17 accepted papers
Multimodal Instruction Following serves as a fundamental capability of multimodal language models, involving accurate comprehension and execution of user-provided instructions. However, existing multimodal instruction-following datasets and benchmarks face the shortcomings outlined below: (a) Lack o
Multimodal sarcasm detection (MSD) aims to identify sarcasm polarity from diverse modalities (i.e., image–text pairs), a task that has received increasing attention. While significant progress has been made, existing approaches still face two major issues: lack of explainability and weak generalizab
In recent years, large language models (LLMs) have demonstrated outstanding capabilities in various tasks. However, LLMs also have various drawbacks, especially hallucination. Hallucination refers to the generation of content that does not align with the user input, contradicts previously generated…
Hallucination has emerged as a critical challenge for large language models (LLMs) and large vision-language models (LVLMs), particularly in high-stakes medical applications. Despite its significance, dedicated research on medical hallucination remains unexplored. In this survey, we first provide a…
We introduce SONO, a novel method leveraging Second-Order Neural Ordinary Differential Equations (Second-Order NODEs) to enhance cross-modal few-shot learning. By employing a simple yet effective architecture consisting of a Second-Order NODEs model paired with a cross-modal classifier, SONO address…
In recent years, multi-modal large language models (MLLMs) have been successfully adopted to generate humorous and engaging descriptions for internet memes. While, it is challenging for the same approaches to apply to ordinary images which lack of inherent funny or exaggerated contents. Thus, crafti…
In applications such as dialogue systems, personalized recommendations, and personal assistants, large language models (LLMs) need to retain and utilize historical information over the long term to provide more accurate and consistent responses. Although long-term memory capability is crucial, recen…
Global biodiversity is declining at an unprecedented rate, yet little information is known about most species and how their populations are changing. Indeed, some 90% Earth’s species are estimated to be completely unknown. Machine learning has recently emerged as a promising tool to facilitate long-…
Complex video question-answering (VQA) requires in-depth understanding of video contents including object and action recognition as well as video classification and summarization, which exhibits great potential in emerging applications in education and entertainment, etc. Multimodal large language m…
The evaluation of the problem-solving capability under incomplete information scenarios of Large Language Models (LLMs) is increasingly important, encompassing capabilities such as questioning, knowledge search, error detection, and path planning. Current research mainly focus on LLMs’ problem-solvi…
Teachers are important to imparting knowledge and guiding learners, and the role of large language models (LLMs) as potential educators is emerging as an important area of study. Recognizing LLMs’ capability to generate educational content can lead to advances in automated and personalized learning.…
Emotional intelligence in large language models (LLMs) is of great importance in Natural Language Processing. However, the previous research mainly focus on basic sentiment analysis tasks, such as emotion recognition, which is not enough to evaluate LLMs’ overall emotional intelligence. Therefore, t…
In the era of social media video platforms, popular “hot-comments” play a crucial role in attracting user impressions of short-form videos, making them vital for marketing and branding purpose. However, existing research predominantly focuses on generating descriptive comments or “danmaku” in Englis…
Multi-Label Text Classification (MLTC) is a fundamental task in the field of Natural Language Processing (NLP) that involves the assignment of multiple labels to a given text. MLTC has gained significant importance and has been widely applied in various domains such as topic recognition, recommendat…
In-air handwriting as a new human-computer interaction way plays an important role in many virtual/mixed-reality applications. Existing methods for in-air handwritten text recognition (IAHTR) typically directly process handwriting trajectories with deep neural networks. However, those methods all si…
Humor is a crucial part of human communication. Understanding humor and generating humorous responses in dialogue can provide natural and empathic human-computer interactions. However, most existing pre-trained language models (PLMs) perform unsatisfactorily in humor generation. On one hand, the se…
Prompt engineering, as an efficient and effective way to leverage Large Language Models (LLM), has drawn a lot of attention from the research community. The existing research primarily emphasizes the importance of adapting prompts to specific tasks, rather than specific LLMs. However, a good prompt…