TableMaster: Adaptive Reasoning for Table Understanding with LLMs
TableMaster hits 78.13% on WikiTQ with GPT-4o-mini, 13 points over Chain-of-Table, via table-of-focus plus adaptive reasoning for agents over Beancount ledgers.
#queries
Заявки
Генериране на заявки, разсъждения върху таблици и извличане на структурирани данни за финансов ИИ
Chain-of-Table: Evolving Tables in the LLM Reasoning Chain
Chain-of-Table hits 67.31% on WikiTQ versus 61.48% for text-only chain-of-thought, and leads by 10.25 points on tables over 4,000 tokens.
TableLlama: Can a 7B Open Model Match GPT-4 on Table Understanding?
TableLlama beats GPT-4 on column type annotation (F1 94 vs 32) but trails by 33 points on WikiTQ compositional reasoning.
TAPAS: Weakly Supervised Table QA Without SQL, and What It Means for Beancount
TAPAS answers table questions by selecting cells, never generating SQL. It fits small Beancount ledger queries but breaks down at scale.
MAC-SQL: Multi-Agent Collaborative Text-to-SQL
MAC-SQL's three-agent design hits 59.59% execution accuracy on BIRD, with the Refiner adding +4.63 points — a template for generating Beancount ledger queries.
DIN-SQL: GPT-4 jumps 67.4%→85.3% EX on Spider
DIN-SQL lifts GPT-4 from 67.4% to 85.3% Spider execution accuracy via schema-link and self-correct stages—same decomposition fits Beancount BQL.
BIRD: GPT-4 hits only 54.89% EX on real databases
On BIRD, GPT-4 reaches 54.89% execution accuracy with domain hints and 34.88% without—a 20-point gap any Beancount NL→BQL interface must close.
GraphRAG: 72–83% wins vanish after LLM-judge bias fix
Microsoft's GraphRAG posts 72–83% sensemaking wins over vector RAG; a 2025 audit collapses those after correcting judge bias—caution for multi-doc ledger QA.