huggingivy/pragmaprobe-llm-pipeline

System Summary: An end-to-end evaluation and alignment pipeline to solve "literal traps" in LLMs processing figurative language. The system inputs un-aligned, raw financial text archives (e.g., Reuters) containing complex business metaphors, passes them through a pipeline with strict data schema validation, identifies where the model misinterprets the context, and dynamically outputs low-rank adaptation (LoRA) weight modifications (SFT) on open-source transformer matrices (Hugging Face). To test the pipeline, raw headlines are injected as dictionary inputs to calculate real-time evaluation statistics, such as pass/fail ratios and confidence scores. When a failure is detected, an automated loop captures the error, queries a relational MySQL-backed knowledge repository to extract human-curated taxonomy data [utilizing a custom-created alignment rule sample to simulate large-scale RAG infrastructure], and appends the correction to a structured preference file for permanent model tuning. The updated pipeline (latest commit) systematically resolves model over-generalization and pattern collapse, successfully transitioning the agent from a rigid negation framework to true pragmatic contextual reasoning.

Examples of industrial applications for the PragmaProbe-LLM pipeline across key machine learning domains:

• LLM Red Teaming & Behavioral Auditing:
  • Why: Un-aligned models mistake adversarial, non-literal, or sarcastic prompt injections for literal instructions, causing severe hallucinations.
  • Application: The pipeline acts as an automated evaluation gate to systematically isolate contextual reasoning failures, generating safety preference datasets to reinforce models against linguistic vulnerabilities.
• Context-Aware Sentiment Recognition:
  • Why: Traditional sentiment analysis tools misclassify intense metaphoric language (e.g., "stocks face a brutal slaughter") as highly violent or negative real-world physical events.
  • Application: By resolving the flouted Maxim of Quality through GraphRAG mapping, it ensures underlying commercial and financial context metrics are parsed accurately without throwing false alarms.
• Social Media & Public Relations Management Workflows:
  • Why: Brand-facing consumer bots easily over-generalize or fall into rigid negation loops when responding to colloquial user comments, idioms, or cultural slang.
  • Application: The architecture can be dropped directly into enterprise moderation pipelines to separate low-judgment automated filtering from high-judgment human-in-the-loop alignment queues, streamlining customer service routing.

End-to-End Pipeline Architecture

[Hugging Face: Reuters] ──> (Regex Anchor) ──> [Pandas Staging] ──> (Base LLM Probing) │ [SFT Model Checkpoint] <── (QLoRA / SFT Training) <── [MySQL GraphRAG] <── (AI-as-a-Judge)

Production Ingestion: Streams real-world financial text archives programmatically via the Hugging Face datasets API (Reuters Financial Corpus). Linguistic Feature Extraction: Employs advanced Regular Expression Anchors to scan text arrays and flag transitive or copular warfare terminology mapped onto business contexts. Adversarial Probing & Evaluation: Interrogates base un-aligned models and routes outputs through an automated AI-as-a-Judge Evaluator Gate to score conversational resolution (Pass: 1 / Fail: 0). GraphRAG Patching: Intercepts failing literal interpretations (Score 0) and runs a localized MySQL parameter query to extract target semantic node weights (e.g., mapping 'slaughter' to 'heavy stock losses'). Supervised Fine-Tuning (QLoRA / SFT): Compiles failures and graph contexts into a structural preference dataset, executing Low-Rank Adaptation (LoRA) fine-tuning to permanently realign the transformer's attention matrices.

Core Performance Engineering Outcomes:

• Over-Refusal Correction: Addresses optimization biases by training models to distinguish between literal physical conflict (war vocab.) and metaphorical corporate reporting.
• Training-Ready Preference Corpus: Generates structured conversational preference data layouts compatible with standard LLM optimization toolkits.

You can review the full deployment architecture on the PragmaProbe GitHub Repository.