Enhancing Financial Report Question-Answering: A Retrieval-Augmented Generation System with Reranking Analysis

Kai Cheng, Longying Lai, Xiaoxi Qi, Yue Liu, Zhiyuan Cheng

Authors on Pith no claims yet

classification 💻 cs.CL

keywords rerankingfinancialpercentsystemanswerdemonstrategenerationperformance

read the original abstract

Financial analysts face significant challenges extracting information from lengthy 10-K reports, which often exceed 100 pages. This paper presents a Retrieval-Augmented Generation (RAG) system designed to answer questions about S&P 500 financial reports and evaluates the impact of neural reranking on system performance. Our pipeline employs hybrid search combining full-text and semantic retrieval, followed by an optional reranking stage using a cross-encoder model. We conduct systematic evaluation using the FinDER benchmark dataset, comprising 1,500 queries across five experimental groups. Results demonstrate that reranking significantly improves answer quality, achieving 49.0 percent correctness for scores of 8 or above compared to 33.5 percent without reranking, representing a 15.5 percentage point improvement. Additionally, the error rate for completely incorrect answers decreases from 35.3 percent to 22.5 percent. Our findings emphasize the critical role of reranking in financial RAG systems and demonstrate performance improvements over baseline methods through modern language models and refined retrieval strategies.

This paper has not been read by Pith yet.

discussion (0)

Forward citations

Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

ShredBench: Evaluating the Semantic Reasoning Capabilities of Multimodal LLMs in Document Reconstruction
cs.CV 2026-04 unverdicted novelty 7.0

ShredBench shows state-of-the-art MLLMs perform well on intact documents but suffer sharp drops in restoration accuracy as fragmentation increases to 8-16 pieces, indicating insufficient cross-modal semantic reasoning...
Air-Know: Arbiter-Calibrated Knowledge-Internalizing Robust Network for Composed Image Retrieval
cs.CV 2026-04 unverdicted novelty 6.0

Air-Know decouples MLLM-based external arbitration from proxy learning via knowledge internalization and dual-stream training to overcome noisy triplet correspondence in composed image retrieval.
INTENT: Invariance and Discrimination-aware Noise Mitigation for Robust Composed Image Retrieval
cs.CV 2026-04 unverdicted novelty 6.0

INTENT mitigates cross-modal correspondence noise and modality-inherent noise in composed image retrieval via FFT-based visual invariant composition and bi-objective discriminative learning.
HABIT: Chrono-Synergia Robust Progressive Learning Framework for Composed Image Retrieval
cs.CV 2026-04 unverdicted novelty 6.0

HABIT improves robustness in composed image retrieval under noisy triplets by quantifying sample cleanliness via mutual information transition rates and applying dual-consistency progressive learning to retain good pa...
ReTrack: Evidence-Driven Dual-Stream Directional Anchor Calibration Network for Composed Video Retrieval
cs.CV 2026-04 unverdicted novelty 6.0

ReTrack calibrates directional bias in composed video features using semantic disentanglement and bidirectional evidence alignment to improve retrieval performance on CVR and CIR tasks.
Resolving the Robustness-Precision Trade-off in Financial RAG through Hybrid Document-Routed Retrieval
cs.CL 2026-03 unverdicted novelty 5.0

HDRR combines document-level semantic routing with scoped chunk retrieval to outperform both pure chunk-based retrieval and semantic file routing on the FinDER benchmark, delivering higher average scores, lower failur...