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BGI ValueGuard: Open-Source Plug-in for Real-Time Ethical Alignment in LLM Fine-Tuning

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BGI ValueGuard: Open-Source Plug-in for Real-Time Ethical Alignment in LLM Fine-Tuning

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GFRIM Oct. 23, 2025
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Challenge: Safety and ethics

Industries

Community and CollaborationLearning and educationSafety and ethics

Technologies

LLMs & NLPNeuro-symbolic AIReinforcement learning

Tags

AIGovernance & tooling

Description

A lightweight, open-source plug-in that monitors LLM fine-tuning in real time, flagging outputs misaligned with BGI’s core values (safety, transparency, inclusivity). Uses a BGI-curated ethical prompt library and lightweight RLHF-style feedback to auto-correct or pause training. Integrates with Hugging Face, SingularityNET, and MeTTa workflows.

Detailed Idea

Alignment with DF goals (BGI, Platform growth, community)

Problem description

BGI community developers fine-tune LLMs for beneficial use cases (e.g., education, governance), but lack real-time tools to ensure outputs stay aligned with BGI’s ethical principles. Post-training audits are too late—harmful biases or misaligned responses can propagate. No lightweight, BGI-native solution exists.

Proposed Solutions

BGI ValueGuard is a PyTorch/Hugging Face plug-in that:

 

  1. Loads a BGI-maintained Ethical Anchor Set (500+ prompts testing safety, fairness, truthfulness).
  2. Runs live inference during training flags violations with SHAP-style explanations.
  3. Applies soft RLHF correction (rewards aligned outputs, penalizes drift).
  4. Exports audit logs to BGI Nexus for community review. Open-source, <5% overhead, works offline.

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