finding

active

finding:probe-based-ranking-reduces-harmful-behavior-by-63-via-datapoint-filtering

Probe-based ranking reduces harmful behavior by 63% via datapoint filtering

Primary quantitative result: probe method outperforms gradient-based and LLM-judge alternatives at lower computational cost.

Source paper

extracted_from

Probe-Based Data Attribution: Surfacing and Mitigating Undesirable Behaviors in LLM Post-Training

(2026) · Frank Xiao · Santiago Aranguri

Neighborhood — ranked by edge-count

Claims (1)

claim

Probe-based data attribution effectively reduces harmful behaviors via data interventions
supports
Authors' central interpretive assertion that their method meaningfully mitigates unwanted behaviors.

Communities (3)

community

Mechanistic interpretability & model evaluation
members_of
Spans attention head decomposition, benchmark awareness, and genomic pathogenicity prediction via neural models.
Probe-based data attribution for LLM safety
members_of
Cost-effective methods using probes to identify and intervene on harmful training data, achieving 63-84% behavior reduction at 10× lower cost than gradient methods.
Probe-based training data attribution
members_of
Uses linear probes on activations to identify and filter harmful training data cheaply (~$30).

Methods (3)

method

Probe-Based Data Attribution
answered_by
Linear classifier approach applied to model activations to identify which training datapoints caused undesired behaviors in post-training.
LLM-judge methods
contradicts
Baseline comparison for data attribution; outperformed by probe-based approach.
Gradient-based data attribution
contradicts
Baseline method against which probe-based ranking is compared; more computationally expensive.

Questions (1)

question

Which training datapoints caused a specific undesired behavior to emerge during post-training?
answered_by
Core research question driving the probe-based data attribution method.

Related by similarity (8)

cosine ≥ 0.65 · no typed edge

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

Probe-based ranking outperforms gradient-based and LLM-judge methods at lower costclaim0.799
Authors' claim that their approach is both more effective in reduction and cheaper than prior methods.
A probe may achieve high performance even on representations that are not causally relevant for the taskclaim0.761
Key interpretive claim from Case Study II distinguishing probe accuracy from causal relevance
Probes trained on the likely dataset perform worse than chance on datasets with anti-correlations between text probability and truthfinding0.760
Shows that truth representations are not reducible to text probability representations
Removing four problematic data sources achieves 84% reduction in harmful behaviorfinding0.755
Key empirical result: removing four identified problematic data sources yields an 84% reduction.
Persona vector-based data filtering and LLM judge-based data filtering have complementary strengths for identifying problematic training dataclaim0.748
Author's interpretive conclusion from comparing filtering strategies
Probe-based data attribution bridges interpretability work (probes/activations) with data-centric alignment.claim0.741
Conceptual framing: integrates mechanistic interpretability tools with alignment-focused data curation.
Even fully deterministic sorting algorithms display genuine, unprogrammed cognitive skills (e.g., delayed gratification, algotype clustering) recognizable to behavior scientistsclaim0.731
Interpretive claim built directly on the sorting-algorithm findings, central to the machine/organism symmetry argument.
Label swapping on flagged datapoints achieves 78% reduction in harmful behaviorfinding0.728
Key empirical result: swapping labels of datapoints flagged by probes yields a 78% reduction.