Enterprise AI Analysis: Bayesian Network Fusion of Large Language Models for Sentiment Analysis
Bayesian Network Fusion Enhances LLM Sentiment Analysis with Interpretability and Accuracy
This study introduces the Bayesian Network LLM Fusion (BNLF) framework for financial sentiment analysis, integrating predictions from multiple Large Language Models (LLMs) through a probabilistic mechanism. BNLF models probabilistic dependencies among LLM predictions, enhancing interpretability and achieving consistent accuracy gains across diverse financial datasets.
Key Findings & Strategic Impact
The BNLF framework demonstrates significant improvements in accuracy and interpretability for financial sentiment analysis, offering a robust and scalable solution for enterprise AI.
0
Overall Accuracy
0
Accuracy Gain Over Baselines
0
Macro-F1 Score
0
BNLF Mean Consistency (Kappa)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
BNLF Framework
The Bayesian Network LLM Fusion (BNLF) framework integrates predictions from multiple LLMs using a probabilistic mechanism to perform sentiment analysis. It leverages the complementary strengths of individual LLMs and dynamically adjusts their influences based on learned probabilistic dependencies within a Bayesian Network. This late-fusion strategy explicitly models the joint probability distribution over input variables, providing a principled approach to capture uncertainty and bias for interpretable sentiment classification. The framework is designed to be lightweight, using medium-sized LLMs without requiring extensive fine-tuning or large GPU resources, making it practical for inference-only tasks. Its modular and scalable nature allows for enhanced interpretability and transparent reasoning, crucial for trustworthy AI applications.
Performance Evaluation
BNLF was rigorously evaluated across three diverse human-annotated financial corpora: Financial PhraseBank, Twitter Financial News Sentiment (TFNS), and FIQA. The framework consistently outperformed all individual LLMs and ensemble baselines, achieving an overall accuracy of 78.6%, a gain of about 6% over the DistilRoBERTa baseline. Notably, BNLF showed balanced performance across sentiment classes, with high F1-scores for neutral (0.850), negative (0.639), and positive (0.667) sentiments. While DistilRoBERTa performed exceptionally well on Financial PhraseBank (99.6% accuracy), BNLF demonstrated superior or competitive performance on the more challenging FIQA and TFNS datasets, highlighting its robustness to varying linguistic styles and contextual characteristics.
Interpretability Analysis
A key advantage of BNLF is its enhanced interpretability, achieved through inference analysis and influence strength assessment within the Bayesian Network. Scenario-based inference revealed how corpus type significantly influences BNLF’s certainty and the dominant sentiment, even with conflicting LLM predictions. For instance, identical negative predictions across LLMs yielded over 95% negative for Financial PhraseBank but shifted to 66.5% negative and 31.5% neutral for TFNS. The influence strength analysis showed that FinBERT had the strongest direct influence (0.364) on BNLF, followed by RoBERTa (0.320) and BERTweet (0.309), while corpus type also directly affected BNLF (0.327). This transparency allows a clear understanding of how sentiment decisions are formed, providing valuable insights into model behavior and supporting the principles of trustworthy AI.
Enterprise Process Flow
Input Text (Financial Data, Social Media)
→
LLM Module (FinBERT, RoBERTa, BERTweet)
→
Individual Sentiment Predictions
→
Bayesian Network Fusion
→
Probabilistic Sentiment Distribution
→
Final Sentiment Label (NEG, NEU, POS)
BNLF Accuracy Advantage
78.6% Accuracy on Combined Test Set, surpassing baselines| Feature | BNLF | Individual LLMs & Simple Ensembles |
|---|---|---|
| Accuracy |
|
|
| Interpretability |
|
|
| Robustness & Consistency |
|
|
Case Study: Conflict Resolution in TFNS Corpus
In the Twitter Financial News Sentiment (TFNS) corpus, despite all individual LLMs (FinBERT, RoBERTa, BERTweet) being fixed to predict 'negative' sentiment, BNLF's probabilistic inference shifted the outcome towards greater uncertainty. It predicted 66.5% negative and 31.5% neutral, demonstrating how corpus type influences the model's certainty and balance between sentiment classes. This highlights BNLF's ability to consider contextual factors beyond individual LLM predictions to provide a more nuanced and reliable sentiment assessment.
Outcome: Reduced False Negatives on Noisy Data
FinBERT's Strongest Influence
0.364 Influence strength of FinBERT on BNLF's final predictionsQuantify Your AI Advantage
Estimate the potential return on investment for integrating advanced AI sentiment analysis into your operations.
Estimated Annual Savings
$0
Annual Hours Reclaimed
0
Your AI Implementation Roadmap
A structured approach to integrating Bayesian Network LLM Fusion for optimal sentiment analysis in your enterprise.
Phase 1: Data Collection & LLM Prediction Generation
Gather diverse financial text data (news, social media, reports). Process text through FinBERT, RoBERTa, and BERTweet to generate individual sentiment predictions, establishing the input for the BNLF.
Phase 2: Bayesian Network Construction & Learning
Define the BN structure based on domain knowledge, linking input text context and LLM predictions to the final sentiment node. Learn Conditional Probability Tables (CPTs) from annotated training data to model probabilistic dependencies.
Phase 3: BNLF Fusion & Inference Engine Deployment
Integrate LLM predictions into the BN for probabilistic inference, generating a fused sentiment distribution. Deploy the BNLF as a lightweight, inference-only service for real-time sentiment classification.
Phase 4: Interpretability & Performance Monitoring
Conduct inference and influence analyses to understand BNLF's decision-making. Continuously monitor performance on new data and refine BN parameters as needed, ensuring transparency and trustworthiness.
Ready to Elevate Your AI Strategy?
Connect with our AI specialists to explore how Bayesian Network Fusion can transform your sentiment analysis capabilities and drive business value.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat