Composite LLM scoring saturates on expert investment frameworks while Gate Reconstruction Accuracy still reveals a clear procedural deficit at the frontier.
ConvFinQA: Exploring the chain of numerical reasoning in 20 conversational finance question answering
9 Pith papers cite this work, alongside 3 external citations. Polarity classification is still indexing.
9
Pith papers citing it
3
external citations · external index
citation-role summary
background 1
citation-polarity summary
roles
background 1polarities
background 1representative citing papers
FinAuditing is a taxonomy-structured multi-document benchmark with 1,102 instances averaging over 33k tokens from XBRL filings, defining three tasks to evaluate LLMs on financial auditing capabilities.
FinTagging decomposes XBRL tagging into FinNI extraction and FinCL full-taxonomy linking, showing LLMs handle extraction but struggle with fine-grained concept alignment in zero-shot settings.
PoT prompting improves numerical reasoning by having language models write programs executed by a computer instead of performing calculations in natural language chains of thought, with an average 12% gain over CoT.
CLExEval introduces a human-annotated evaluation framework on 40 rare cases that identifies verbosity bias, hidden knowledge paradox, and 68.6% reasoning-to-output mismatch in LLMs while showing LLM-as-a-Judge overestimates reliability.
FINESSE-Bench is a new hierarchical benchmark suite combining certification-style exams, trading tasks, and a Russian olympiad set to evaluate LLMs on financial competencies at multiple difficulty levels.
TaNOS improves cross-domain numerical reasoning over tables by combining header anonymization, operation sketches, and self-supervised pretraining, achieving 80.13% accuracy on FinQA with 10% of training data.
Entity-based chunk filtering reduces RAG vector index size by 25-36% with retrieval quality near baseline levels.
A survey synthesizing recent LLM research and assessing its applicability to financial data analysis.
citing papers explorer
-
InvestPhilBench: A Multi-Layer Benchmark for Evaluating Large Language Model Procedural Reasoning in Expert Investment Philosophy
Composite LLM scoring saturates on expert investment frameworks while Gate Reconstruction Accuracy still reveals a clear procedural deficit at the frontier.
-
FinAuditing: A Financial Taxonomy-Structured Multi-Document Benchmark for Evaluating LLMs
FinAuditing is a taxonomy-structured multi-document benchmark with 1,102 instances averaging over 33k tokens from XBRL filings, defining three tasks to evaluate LLMs on financial auditing capabilities.
-
FinTagging: Benchmarking LLMs for Extracting and Structuring Financial Information
FinTagging decomposes XBRL tagging into FinNI extraction and FinCL full-taxonomy linking, showing LLMs handle extraction but struggle with fine-grained concept alignment in zero-shot settings.
-
Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks
PoT prompting improves numerical reasoning by having language models write programs executed by a computer instead of performing calculations in natural language chains of thought, with an average 12% gain over CoT.
-
CLExEval: A Human-in-the-Loop Framework for Qualitative Evaluation of LLM Clinical Reasoning
CLExEval introduces a human-annotated evaluation framework on 40 rare cases that identifies verbosity bias, hidden knowledge paradox, and 68.6% reasoning-to-output mismatch in LLMs while showing LLM-as-a-Judge overestimates reliability.
-
FINESSE-Bench: A Hierarchical Benchmark Suite for Financial Domain Knowledge and Technical Analysis in Large Language Models
FINESSE-Bench is a new hierarchical benchmark suite combining certification-style exams, trading tasks, and a Russian olympiad set to evaluate LLMs on financial competencies at multiple difficulty levels.
-
Generalizing Numerical Reasoning in Table Data through Operation Sketches and Self-Supervised Learning
TaNOS improves cross-domain numerical reasoning over tables by combining header anonymization, operation sketches, and self-supervised pretraining, achieving 80.13% accuracy on FinQA with 10% of training data.
-
Reducing Redundancy in Retrieval-Augmented Generation through Chunk Filtering
Entity-based chunk filtering reduces RAG vector index size by 25-36% with retrieval quality near baseline levels.
-
Bridging Language Models and Financial Analysis
A survey synthesizing recent LLM research and assessing its applicability to financial data analysis.