arXiv Computation and Language
Evidence-Gated Bilingual RAG for Reliable Consumer Banking Search
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Language-Routed RAG and Direct Option Scoring for Multilingual Financial QA: DS@GT at FinMMEval
arXiv:2607. 22841v1 Announce Type: cross Abstract: We present DS@GT's submission to FinMMEval 2026 Task 1, a multilingual financial exam question answering benchmark spanning English, Spanish, Greek, Chinese, and Hindi.
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Cross-Lingual Legal QA for Vietnamese Labour Law: Retrieval, Translation, and Verifier-Guided Correction
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IGT @ FinMMEval 2026 Task 2: Question-Type Prompting with Targeted Extraction for Multilingual Financial QA
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arXiv AI
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Overview of FinMMEval 2026 Task 1: Multilingual Financial Multiple-Choice Question Answering
arXiv:2607. 19856v1 Announce Type: cross Abstract: FinMMEval 2026 Task 1 evaluates multilingual financial multiple-choice question answering in English, Chinese, Arabic, and Hindi.
arXiv AI
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AB-RAG: Adaptive Budgeted Retrieval-Augmented Generation for Reliable Question Answering
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AB-RAG: Adaptive Budgeted Retrieval-Augmented Generation for Reliable Question Answering
Retrieval-Augmented Generation (RAG) has become the standard way to ground large language models in external knowledge, yet most systems retrieve a fixed number of passages for every question regardless of its difficulty. This wastes computation on easy questions, starves hard ones, and gives no signal for when a generated answer can be trusted.