IntrAgent uses a two-stage pipeline of section ranking and iterative reading to perform content-grounded literature information retrieval, achieving 13.2% higher accuracy than RAG and agent baselines on the new IntraBench benchmark.
Financial report chunking for effective retrieval augmented generation
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
representative citing papers
Tree reasoning outperforms vector search on complex document queries but a hybrid approach balances results across tiers, with validation showing an 11.7-point gap on real finance documents.
Systematic tests show that specific PDF parsers combined with overlapping chunking strategies better preserve structure and improve RAG answer correctness on financial QA benchmarks including the new TableQuest dataset.
RefineRAG achieves 90% attack success on NQ by generating toxic seeds then optimizing them via retriever-in-the-loop word refinement, outperforming prior methods on effectiveness and naturalness.
MultiFinRAG is a multimodal RAG framework that improves accuracy on financial QA tasks involving text, tables, and images by 19 percentage points over ChatGPT-4o while running on commodity hardware.
Structured memory improves precision on deterministic financial calculations while retrieval-augmented generation outperforms in conversational settings, supporting a hybrid deployment framework for resource-constrained SMEs.
citing papers explorer
-
IntrAgent: An LLM Agent for Content-Grounded Information Retrieval through Literature Review
IntrAgent uses a two-stage pipeline of section ranking and iterative reading to perform content-grounded literature information retrieval, achieving 13.2% higher accuracy than RAG and agent baselines on the new IntraBench benchmark.
-
Adaptive Query Routing: A Tier-Based Framework for Hybrid Retrieval Across Financial, Legal, and Medical Documents
Tree reasoning outperforms vector search on complex document queries but a hybrid approach balances results across tiers, with validation showing an 11.7-point gap on real finance documents.
-
Empirical Evaluation of PDF Parsing and Chunking for Financial Question Answering with RAG
Systematic tests show that specific PDF parsers combined with overlapping chunking strategies better preserve structure and improve RAG answer correctness on financial QA benchmarks including the new TableQuest dataset.
-
RefineRAG: Word-Level Poisoning Attacks via Retriever-Guided Text Refinement
RefineRAG achieves 90% attack success on NQ by generating toxic seeds then optimizing them via retriever-in-the-loop word refinement, outperforming prior methods on effectiveness and naturalness.
-
MultiFinRAG: An Optimized Multimodal Retrieval-Augmented Generation (RAG) Framework for Financial Question Answering
MultiFinRAG is a multimodal RAG framework that improves accuracy on financial QA tasks involving text, tables, and images by 19 percentage points over ChatGPT-4o while running on commodity hardware.
-
Architecture Matters More Than Scale: A Comparative Study of Retrieval and Memory Augmentation for Financial QA Under SME Compute Constraints
Structured memory improves precision on deterministic financial calculations while retrieval-augmented generation outperforms in conversational settings, supporting a hybrid deployment framework for resource-constrained SMEs.
- MetaGraph: A Large-Scale Meta-Analysis of GenAI in Financial NLP (2022-2025)