Abstract Large Language Models (LLMs) have emerged as powerful tools in the field of
Natural Language Processing (NLP) and have recently gained significant attention in the …

Due to the cumbersome nature of human evaluation and limitations of code-based
evaluation, Large Language Models (LLMs) are increasingly being used to assist humans in …

Generative modeling of discrete data underlies important applications spanning text-based
agents like ChatGPT to the design of the very building blocks of life in protein sequences …

Most recommender systems adopt collaborative filtering (CF) and provide recommendations
based on past collective interactions. Therefore, the performance of CF algorithms degrades …

Large Language Models (LLMs) demonstrate robust capabilities across various fields,
leading to a paradigm shift in LLM-enhanced Recommender System (RS). Research to date …

Associating unstructured data with structured information is crucial for real-world tasks that
require relevance search. However, existing graph learning benchmarks often overlook the …

Recent studies empirically indicate that language models (LMs) encode rich world
knowledge beyond mere semantics, attracting significant attention across various fields …

Deep neural networks have become a powerful technique for learning representations from
user-item interaction data in collaborative filtering (CF) for recommender systems. However …

Recommender systems predict personalized item rankings based on user preference
distributions derived from historical behavior data. Recently, diffusion models (DMs) have …

This paper investigates the faithfulness of multimodal large language model (MLLM) agents
in the graphical user interface (GUI) environment, aiming to address the research question …