arxivApr 20bullish

CHOP: Chunkwise Context-Preserving Framework for RAG on Multi Documents

arXiv:2604.15802v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) systems lose retrieval accuracy when similar documents coexist in the vector database, causing unnecessary information, hallucinations, and factual errors. To alleviate this issue, we propose CHOP, a framework that

LARA2 models #retrieval #language-models #information-retrieval

arxivApr 16bullish

Public Profile Matters: A Scalable Integrated Approach to Recommend Citations in the Wild

arXiv:2603.17361v2 Announce Type: replace-cross Abstract: Proper citation of relevant literature is essential for contextualising and validating scientific contributions. While current citation recommendation systems leverage local and global textual information, they often overlook the nuances of t

PRDA2 models #information-retrieval #citation-recommendation #artificial-intelligence Read on arxiv →

arxivApr 8bullish

Data-Driven Function Calling Improvements in Large Language Model for Online Financial QA

arXiv:2604.05387v1 Announce Type: cross Abstract: Large language models (LLMs) have been incorporated into numerous industrial applications. Meanwhile, a vast array of API assets is scattered across various functions in the financial domain. An online financial question-answering system can leverage

LA1 model #financial-qa #language-models #data-augmentation Read on arxiv →

arxivApr 4

From BM25 to Corrective RAG: Benchmarking Retrieval Strategies for Text-and-Table Documents

arXiv:2604.01733v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems critically depend on retrieval quality, yet no systematic comparison of modern retrieval methods exists for heterogeneous documents containing both text and tabular data. We benchmark ten retrieval strateg

BMHYHY3 models #information-retrieval #benchmark #financial-qa Read on arxiv →