5th Workshop on Scholarly Document Processing

4th Workshop on Scholarly Document Processing

Stellenausschreibung ID 11872

PDF-Based Question Answering with Amazon Bedrock and Haystack

Amazon Bedrock is a fully managed service that provides high-performing foundation models from leading AI startups and Amazon through a single API. You can choose from various foundation models to…

GitHub - QwenLM/Qwen2-VL: Qwen2-VL is the multimodal large language model series developed by Qwen team, Alibaba Cloud.

Qwen2-VL is the multimodal large language model series developed by Qwen team, Alibaba Cloud. - QwenLM/Qwen2-VL

MBZUAI - Mohamed bin Zayed University of Artificial Intelligence

Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) is a graduate research university dedicated to advancing AI as a global force for good.

Meet GraphRAG: Microsoft’s New Graph-Based AI Method for Superior Data Insights

Microsoft introduces GraphRAG, a graph-based AI method for retrieval-augmented generation, now available on GitHub. This tool enhances data retrieval and question answering for private or unseen datasets, offering a systematic and complete response production.

Beyond Boundaries: A Human-like Approach for Question Answering over Structured and Unstructured Information Sources

Together with my co-authors, we are excited to share our work on an easy-to-apply method to improve LLM reasoning and how we applied it for question answering across heterogenous sources. 🚀 Language models relying solely on their internal parameters lack knowledge about recent knowledge as well as

Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation

Large language models (LLMs) have shown impressive prowess in solving a wide range of tasks with world knowledge. However, it remains unclear how well LLMs are able to perceive their factual knowledge boundaries, particularly under retrieval augmentation settings. In this study, we present the first analysis on the factual knowledge boundaries of LLMs and how retrieval augmentation affects LLMs on open-domain question answering (QA), with a bunch of important findings. Specifically, we focus on three research questions and analyze them by examining QA, priori judgement and posteriori judgement capabilities of LLMs. We show evidence that LLMs possess unwavering confidence in their knowledge and cannot handle the conflict between internal and external knowledge well. Furthermore, retrieval augmentation proves to be an effective approach in enhancing LLMs' awareness of knowledge boundaries. We further conduct thorough experiments to examine how different factors affect LLMs and propose a simple method to dynamically utilize supporting documents with our judgement strategy. Additionally, we find that the relevance between the supporting documents and the questions significantly impacts LLMs' QA and judgemental capabilities. The code to reproduce this work is available at https://github.com/RUCAIBox/LLM-Knowledge-Boundary.