Abstract Information Extraction (IE) aims to extract structural knowledge from plain natural
language texts. Recently, generative Large Language Models (LLMs) have demonstrated …

Since the release of ChatGPT and GPT-4, large language models (LLMs) and multimodal
large language models (MLLMs) have attracted widespread attention for their exceptional …

The recent surge in Large Language Models (LLMs) has garnered significant attention
across numerous fields. Fine-tuning is often required to fit general LLMs for a specific …

With the information explosion on the Web, search and recommendation are foundational
infrastructures to satisfying users' information needs. As the two sides of the same coin, both …

Model editing aims to precisely alter the behaviors of large language models (LLMs) in
relation to specific knowledge, while leaving unrelated knowledge intact. This approach has …

Reranking is a critical component in recommender systems, playing an essential role in
refining the output of recommendation algorithms. Traditional reranking models have …

Query expansion, pivotal in search engines, enhances the representation of user
information needs with additional terms. While existing methods expand queries using …

Large language models (LLMs) have not only revolutionized the field of natural language
processing (NLP) but also have the potential to bring a paradigm shift in many other fields …

In the pursuit of enhancing domain-specific Large Language Models (LLMs), Retrieval-
Augmented Generation (RAG) emerges as a promising solution to mitigate issues such as …

Large Language Model (LLM) has transformative potential in various domains, including
recommender systems (RS). There have been a handful of research that focuses on …