datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
fine_grained_unlearning
Fine-Grained Knowledge Unlearning — Namesake Benchmark
A benchmark for fine-grained knowledge unlearning: can a method remove a fact
about entity X without damaging the same fact on entity Y, when X and Y
have (near-)identical names and share exactly that one attribute?
Each sample is a pair of real people who
share an identical or near-identical name,
share one career element (e.g. both are basketball players) — the fact to
unlearn on X and retain on Y,
differ on everything… See the full description on the dataset page: https://huggingface.co/datasets/ernlavr/fine_grained_unlearning.kodcode-complete_1000_qwen7b_att_iter1_att20_sol5_finegrained_at1.2_st0.7_zeroed_shapedkodcode-complete_1000_qwen7b_att_iter0_att20_sol5_finegrained_at1.2_st0.7kodcode-complete_1000_qwen7b_att_iter0_att20_sol5_finegrained_at1.2_st0.7_filtered_shapedkodcode-complete_1000_qwen7b_att_iter0_att20_sol5_finegrained_at1.2_st0.7_shapedkodcode-complete_1000_qwen7b_att_iter0_att10_sol5_finegrained_at1.2_st0.7kodcode-complete_1000_qwen7b_sol_iter0_att40_sol5_lr5e5_3ep_finegrainedkodcode-complete_1000_qwen7b_att_iter0_att40_sol5_finegrained_at1.2_st0.7kodcode-complete_1000_gpt-4o_qwen7b_att_iter0_att10_sol5_finegrained_unfilteredkodcode-complete_1000_qwen7b_att_iter1_att20_sol5_finegrained_at1.2_st0.7world_model_prelim_analysis_finegrainedkodcode-complete_1000_qwen7b_att_iter0_att20_sol5_finegrained_at1.2_st0.7_filtered_shapingkodcode-complete_1000_qwen7b_att_iter0_att20_sol5_finegrained_at1.2_st0.7_zeroedkodcode-complete_1000_qwen7b_att_iter0_att20_sol5_finegrained_at1.2_st0.7_filteredkodcode-complete_1000_qwen7b_att_iter0_att20_sol5_finegrained_filtered_strippedfine_grained_cup_baselinekodcode-complete_1000_qwen7b_sol_iter0_att10_sol5_lr5e5_3ep_finegrainedkodcode-complete_1000_qwen7b_att_iter0_att20_sol5_finegrained_at1.2_st0.7_zeroed_shapedfine_grained_hammerfine_grained_cup_overlay