Pith. sign in

REVIEW 2 cited by

Open FinLLM Leaderboard: Towards Financial AI Readiness

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2501.10963 v2 pith:5IUO6R7Q submitted 2025-01-19 cs.CE

classification cs.CE
keywords openfinancialleaderboardmodelsadoptioncapabilitiescontributionsfinllm
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Financial large language models (FinLLMs) with multimodal capabilities are envisioned to revolutionize applications across business, finance, accounting, and auditing. However, real-world adoption requires robust benchmarks of FinLLMs' and FinAgents' performance. Maintaining an open leaderboard is crucial for encouraging innovative adoption and improving model effectiveness. In collaboration with Linux Foundation and Hugging Face, we create an open FinLLM leaderboard, which serves as an open platform for assessing and comparing AI models' performance on a wide spectrum of financial tasks. By demoncratizing access to advances of financial knowledge and intelligence, a chatbot or agent may enhance the analytical capabilities of the general public to a professional level within a few months of usage. This open leaderboard welcomes contributions from academia, open-source community, industry, and stakeholders. In particular, we encourage contributions of new datasets, tasks, and models for continual update. Through fostering a collaborative and open ecosystem, we seek to promote financial AI readiness.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Interpretable LLMs for Credit Risk: A Systematic Review and Taxonomy

    q-fin.RM 2025-06 conditional novelty 4.0 of 10

    A systematic review and taxonomy that organizes LLM-based credit risk research by model architecture, data modality, explainability mechanism, and application domain.

  2. Towards Unified Multimodal Financial Forecasting: Integrating Sentiment Embeddings and Market Indicators via Cross-Modal Attention

    cs.AI 2025-08 unverdicted novelty 3.0 of 10

    The abstract claims a multimodal stock predictor beats numeric-only baselines, but the body text is an unrelated topology note, leaving the claim unsupported.

Pith tools