Search Results for author:
Found 12 papers, 8 papers with code
#HowYouTagTweets: Learning User Hashtagging Preferences via Personalized Topic Attention
It is hypothesized that one’s interests in a hashtag are related with what they said before (user history) and the existing posts present the hashtag (hashtag contexts).
RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation
To this end, we propose learning to Refine Query for Retrieval Augmented Generation (RQ-RAG) in this paper, endeavoring to enhance the model by equipping it with capabilities for explicit rewriting, decomposition, and disambiguation.
PopALM: Popularity-Aligned Language Models for Social Media Trendy Response Prediction
Social media platforms are daily exhibiting millions of events.
CMMMU: A Chinese Massive Multi-discipline Multimodal Understanding Benchmark
We introduce CMMMU, a new Chinese Massive Multi-discipline Multimodal Understanding benchmark designed to evaluate LMMs on tasks demanding college-level subject knowledge and deliberate reasoning in a Chinese context.
Mitigating Unhelpfulness in Emotional Support Conversations with Multifaceted AI Feedback
An emotional support conversation system aims to alleviate users' emotional distress and assist them in addressing their challenges.
Align on the Fly: Adapting Chatbot Behavior to Established Norms
In this paper, we aim to align large language models with the ever-changing, complex, and diverse human values (e. g., social norms) across time and locations.
HICL: Hashtag-Driven In-Context Learning for Social Media Natural Language Understanding
Natural language understanding (NLU) is integral to various social media applications.
Topic-Guided Self-Introduction Generation for Social Media Users
To allow users to better showcase themselves and network with others, we explore the auto-generation of social media self-introduction, a short sentence outlining a user's personal interests.
Borrowing Human Senses: Comment-Aware Self-Training for Social Media Multimodal Classification
Social media is daily creating massive multimedia content with paired image and text, presenting the pressing need to automate the vision and language understanding for various multimodal classification tasks.
Understanding Social Media Cross-Modality Discourse in Linguistic Space
To fill in the gap, we present a novel concept of cross-modality discourse, reflecting how human readers couple image and text understandings.
Imagine, Reason and Write: Visual Storytelling with Graph Knowledge and Relational Reasoning
Finally, we integrate the imaginary concepts and relational knowledge to generate human-like story based on the original semantics of images.
Ranked #2 on
Visual Storytelling
on VIST
Interactive Key-Value Memory-augmented Attention for Image Paragraph Captioning
In this paper, we propose an Interactive key-value Memory- augmented Attention model for image Paragraph captioning (IMAP) to keep track of the attention history (salient objects coverage information) along with the update-chain of the decoder state and therefore avoid generating repetitive or incomplete image descriptions.
