HFEPX · Eval paper review
Sayed Mohammad Vakilzadeh Hatefi, Maximilian Dreyer, Reduan Achtibat, Patrick Kahardipraja +4 more
Published
Jun 16, 2025
Citations
0
Trust level
Low
Usefulness score
0/100 (Low)
Extraction confidence
20% (Low)
Derived from extracted protocol signals and abstract evidence.
Rater population
Not reported
Signals refreshed
May 7, 2026
This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.
Use this as background context only. Do not make protocol decisions from this page alone.
All signals on this page are inferred from the abstract only and may be inaccurate. Do not use this page as a primary protocol reference.
Best use
Background context only
Use if you need
Background context only.
What to verify
Validate the evaluation procedure and quality controls in the full paper before operational use.
Main weakness
This paper looks adjacent to evaluation work, but not like a strong protocol reference.
Treat as adjacent context, not a core eval-method reference.
If you are doing eval pipeline work, start here
Large Language Models (LLMs) are widely deployed in real-world applications, yet their internal mechanisms remain difficult to interpret and control, limiting our ability to diagnose and correct undesirable behaviors. Mechanistic interpretability addresses this challenge by identifying circuits -- subsets of model components responsible for specific behaviors. However, discovering such circuits in LLMs remains difficult due to their scale and complexity. We frame circuit discovery as identifying parameters that contribute most to model outputs on task-specific inputs, and use Layer-wise Relevance Propagation (LRP) with reference samples to attribute and extract these components via pruning. Building on this, we introduce contrastive relevance to isolate circuits associated with undesired behaviors while preserving general capabilities, enabling targeted model correction. On OPT-125M, we show that pruning as little as ~0.3% of neurons substantially reduces toxic outputs, while pruning approximately 0.03% of weight elements mitigates repetitive text generation without degrading general performance. These results establish attribution-guided pruning as an effective mechanism for identifying and intervening on behavior-specific circuits in LLMs. We further validate our findings on additional small-scale language models, demonstrating that the proposed approach transfers across architectures. Our code is publicly available at https://github.com/erfanhatefi/SparC3.
These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.
None explicit
No explicit feedback protocol extracted.
"Large Language Models (LLMs) are widely deployed in real-world applications, yet their internal mechanisms remain difficult to interpret and control, limiting our ability to diagnose and correct undesirable behaviors."
None explicit
Validate eval design from full paper text.
"Large Language Models (LLMs) are widely deployed in real-world applications, yet their internal mechanisms remain difficult to interpret and control, limiting our ability to diagnose and correct undesirable behaviors."
Not reported
No explicit QC controls found.
"Large Language Models (LLMs) are widely deployed in real-world applications, yet their internal mechanisms remain difficult to interpret and control, limiting our ability to diagnose and correct undesirable behaviors."
Not extracted
No benchmark anchors detected.
"Large Language Models (LLMs) are widely deployed in real-world applications, yet their internal mechanisms remain difficult to interpret and control, limiting our ability to diagnose and correct undesirable behaviors."
Relevance
Useful for evaluation criteria comparison.
"We frame circuit discovery as identifying parameters that contribute most to model outputs on task-specific inputs, and use Layer-wise Relevance Propagation (LRP) with reference samples to attribute and extract these components via pruning."
No benchmark or dataset names were extracted from the available abstract.
Large Language Models (LLMs) are widely deployed in real-world applications, yet their internal mechanisms remain difficult to interpret and control, limiting our ability to diagnose and correct undesirable behaviors.
Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.
Human feedback protocol is explicit
No explicit human feedback protocol detected.
Evaluation mode is explicit
No clear evaluation mode extracted.
Quality control reporting appears
No calibration/adjudication/IAA control explicitly detected.
Benchmark or dataset anchors are present
No benchmark/dataset anchor extracted from abstract.
Metric reporting is present
Detected: relevance
