#data-science
Data science methods applied to financial datasets and accounting workflows
Izacard and Grave's FiD architecture independently encodes retrieved passages then fuses them in the decoder, outperforming RAG-Sequence by 4–11 points on NQ and TriviaQA. This post examines the design and its implications for Beancount ledger QA, where multi-entry synthesis across transactions is the norm.
A NeurIPS 2024 Spotlight paper ablates three LLM-based time series forecasting methods — OneFitsAll, Time-LLM, and CALF — and finds that removing the language model improves accuracy in most cases, with up to a 1,383× training speedup. For finance AI applications like Beancount balance prediction, lightweight purpose-built models consistently beat repurposed LLMs.
TAT-LLM fine-tunt LLaMA 2 7B met LoRA op financiële tabel-tekst QA-benchmarks en behaalt 64,60% EM op FinQA — waarmee het de 63,91% van GPT-4 verslaat — door redenering te ontleden in deterministische Extraheer-Redeneer-Voer-uit stappen die rekenkundige fouten elimineren.
Empirical comparison of RAG vs. unsupervised fine-tuning across 7B-parameter LLMs shows RAG achieves 0.875+ accuracy on post-cutoff facts while fine-tuning plateaus at 0.504 — with direct implications for Beancount agent design and any system requiring frequent knowledge updates.
Lewis et al. hit 44.5 EM on Natural Questions with a frozen FAISS index; for Beancount ledgers that means reindex-or-miss when balances change daily.
MultiHiertt (ACL 2022) introduces 10,440 QA pairs from real financial reports averaging 3.89 hierarchical tables each; state-of-the-art models score 38% F1 versus 87% for humans, with a 15-point penalty for cross-table questions — quantifying the retrieval gap finance AI must close.
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 is a 16,552-question benchmark over hybrid table-plus-text financial report contexts that showed evidence grounding — not arithmetic — is the core bottleneck in finance AI; by 2024, fine-tuned 7B LLMs reached 83% F1, closing most of the gap against a 91% human ceiling.
FinanceBench evaluates 16 AI configurations against 10,231 questions from real SEC filings; shared-vector-store RAG answers correctly only 19% of the time, and even GPT-4-Turbo with the oracle passage reaches just 85% accuracy — showing that numerical reasoning, not retrieval, is the binding constraint for enterprise finance AI.
Self-consistency replaces greedy chain-of-thought decoding with a majority vote over N sampled reasoning paths — raising GPT-3 accuracy on GSM8K by 17.9 percentage points with no extra training — and applies directly to multi-step financial calculations where a single model decode is unreliable.
PAL gains +38.1pp over chain-of-thought on GSM-hard by running Python for arithmetic—the right split for reliable Beancount ledger calculations.
Four 2024–2025 benchmarks show GPT-4 scoring 42% on real-world table QA versus 86% for humans, with complex aggregations collapsing to 19.6%—and Beancount's native syntax sits at the worst-performing end of the serialization hierarchy for LLM input.
