#llm
Large language model research with applications in financial tasks
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.
FinQA (EMNLP 2021) built 8,281 QA pairs from S&P 500 earnings reports requiring multi-step arithmetic programs. Neural models scored 61% at release versus 91% for human experts; accuracy collapses to 22% on three-or-more-step programs. The failure modes — domain constants, cross-modality grounding, chain length — map directly to the challenges Beancount agents face today.
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.
DSPy replaces hand-crafted prompt strings with declarative signatures and a metric-driven compiler—boosting Llama2-13b from 9.4% to 46.9% on GSM8K math reasoning and offering a more maintainable path for production finance AI pipelines.
LATS (Language Agent Tree Search, ICML 2024) unifies ReAct, Tree of Thoughts, and Reflexion in a single MCTS framework, reaching 92.7% pass@1 on HumanEval with GPT-4. For git-backed Beancount ledgers, the state-reversion requirement that constrains LATS in production is trivially satisfied.
Self-RAG (ICLR 2024 Oral) trains a language model to decide when to retrieve and then grade its own results using four reflection tokens — reaching 55.8% on PopQA and 80.2 FactScore on biographies while outperforming ChatGPT on five benchmarks. Analysis covers the mechanism, ablation results, reproducibility limits, and implications for finance AI agents over Beancount ledgers.
Voyager's persistent code skill library discovers 3.3× more Minecraft items than prior SOTA without fine-tuning—the reuse pattern ledger agents need.
HippoRAG's OpenIE+PageRank memory hits 89.1% Recall@5 on 2WikiMultiHopQA versus 68.2% ColBERTv2—path-aware retrieval for multi-year ledgers.
AgentBench (Liu et al., ICLR 2024) benchmarks 27 LLMs across 8 interactive environments — GPT-4 scored 4.01 overall versus 0.96 for the best open-source model. The three dominant failure modes (task-limit exceeded in 67.9% of knowledge-graph failures, format errors in 53.3% of database failures, and invalid actions) map directly onto the risks of deploying a Beancount write-back agent on a real ledger.
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 (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.
Gorilla (Patil et al., NeurIPS 2024) fine-tunes a 7B LLaMA model with Retriever-Aware Training on retrieved API documentation, cutting hallucination rates from 78% to 11% versus GPT-4 zero-shot — with direct implications for finance AI write-back agents where wrong account names or inverted signs are correctness failures, not annoyances.
