The increasing adoption of large language models (LLMs) has raised serious concerns about their reliability and trustworthiness.As a result, a growing body of research focuses on evidence-based text generation with LLMs, aiming to link model outputs to supporting evidence to ensure traceability and verifiability.However, the field is fragmented due to inconsistent terminology, isolated evaluation practices, and a lack of unified benchmarks.To bridge this gap, we systematically analyze 134 papers, introduce a unified taxonomy of evidence-based text generation with LLMs, and investigate 300 evaluation metrics across seven key dimensions.Thereby, we focus on approaches that use citations, attribution, or quotations for evidence-based text generation.Building on this, we examine the distinctive characteristics and representative methods in the field.Finally, we highlight open challenges and outline promising directions for future work.

We present SQuAI (https://squai.scads.ai/), a scalable and trustworthy multi-agent retrieval-augmented generation (RAG) framework for scientific question answering (QA) with large language models (LLMs). SQuAI addresses key limitations of existing RAG systems in the scholarly domain, where complex, open-domain questions demand accurate answers, explicit claims with citations, and retrieval across millions of scientific documents. Built on over 2.3 million full-text papers from arXiv.org, SQuAI employs four collaborative agents to decompose complex questions into sub-questions, retrieve targeted evidence via hybrid sparse-dense retrieval, and adaptively filter documents to improve contextual relevance. To ensure faithfulness and traceability, SQuAI integrates in-line citations for each generated claim and provides supporting sentences from the source documents. Our system improves faithfulness, answer relevance, and contextual relevance by up to +0.088 (12%) over a strong RAG baseline. We further release a benchmark of 1,000 scientific question-answer-evidence triplets to support reproducibility. With transparent reasoning, verifiable citations, and domain-wide scalability, SQuAI demonstrates how multi-agent RAG enables more trustworthy scientific QA with LLMs.

Given a text query on a controversial topic, the task of Image Retrieval for Argumentation is to rank images according to how well they can be used to support a discussion on the topic. An important subtask therein is to determine the stance of the retrieved images, i.e., whether an image supports the pro or con side of the topic. In this paper, we conduct a comprehensive reproducibility study of the state of the art as represented by the CLEF'22 Touché lab and an in-house extension of it. Based on the submitted approaches, we developed a unified and modular retrieval process and reimplemented the submitted approaches according to this process. Through this unified reproduction (which also includes models not previously considered), we achieve an effectiveness improvement in argumentative image detection of up to 0.832 [email protected] However, despite this reproduction success, our study also revealed a previously unknown negative result: for stance detection, none of the reproduced or new approaches can convincingly beat a random baseline. To understand the apparent challenges inherent to image stance detection, we conduct a thorough error analysis and provide insight into potential new ways to approach this task.