FinRAGBench-V: Multimodal RAG with Visual Citations in the Financial Domain
FinRAGBench-V finds top models reach only 20–61% block-level citation recall on financial pages. Multimodal retrieval beats text-only by nearly 50 points.
#financial-reporting
Финансова отчетност
Генериране и одит на финансови отчети с езикови модели
Fin-RATE: How LLMs Fail at Cross-Period and Cross-Entity Financial Analysis
Fin-RATE shows LLM accuracy collapses 18.60% on longitudinal tracking, with the retrieval pipeline, not the model, as the binding bottleneck.
FinDER: Real Analyst Queries Expose a 74% Recall Gap in Financial RAG
FinDER's 5,703 real analyst queries show top RAG recalls only 25.95% of 10-K evidence. Normalize abbreviations first, before swapping embeddings.
DocFinQA: Long-Context Financial Reasoning on Full SEC Filings
DocFinQA swaps FinQA's 700-word passages for full SEC filings, a 175× longer context that nearly halves GPT-4 accuracy on long documents.
FinAuditing: LLMs Score Under 14% on Real SEC XBRL Auditing Tasks
FinAuditing shows top LLMs hit just 13.86% on financial math verification of real SEC XBRL filings, capping what AI accounting tools can automate unaided.
TAT-LLM: Fine-Tuned 7B LLaMA 2 Beats GPT-4 on Financial QA
TAT-LLM fine-tunes LLaMA 2 7B to 64.60% EM on FinQA, edging GPT-4's 63.91%: an extract-reason-execute pipeline lets a small model do table arithmetic.
MultiHiertt: Benchmarking Numerical Reasoning Over Multi-Hierarchical Financial Tables
MultiHiertt shows models score 38% F1 against 87% for humans on 10,440 financial QA pairs, with a 15-point drop on cross-table questions.
ConvFinQA: models trail experts by 21 points (68.9%)
ConvFinQA's best model scores 68.9% execution accuracy versus 89.4% for human experts—a 21-point gap that multi-turn ledger chat still faces.
TAT-QA: Hybrid Table-Text QA Benchmark for Financial Annual Report Reasoning
TAT-QA's hybrid table-text questions showed evidence grounding, not arithmetic, is finance AI's bottleneck. Fine-tuned 7B LLMs hit 83% F1 by 2024.
FinQA: The Benchmark Measuring AI Numerical Reasoning on Financial Reports
FinQA found neural models scored 61% on financial-report math versus 91% for human experts, collapsing to 22% on three-or-more-step programs.
FinanceBench: Why Vector-Store RAG Fails on Real Financial Documents
FinanceBench tests 16 AI setups on 10,231 real SEC filing questions: shared-vector-store RAG answers only 19% right, so retrieval is not the bottleneck.
PHANTOM (NeurIPS 2025): Измерване на откриването на халюцинации в LLM при финансови документи
PHANTOM (NeurIPS 2025) измерва откриването на халюцинации в реални SEC документи. Qwen3-30B води с F1=0.882, но 7B моделите гадаят почти случайно.