Roman Smirnov
We lifted 4 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| rgsmirnov/cfg_safety_llm | canonical | 2 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| get_cosine_schedule_with_warmup | Ran | rgsmirnov/cfg_safety_llm/train_orpo.py pointer only (licence: NONE) · get_code("268d8edf04ec9d98") |
| read_jsonl | Ran | rgsmirnov/cfg_safety_llm/train_orpo.py pointer only (licence: NONE) · get_code("435e1bfd0feee04a") |
| answer | Not yet run | rgsmirnov/cfg_safety_llm/model_eval.py pointer only (licence: NONE) · get_code("350d2baec2089c5c") |
| answer | Not yet run | rgsmirnov/cfg_safety_llm/inference.py pointer only (licence: NONE) · get_code("f3c09a516508c333") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
This article is an extended version of the NeurIPS 2024 LLM-PC submission that was awarded the second prize. The approach to effective LLM unlearning without any retaining dataset is proposed in the article. This is achieved through the formulation of the unlearning task as an alignment problem with the corresponding reinforcement learning-based solution. Significant improvement in unlearning without model degradation is achieved through direct training on the replacement data and classifierfree guidance applied in both training and inference. Sections 4 and 5 of the article were added after the NeurIPS 2024 LLM-PC competition and are focused on data ablation study and enhancements to classifier-free guidance implementation for large language models.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2412.06846")
get_code_for_paper("2412.06846")
have("2412.06846")
Connect an agent — have() is free.