33 tagged with "Plain-Text Accounting"
Research grounded in plain-text accounting formats and workflows
BIRD Benchmark: The Real-Database Gap in LLM Text-to-SQL
The BIRD benchmark (NeurIPS 2023) tests LLMs on 95 real databases — GPT-4 reaches only 54.89% execution accuracy with domain hints and 34.88% without, a 20-point gap that directly shapes what a natural-language BQL interface for Beancount would need to solve.
GraphRAG: From Local to Global Query-Focused Summarization
Microsoft's GraphRAG builds a Leiden-partitioned entity graph over a text corpus and precomputes community summaries to answer global sensemaking questions that standard vector RAG cannot handle — but a 2025 bias audit shows its 72–83% win rates collapse after correcting for position and length artifacts in LLM-as-judge evaluation.
StructRAG (ICLR 2025): Picking the Right Document Structure Beats GraphRAG by 28 Points
StructRAG (ICLR 2025) routes each query to a task-appropriate structure type — table, graph, catalogue, algorithm, or chunk — before reasoning, scoring 28 points higher than GraphRAG on the Loong benchmark while running 22× faster, with the DPO-trained router alone accounting for a 15-point accuracy gain.
Fusion-in-Decoder: How Multi-Passage Retrieval Improves Generative QA
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.
IRCoT: Interleaving Retrieval with Chain-of-Thought for Multi-Step QA
IRCoT interleaves BM25 retrieval with each step of a chain-of-thought reasoning loop, achieving +11.3 retrieval recall and +7.1 F1 on HotpotQA over one-step RAG — and shows a 3B model can beat GPT-3 175B when retrieval strategy is right.
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Lewis et al.'s NeurIPS 2020 paper introduced the hybrid RAG architecture—a BART-large generator paired with a FAISS-indexed retriever over 21 million Wikipedia passages—achieving 44.5 EM on Natural Questions and establishing the parametric/non-parametric split that now underlies most production AI systems. This review covers RAG-Sequence vs. RAG-Token trade-offs, the retrieval collapse failure mode, and what stale indexes mean for financial AI built on append-only Beancount ledgers.
LATS: Language Agent Tree Search — 추론, 행동, 계획을 하나의 프레임워크로 통합
LATS(Language Agent Tree Search, ICML 2024)는 ReAct, Tree of Thoughts, Reflexion을 단일 MCTS 프레임워크로 통합하여 GPT-4와 함께 HumanEval에서 92.7%의 pass@1을 달성했습니다. Git 기반의 Beancount 장부의 경우, 운영 환경에서 LATS를 제한하는 상태 복원 요구 사항을 아주 쉽게 충족할 수 있습니다.
Self-RAG: Adaptive Retrieval and Self-Critique for LLMs
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: Skill Libraries as the Foundation for Lifelong AI Agent Learning
Voyager, a GPT-4-powered Minecraft agent from NVIDIA and Caltech, demonstrates that a persistent code skill library enables genuine lifelong learning without fine-tuning — discovering 3.3× more items than prior state-of-the-art. The pattern maps directly onto long-horizon Beancount ledger automation, though financial correctness demands staging layers that game sandboxes never require.
HippoRAG: Neurobiologically Inspired Long-Term Memory for LLMs
HippoRAG (NeurIPS 2024) builds a knowledge graph from OpenIE triples and applies Personalized PageRank at query time, reaching 89.1% Recall@5 on 2WikiMultiHopQA versus 68.2% for ColBERTv2—with direct implications for querying complex financial ledgers across multi-year transaction histories.
AgentBench：评估作为代理的 LLM —— 对金融 AI 可靠性的启示
AgentBench（Liu 等人，ICLR 2024）在 8 个交互式环境中对 27 个大语言模型进行了基准测试 —— GPT-4 的综合得分为 4.01，而表现最好的开源模型仅为 0.96。三种主要的失败模式（知识图谱失败中 67.9% 为超出任务限制、数据库失败中 53.3% 为格式错误以及无效操作）直接对应了在真实账本上部署 Beancount 回写代理的风险。
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.