AI Safety & Reliability

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 Hallucination, defined as the generation of plausible but factually incorrect output, is a well-documented limitation of large language models (LLMs). In high-stakes domains such as macroeconomic forecasting, ungrounded outputs present serious epistemic and operational risks. This paper outlines the architectural and methodological strategies employed in the MACRA.AI platform to minimize hallucination risk. We propose a hybrid system in which LLMs are used solely for synthesizing soft priors and narrative interpretation, while all forecasting and probabilistic inference are conducted through structured econometric models (e.g., VARs) and deep learning (e.g., LSTM-based time series models). We show that hallucination is avoided not by mitigating model-level tendencies, but by structurally constraining the role of generative models within a broader deterministic and probabilistic framework.

The integration of LLMs into quantitative domains has exposed a critical tension between fluency and factuality. In macroeconomic systems, where policy signals, forecast precision, and market expectations converge, hallucinations are not benign, they can mislead stakeholders, misprice systemic risk, and distort resource allocation. The MACRA.AI architecture addresses this challenge through design principles that decouple linguistic generation from quantitative reasoning, ensuring that all inferential steps remain grounded in observed data or validated priors.

LLMs such as GPT-4, Claude, and PaLM are trained to maximize likelihood over token sequences. In the absence of structured conditioning, they often produce syntactically coherent yet semantically untrue outputs. This is particularly problematic when:

• Forecast values are generated directly from LLM prompts.
• Textual interpretation lacks anchoring in structured data.
• Narrative outputs are used in downstream decision pipelines.

In macroeconomic contexts, these risks are amplified by the temporal, interdependent, and nonlinear nature of real-world systems. A hallucinated “policy pivot” or a fabricated inflation trajectory can have outsized impacts on risk pricing and public perception.

MACRA.AI is a modular forecasting system that integrates five primary classes of models:

1. Bayesian Inference Engine – Aggregates empirical and narrative priors into probabilistic forecasts.
2. Vector Autoregressive Models (VARs) – Model interdependencies among macro variables.
3. Deep Learning Modules (LSTM, CNN-LSTM) – Capture nonlinear, regime-shifting behaviours in transmission mechanisms.
4. RAG-Layered LLMs – Perform narrative extraction and policy scenario interpretation.
5. Validation and Traceability Layer – Ensures all outputs are explainable, reproducible, and auditable.

The core principle is architectural asymmetry: forecasting power resides exclusively in deterministic or probabilistically constrained components, while LLMs serve supportive, not generative, roles.

LLMs in MACRA.AI are used to extract narrative priors from:

• Central bank minutes (e.g., BoE, FOMC)
• IMF and BIS reports
• Earnings call transcripts
• Sentiment-based macro surveys

The process involves:

• Extractive prompts that map language to parameterized priors (e.g., “hawkish tone” → P[no cut] = 0.65)
• Confidence thresholds (using entropy metrics or multiple sampling runs)
• Temporal tagging for source traceability

These priors are then soft inputs to the Bayesian inference engine. Empirical data always dominates in posterior construction.

LLMs are permitted to generate narrative outputs (e.g., scenario descriptions, risk summaries) only via retrieval-augmented generation (RAG). RAG prompts contain:

• Extracted model outputs (e.g., forecast probabilities)
• Source citations
• Context windows with real-time macro indicators

Outputs that contradict quantitative model states are flagged and either rejected or routed for manual review.

VARs model lagged relationships between endogenous variables such as:

• Industrial production
• Employment, vacancies, and earnings
• Credit spreads and default rates
• Trade spillovers and global indices

They provide transparency, statistical interpretability, and deterministic outputs based on observed data. Importantly, they are immune to hallucination because they do not rely on natural language generation.

MACRA.AI integrates multiple deep learning architectures:

• LSTMs / BiLSTMs to capture dynamic lag structures in policy transmission
• CNN-LSTMs for identifying non-obvious temporal anomalies
• Encoder-Decoder LSTMs for simulating counterfactuals (e.g., Fed cut vs. hold paths)

Training is restricted to structured time series (FRED, ONS, ECB, BIS, Experian, etc.), eliminating the possibility of hallucination through exposure to free text.

These models operate within bounded domains and are audited via out-of-sample validation and backtest accuracy metrics.

MACRA.AI demonstrates a robust path forward in integrating LLMs with macroeconomic forecasting without compromising on factual grounding. By assigning tightly scoped and verifiable roles to LLMs and isolating core inference within deterministic and ML-based architectures, the system achieves a balance between interpretability, linguistic fluency, and epistemic safety.

In sum, LLMs interpret, but do not infer. Forecasting is reserved for models that respect causality, lag, and data integrity.

This hybrid architecture may offer a replicable blueprint for deploying LLMs in other high-stakes quantitative environments, from epidemiology to infrastructure planning.

Keywords: hallucination, LLMs, macroeconomic forecasting, Bayesian inference, deep learning, VAR, interpretability, generative AI, RAG, scenario analysis