Core NLP & Model Operations

Unlearning, text watermarking, continual learning, and domain adaptation work.

Core NLP & Model Ops

This theme bundles the infrastructure that keeps language models reliable once they leave the lab: unlearning, watermarking, continual learning, and domain adaptation for high-stakes verticals.

  • Beyond the Unlearning Mirage - Dynamic evaluations that stress-test multi-hop leakage and fuse in watermarking + continual-learning tactics.
  • Domain-Specific Evaluations With Real Consequences - Finance, medical, and cybersecurity benchmarks that measure impact instead of just BLEU.

Highlights

  1. Unlearning & Safety
    • Dynamic probe generation (single hop → multi-hop, alias chains)
    • Activation-pathway tracing to confirm concepts are truly removed
    • Pip package + leaderboard for reproducible stress tests
  2. Provenance & Watermarking
    • Taxonomy covering robustness under editing/fine-tuning
    • Guidance for platform operators to audit leaked generations
  3. Continual Learning & Domain Adaptation
    • Power-law learning environments that mitigate catastrophic interference
    • Domain-specific adapters for finance, clinical narratives, and more

How to use this work

  • Plug your unlearning method into the evaluator before deployment.
  • Cite the watermarking taxonomy when rolling out provenance policies.
  • Adopt the domain benchmarks when showcasing vertical LLM claims.

Questions or collab ideas? Drop a note in Discord or email-this stack is meant to be extended.