claim

active

claim:probe-based-ranking-outperforms-gradient-based-and-llm-judge-methods-at-lower-cost

Probe-based ranking outperforms gradient-based and LLM-judge methods at lower cost

Authors' claim that their approach is both more effective in reduction and cheaper than prior methods.

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

Findings (1)

finding

Probe-based method is approximately 10× cheaper than gradient-based alternatives ($30 vs $320 once trained)
supports
Cost efficiency finding: the probe-based approach costs ~$30 vs ~$320 for gradient-based methods after training.

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).

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 reduces harmful behavior by 63% via datapoint filteringfinding0.799
Primary quantitative result: probe method outperforms gradient-based and LLM-judge alternatives at lower computational cost.
A probe may achieve high performance even on representations that are not causally relevant for the taskclaim0.778
Key interpretive claim from Case Study II distinguishing probe accuracy from causal relevance
LLMs linearly represent truth-relevant information beyond the plausibility of text, as evidenced by probes trained on likely performing poorly on anti-correlated datasetsclaim0.771
Establishes that the observed linear structure is not merely a representation of text probability
Probes trained on the likely dataset perform worse than chance on datasets with anti-correlations between text probability and truthfinding0.768
Shows that truth representations are not reducible to text probability representations
MM probes trained on larger_than+smaller_than achieve lower NIE than those trained on cities+neg_cities despite higher classification accuracy on sp_en_transfinding0.763
Dissociation between classification accuracy and causal implication; training on opposites does not always help causally
Simple difference-in-mean probes generalize as well as other probing techniques while identifying directions which are more causally implicated in model outputsclaim0.759
Key methodological claim: MM probes are both competitive in accuracy and superior in causal influence
The gradient-magnitude balancing method outperforms GradNorm on NYUv2, Cityscapes, Office-31, Office-Home.finding0.758
Comparison of gradient-magnitude balancing with GradNorm.
The proposed gradient-magnitude balancing method consistently outperforms GradNorm, as it guarantees equal gradient magnitudes and considers update magnitude.claim0.757
Advantage over GradNorm.