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This paper presents a method using Group Relative Policy Optimization (GRPO) to fine-tune an open-weight language model for generating actionable financial advice, outperforming commercial LLMs under a judge-independent CATE evaluation while also matching safety criteria.
This paper introduces LabelFusion-TS, which fuses a fine-tuned RoBERTa encoder, a prompted LLM, and time-series transformers over market data to classify Federal Reserve communication as hawkish, dovish, or neutral. The fused system achieves 70.2% weighted F1, outperforming a zero-shot LLM and showing early evidence that market time series help financial text classification.
This paper presents AWARE-FX, an auditable AI/NLP decision-support system that extracts and scores corporate foreign-exchange hedging disclosures from annual reports, evaluated on 24,909 Hong Kong firm-years with FinBERT, ModernBERT, and Qwen3-8B comparisons.
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.
Presents a framework for financial sentiment analysis using distillation with synthetic data, transferring knowledge from a large teacher to compact student models, with clustering-based seed selection for efficient low-resource domain adaptation.
This paper introduces Semantic State Abstraction Interfaces (SSAI) to separate representation hypotheses from optimization variance in LLM-augmented portfolio decisions. It concludes that SSAI's apparent advantage is largely a basket-selection effect, with dense encodings and principal components performing better empirically.