Tootfinder

VeritasFi: An Adaptable, Multi-tiered RAG Framework for Multi-modal Financial Question Answering
Zhenghan Tai, Hanwei Wu, Qingchen Hu, Jijun Chi, Hailin He, Lei Ding, Tung Sum Thomas Kwok, Bohuai Xiao, Yuchen Hua, Suyuchen Wang, Peng Lu, Muzhi Li, Yihong Wu, Liheng Ma, Jerry Huang, Jiayi Zhang, Gonghao Zhang, Chaolong Jiang, Jingrui Tian, Sicheng Lyu, Zeyu Li, Boyu Han, Fengran Mo, Xinyue Yu, Yufei Cui, Ling Zhou, Xinyu Wang

VeritasFi: An Adaptable, Multi-tiered RAG Framework for Multi-modal Financial Question Answering
Retrieval-Augmented Generation (RAG) is becoming increasingly essential for Question Answering (QA) in the financial sector, where accurate and contextually grounded insights from complex public disclosures are crucial. However, existing financial RAG systems face two significant challenges: (1) they struggle to process heterogeneous data formats, such as text, tables, and figures; and (2) they encounter difficulties in balancing general-domain applicability with company-specific adaptation. To…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[13/17]:
- Judge Before Answer: Can MLLM Discern the False Premise in Question?
Jidong Li, Lingyong Fang, Haodong Zhao, Sufeng Duan, Gongshen Liu

Teaching Language Models to Faithfully Express their Uncertainty
Bryan Eikema, Evgenia Ilia, Jos\'e G. C. de Souza, Chrysoula Zerva, Wilker Aziz
https://arxiv.org/abs/2510.12587

Teaching Language Models to Faithfully Express their Uncertainty
Large language models (LLMs) often miscommunicate their uncertainty: repeated queries can produce divergent answers, yet generated responses are typically unhedged or hedged in ways that do not reflect this variability. This conveys unfaithful information about the uncertain state of the LLMs' knowledge, creating a faithfulness gap that affects even strong LLMs. We introduce Faithful Uncertainty Tuning (FUT): a fine-tuning approach that teaches instruction-tuned LLMs to express uncertainty fait…

The Smashing Magazine comment form is still broken, so I’m memorializing the comment I _wanted_ to leave about CSS carousels:
https://adrianroselli.com/2025/05/my-request-to-google-on-accessibility.html#Update06 (anchor)
Includes quote fr…

My Request to Google on Accessibility
Hey, Alphabet or Google or Chrome or whomever in that illegal monopoly continues to release things to the web platform that are full of accessibility barriers, I have what I think is a straightforward request. My Request Please, if your team cannot explain how the thing satisfies all WCAG Success…

Crosslisted article(s) found for cs.CE. https://arxiv.org/list/cs.CE/new
[1/1]:
- A Role-Aware Multi-Agent Framework for Financial Education Question Answering with LLMs
Andy Zhu, Yingjun Du

Crosslisted article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[1/2]:
- Look before Transcription: End-to-End SlideASR with Visually-Anchored Policy Optimization
Rui Hu, Delai Qiu, Yining Wang, Shengping Liu, Jitao Sang

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[12/17]:
- VeritasFi: An Adaptable, Multi-tiered RAG Framework for Multi-modal Financial Question Answering
Zhenghan Tai, et al.

COSTAR-A: A prompting framework for enhancing Large Language Model performance on Point-of-View questions
Nzubechukwu C. Ohalete (School of Data Science and Analytics, Kennesaw State University, GA, USA), Kevin B. Gittner (School of Data Science and Analytics, Kennesaw State University, GA, USA), Lauren M. Matheny (School of Data Science and Analytics, Kennesaw State University, GA, USA)

COSTAR-A: A prompting framework for enhancing Large Language Model performance on Point-of-View questions
Large Language Models (LLMs) are highly sensitive to prompt design, and making optimized prompting techniques is crucial for generating consistent, high-quality outputs. In this study, we introduce COSTAR-A, a novel prompt engineering framework that enhances the existing COSTAR method, which stands for Context, Objective, Style, Tone, Audience, and Response, by adding the 'Answer' component at the end. We demonstrate that while the original COSTAR framework improves prompt clarity and aligns ou…

Crosslisted article(s) found for cs.IR. https://arxiv.org/list/cs.IR/new
[1/1]:
- Towards Reliable and Interpretable Document Question Answering via VLMs
Alessio Chen, Simone Giovannini, Andrea Gemelli, Fabio Coppini, Simone Marinai

VeriCite: Towards Reliable Citations in Retrieval-Augmented Generation via Rigorous Verification
Haosheng Qian, Yixing Fan, Jiafeng Guo, Ruqing Zhang, Qi Chen, Dawei Yin, Xueqi Cheng
https://arxiv.org/abs/2510.11394

VeriCite: Towards Reliable Citations in Retrieval-Augmented Generation via Rigorous Verification
Retrieval-Augmented Generation (RAG) has emerged as a crucial approach for enhancing the responses of large language models (LLMs) with external knowledge sources. Despite the impressive performance in complex question-answering tasks, RAG still struggles with hallucinations. Attributing RAG-generated content through in-line citations has demonstrated potential in reducing hallucinations and facilitating human verification. Existing citation generation methods primarily rely on either fine-tuni…