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LEDGER: A Long-Context Benchmark of Corporate Annual Reports for Grounded Financial Retrieval and Extraction

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arXiv:2606. 13100v1 Announce Type: new Abstract: Finance reporting is a natural proving ground for large language models, and the very-long-context capabilities of recent models across all sizes make rigorous evaluation in this domain an increasingly pressing need.

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Authors: Charles Moslonka, Amaury de Vitry, Arthur Garnier, Hicham Randrianarivo, Emmanuel Malherbe
Released: 2026-06-12 (27d ago)
Topic: Related to current project
Relation to my work: Read next because LEDGER: A Long-Context Benchmark of Corporate Annual Reports for Grounded Financial Retrieval and Extraction overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: text, eval, source, rate, extraction, full, chain, language. Source: arxiv cs.CL (NLP).
arXiv: 2606.13100

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