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Graph-Augmented Retrieval for Cross-Entity Financial Sentiment Analysis: A Comparative Study

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arXiv:2606. 00062v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has become foundational for grounding large language models in domain-specific corpora, yet conventional vector-based RAG systems are fundamentally limited in their ability to capture the structured, multi-entity relationships that underpin financial market analysis.

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Authors: Rajan Bastakoti, Sagar Bhetwal, Nirajan Acharya, Gaurav Kumar Gupta
Released: 2026-06-02 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because Graph-Augmented Retrieval for Cross-Entity Financial Sentiment Analysis: A Comparative Study overlaps with 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)", clean result "Only continuous soft prefixes hit both EM axes at once on Qwen-2.5-7B-Instruct: discrete prompt searches split between the alignment objective and the distributional objective, and both discretizations of the soft prefix collapse (MODERATE confidence)", clean result "Training one persona to emit a [ZLT] marker without bystanders adopting it has a one-cell-wide LR x epochs window on Qwen2.5-7B-Instruct (LOW confidence)". Matching terms: rect, under, eval, line, alone, language, model. Source: arxiv cs.CL (NLP).
arXiv: 2606.00062

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