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This paper evaluates intrinsic sequence-likelihood confidence in retrieval-dominated extractive QA, finding that confidence signals are insufficient for model control and that retrieval alone achieves high accuracy under pre-specified criteria.
This paper presents team HSA_CORAL's submission to the FinCausal 2026 shared task, comparing encoder-only, encoder-decoder, and decoder-only LLMs for extractive question answering of cause-effect relations in financial narratives. Fine-tuned GPT-4.1 Mini achieved top scores in the English subtask and third in Spanish.
ACL-Verbatim introduces a family of lightweight extractive models for grounded RAG that return exact text spans from source, outperforming larger LLM-based extractors.