Tootfinder

Is it just me, or do LLMs have a strong tendency to try and guess what you want to hear and then tell you exactly that, regardless of factual accuracy?
#AI #ML #LLM

Understanding and Benchmarking the Trustworthiness in Multimodal LLMs for Video Understanding
Youze Wang, Zijun Chen, Ruoyu Chen, Shishen Gu, Yinpeng Dong, Hang Su, Jun Zhu, Meng Wang, Richang Hong, Wenbo Hu
https://arxiv.org/abs/2506.12336

Understanding and Benchmarking the Trustworthiness in Multimodal LLMs for Video Understanding
Recent advancements in multimodal large language models for video understanding (videoLLMs) have improved their ability to process dynamic multimodal data. However, trustworthiness challenges factual inaccuracies, harmful content, biases, hallucinations, and privacy risks, undermine reliability due to video data's spatiotemporal complexities. This study introduces Trust-videoLLMs, a comprehensive benchmark evaluating videoLLMs across five dimensions: truthfulness, safety, robustness, fairness, …

Reinforcement Fine-Tuning for Reasoning towards Multi-Step Multi-Source Search in Large Language Models
Wentao Shi, Yiqing Shen
https://arxiv.org/abs/2506.08352

Reinforcement Fine-Tuning for Reasoning towards Multi-Step Multi-Source Search in Large Language Models
Large language models (LLMs) can face factual limitations when responding to time-sensitive queries about recent events that arise after their knowledge thresholds in the training corpus. Existing search-augmented approaches fall into two categories, each with distinct limitations: multi-agent search frameworks incur substantial computational overhead by separating search planning and response synthesis across multiple LLMs, while single-LLM tool-calling methods restrict themselves to sequentia…

This https://arxiv.org/abs/2503.22315 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

Large Language Models Are Democracy Coders with Attitudes
Current political developments worldwide illustrate that research on democratic backsliding is as important as ever. A recent exchange in Political Science & Politics (2/2024) has highlighted again a fundamental challenge in this literature: the measurement of democracy. With many democracy indicators consisting of subjective assessments rather than factual observations, trends in democracy over time could be due to human biases in the coding of these indicators rather than empirical facts. In …

iQUEST: An Iterative Question-Guided Framework for Knowledge Base Question Answering
Shuai Wang, Yinan Yu
https://arxiv.org/abs/2506.01784 https://

iQUEST: An Iterative Question-Guided Framework for Knowledge Base Question Answering
While Large Language Models (LLMs) excel at many natural language processing tasks, they often suffer from factual inaccuracies in knowledge-intensive scenarios. Integrating external knowledge resources, particularly knowledge graphs (KGs), provides a transparent and updatable foundation for more reliable reasoning. Knowledge Base Question Answering (KBQA), which queries and reasons over KGs, is central to this effort, especially for complex, multi-hop queries. However, multi-hop reasoning pose…

This https://arxiv.org/abs/2505.11396 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Finding Counterfactual Evidences for Node Classification
Counterfactual learning is emerging as an important paradigm, rooted in causality, which promises to alleviate common issues of graph neural networks (GNNs), such as fairness and interpretability. However, as in many real-world application domains where conducting randomized controlled trials is impractical, one has to rely on available observational (factual) data to detect counterfactuals. In this paper, we introduce and tackle the problem of searching for counterfactual evidences for the GNN…

This https://arxiv.org/abs/2505.01146 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

Retrieval-Augmented Generation in Biomedicine: A Survey of Technologies, Datasets, and Clinical Applications
Recent advances in large language models (LLMs) have demonstrated remarkable capabilities in natural language processing tasks. However, their application in the biomedical domain presents unique challenges, particularly regarding factual accuracy and up-to-date knowledge integration. Retrieval Augmented Generation (RAG) has emerged as a promising solution to address these challenges by combining the generative capabilities of LLMs with external knowledge retrieval. This comprehensive survey ex…

DRAG: Distilling RAG for SLMs from LLMs to Transfer Knowledge and Mitigate Hallucination via Evidence and Graph-based Distillation
Jennifer Chen, Aidar Myrzakhan, Yaxin Luo, Hassaan Muhammad Khan, Sondos Mahmoud Bsharat, Zhiqiang Shen
https://arxiv.org/abs/2506.01954

DRAG: Distilling RAG for SLMs from LLMs to Transfer Knowledge and Mitigate Hallucination via Evidence and Graph-based Distillation
Retrieval-Augmented Generation (RAG) methods have proven highly effective for tasks requiring factual consistency and robust knowledge retrieval. However, large-scale RAG systems consume significant computational resources and are prone to generating hallucinated content from Humans. In this work, we introduce $\texttt{DRAG}$, a novel framework for distilling RAG knowledge from large-scale Language Models (LLMs) into small LMs (SLMs). Our approach leverages evidence- and knowledge graph-based d…

This https://arxiv.org/abs/2505.12574 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

PoisonArena: Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) systems, widely used to improve the factual grounding of large language models (LLMs), are increasingly vulnerable to poisoning attacks, where adversaries inject manipulated content into the retriever's corpus. While prior research has predominantly focused on single-attacker settings, real-world scenarios often involve multiple, competing attackers with conflicting objectives. In this work, we introduce PoisonArena, the first benchmark to systematically stu…

The World As Large Language Models See It: Exploring the reliability of LLMs in representing geographical features
Omid Reza Abbasi, Franz Welscher, Georg Weinberger, Johannes Scholz
https://arxiv.org/abs/2506.00203

The World As Large Language Models See It: Exploring the reliability of LLMs in representing geographical features
As large language models (LLMs) continue to evolve, questions about their trustworthiness in delivering factual information have become increasingly important. This concern also applies to their ability to accurately represent the geographic world. With recent advancements in this field, it is relevant to consider whether and to what extent LLMs' representations of the geographical world can be trusted. This study evaluates the performance of GPT-4o and Gemini 2.0 Flash in three key geospatial …

Retrieval-Augmented Generation: A Comprehensive Survey of Architectures, Enhancements, and Robustness Frontiers
Chaitanya Sharma
https://arxiv.org/abs/2506.00054

Retrieval-Augmented Generation: A Comprehensive Survey of Architectures, Enhancements, and Robustness Frontiers
Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm to enhance large language models (LLMs) by conditioning generation on external evidence retrieved at inference time. While RAG addresses critical limitations of parametric knowledge storage-such as factual inconsistency and domain inflexibility-it introduces new challenges in retrieval quality, grounding fidelity, pipeline efficiency, and robustness against noisy or adversarial inputs. This survey provides a comprehensive s…

Retrieval-Augmented Generation in Biomedicine: A Survey of Technologies, Datasets, and Clinical Applications
Jiawei He, Boya Zhang, Hossein Rouhizadeh, Yingjian Chen, Rui Yang, Jin Lu, Xudong Chen, Nan Liu, Irene Li, Douglas Teodoro
https://arxiv.org/abs/2505.01146

Retrieval-Augmented Generation in Biomedicine: A Survey of Technologies, Datasets, and Clinical Applications
Recent advances in large language models (LLMs) have demonstrated remarkable capabilities in natural language processing tasks. However, their application in the biomedical domain presents unique challenges, particularly regarding factual accuracy and up-to-date knowledge integration. Retrieval Augmented Generation (RAG) has emerged as a promising solution to address these challenges by combining the generative capabilities of LLMs with external knowledge retrieval. This comprehensive survey ex…