasets.
KC-ISA: An Implicit Sentiment Analysis Model Combining Knowledge Enhancement and Context Features
Moreover, to verify the generality of the model, we also conduct experiments on two common sentiment analysis datasets.
MulZDG: Multilingual Code-Switching Framework for Zero-shot Dialogue Generation
Building dialogue generation systems in a zero-shot scenario remains a huge challenge, since the typical zero-shot approaches in dialogue generation rely heavily on large-scale pre-trained language generation models such as GPT-3 and T5.
Alleviating Sparsity of Open Knowledge Graphs with Ternary Contrastive Learning
Sparsity of formal knowledge and roughness of non-ontological construction make sparsity problem particularly prominent in Open Knowledge Graphs (OpenKGs).
RoCar: A Relationship Network-based Evaluation Method to Large Language Models
Large language models (LLMs) have received increasing attention.
Personalized Microblog Sentiment Classification via Adversarial Cross-lingual Multi-task Learning
Then the attention-based CNN model is incorporated into a novel adversarial cross-lingual learning framework, in which with the help of user properties as bridge between languages, we can extract the language-specific features and language-independent features to enrich the user post representation so as to alleviate the data insufficiency problem.
A Co-Attention Neural Network Model for Emotion Cause Analysis with Emotional Context Awareness
Emotion cause analysis has been a key topic in natural language processing.
Answer-Supervised Question Reformulation for Enhancing Conversational Machine Comprehension
Moreover, pretraining is essential in reinforcement learning models, so we provide a high-quality annotated dataset for question reformulation by sampling a part of QuAC dataset.
Answer-guided and Semantic Coherent Question Generation in Open-domain Conversation
We observe that the answer has strong semantic coherence to its question and post, which can be used to guide question generation.
A Graph Reasoning Network for Multi-turn Response Selection via Customized Pre-training
We investigate response selection for multi-turn conversation in retrieval-based chatbots.
Learning to Improve Persona Consistency in Multi-party Dialogue Generation via Text Knowledge Enhancement
In an open-domain dialogue system, the consistent persona is a key factor to generate real and coherent dialogues.
DialogConv: A Lightweight Fully Convolutional Network for Multi-view Response Selection
Current end-to-end retrieval-based dialogue systems are mainly based on Recurrent Neural Networks or Transformers with attention mechanisms.
PVGRU: Generating Diverse and Relevant Dialogue Responses via Pseudo-Variational Mechanism
We investigate response generation for multi-turn dialogue in generative-based chatbots.
Evaluate What You Can't Evaluate: Unassessable Quality for Generated Response
There are risks in using eference-free evaluators based on LLMs to evaluate the quality of dialogue responses.
T-COL: Generating Counterfactual Explanations for General User Preferences on Variable Machine Learning Systems
The application of CEs encounters two main challenges: general user preferences and variable ML systems.
HiFT: A Hierarchical Full Parameter Fine-Tuning Strategy
As LMs grow in size, fine-tuning the full parameters of LMs requires a prohibitively large amount of GPU memory.
