Recently, tool learning with large language models (LLMs) has emerged as a promising
paradigm for augmenting the capabilities of LLMs to tackle highly complex problems …

Information Retrieval (IR) systems are crucial tools for users to access information, widely
applied in scenarios like search engines, question answering, and recommendation …

Retrieval-Augmented Generation (RAG) has quickly grown into a pivotal paradigm in the
development of Large Language Models (LLMs). While much of the current research in this …

Large language models (LLMs) often produce unsupported or unverifiable content, known
as" hallucinations." To mitigate this, retrieval-augmented LLMs incorporate citations …

As a cornerstone of modern information access, search engines have become
indispensable in everyday life. With the rapid advancements in AI and natural language …

While recent Large Language Models (LLMs) have proven useful in answering user queries,
they are prone to hallucination, and their responses often lack credibility due to missing …

Enabling Large Language Models (LLMs) to generate citations in Question-Answering (QA)
tasks is an emerging paradigm aimed at enhancing the verifiability of their responses when …

Despite the significant progress of large language models (LLMs) in various tasks, they often
produce factual errors due to their limited internal knowledge. Retrieval-Augmented …

The influence of contextual input on the behavior of large language models (LLMs) has
prompted the development of context attribution methods that aim to quantify each context …

Despite demonstrating emergent reasoning abilities, Large Language Models (LLMS) often
lose track of complex, multi-step reasoning. Existing studies show that providing guidance …