35 tagged with "Finance"
Financial research, analysis, and domain knowledge for accounting AI
BloombergGPT and the Limits of Domain-Specific LLMs in Finance
Bloomberg trained a 50B-parameter LLM on 569B tokens of financial data and beat general models on sentiment and table-reasoning benchmarks — then GPT-4 matched it without any finance-specific pretraining. What the $10M experiment reveals about domain pretraining trade-offs, tokenization of numbers, and why tool-use is more reliable than model internals for accounting agents.
AutoGen: Multi-Agent Conversation Frameworks for Finance AI
AutoGen (Wu et al., 2023) introduces a multi-agent conversation framework where LLM-backed agents pass messages to complete tasks; a two-agent setup lifts MATH benchmark accuracy from 55% to 69%, and a dedicated SafeGuard agent improves unsafe-code detection by up to 35 F1 points — findings directly applicable to building safe, modular Beancount automation pipelines.
MemGPT: Virtual Context Management for LLM Agents
MemGPT applies OS-style virtual memory paging to LLMs, using three-tier storage — working memory, recall, and archival — to give agents persistent recall across sessions; on multi-session chat benchmarks, MemGPT with GPT-4 achieves 92.5% accuracy versus a 32.1% fixed-context baseline.
LLMs Cannot Self-Correct Reasoning Yet — ICLR 2024 Findings and Finance AI Implications
Huang et al. (ICLR 2024) show that LLMs asked to review their own reasoning without external feedback consistently degrade accuracy — GPT-4 drops from 95.5% to 91.5% on GSM8K — and what this means for designing reliable Beancount journal entry agents.
CRITIC: Why LLM Self-Correction Requires External Tool Feedback
CRITIC (ICLR 2024) achieves 7.7 F1 gains on open-domain QA and a 79.2% toxicity reduction by grounding LLM revision in external tool signals — a verify-then-correct loop that maps directly onto write-back safety for Beancount finance agents.
Себесъгласуваност: Изборът чрез мнозинство повишава точността на веригата от мисли
Себесъгласуваността заменя „алчното“ декодиране на веригата от мисли с гласуване с мнозинство върху N извлечени пътища на разсъждение — повишавайки точността на GPT-3 върху GSM8K със 17,9 процентни пункта без допълнително обучение — и се прилага директно към многостъпкови финансови изчисления, където единичното декодиране на модела е ненадеждно.
PAL: Program-Aided Language Models for Reliable Financial Arithmetic
PAL (Program-Aided Language Models) achieves a +38pp accuracy gain over chain-of-thought on arithmetic-heavy tasks by delegating computation to a Python interpreter — a directly applicable architecture for reliable Beancount ledger queries and finance AI.
Can LLMs Reason Over Tabular Data? What Four Benchmarks Tell Us About Finance AI
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.
Chain-of-Thought Prompting: Precision-Recall Trade-offs for Finance AI
A close reading of Wei et al.'s 2022 Chain-of-Thought paper and what it means for finance AI — why CoT raises precision but may cut recall on rare-event detection, why the scale threshold matters for production agents, and what a finance team building on LLMs should watch out for.
PHANTOM (NeurIPS 2025): Measuring LLM Hallucination Detection in Financial Documents
PHANTOM (NeurIPS 2025) is the first benchmark to measure LLM hallucination detection on real SEC filings across context lengths up to 30,000 tokens. Qwen3-30B-A3B-Thinking leads with F1=0.882; 7B models score near random guessing — with direct implications for autonomous accounting agents.
FinBen: Benchmarking LLMs Across 36 Financial Tasks — Implications for Accounting AI
FinBen evaluates 15 LLMs across 36 financial datasets at NeurIPS 2024, finding GPT-4 reaches 0.63 Exact Match on numerical QA and 0.54 on stock movement forecasting — near chance. Here is what those numbers mean for building a reliable accounting agent on a Beancount ledger.