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This paper resolves a debate on gradient-based data attribution methods for large language models by demonstrating that they primarily track answer format rather than task semantics, challenging their reliability in targeted instruction tuning.
Ramp developed a semantic layer to attribute AI agent spend to objectives and outcomes, moving from monitoring spend to understanding AI ROI.
This paper demonstrates that a small coordinated Wikipedia editing campaign can measurably shape how language models handle topics, using animal welfare as a case study.
DRIFT proposes a method that uses on-policy influence functions to refine training data distribution for supervised fine-tuning of large language models, consistently improving performance ceilings over existing baselines.
The paper proposes NULLs (Natively Unlearnable LLMs), a model class that isolates source-specific contributions in sparsely activated sinks while sharing backbone neurons, enabling clean unlearning of individual data sources without retraining and preserving general language capabilities.
GRASP introduces a geometry-aware, interaction-based method for scalable pretraining data attribution that models subset dynamics, outperforming existing additive approaches by over double the task-level rank correlation while reducing computation costs.
This paper provides the first systematic analysis of error sources in trajectory-based data attribution methods, identifies optimizer mismatch as the dominant error, proposes AdamW-influence to address it, and offers practical guidelines for data selection via a K-step look-ahead framework.