Neurons Speak in Ranges: Breaking Free from Discrete Neuronal Attribution
Authors: Muhammad Umair Haider
Hammad Rizwan
Hassan Sajjad
Peizhong Ju
A.B. Siddique
Venue: Accepted by TMLR ()

Abstract: Pervasive polysemanticity in large language models (LLMs) undermines discrete neuron–concept attribution, posing a significant challenge for model interpretation and control. We systematically analyze both encoder and decoder based LLMs across diverse datasets, and observe that even highly salient neurons for specific semantic concepts consistently exhibit polysemantic behavior.
Importantly, we uncover a consistent pattern: concept-conditioned activation magnitudes of neurons form distinct, often Gaussian-like distributions with minimal overlap. Building on this observation, we hypothesize that interpreting and intervening on concept-specific activation ranges can enable more precise interpretability and targeted manipulation in LLMs. To this end, we introduce NeuronLens, a novel range-based interpretation and manipulation framework, that localizes concept attribution to activation ranges within a neuron.
Extensive empirical evaluations show that range-based interventions enable effective manipulation of target concepts while causing substantially less collateral degradation to auxiliary concepts and overall model performance compared to neuron-level masking.

Published in Transactions on Machine Learning Research (04/2026)
Neurons Speak in Ranges: Breaking Free from Discrete Neuronal Attribution
Muhammad Umair Haider
Deportment of Computer Science University of Kentucky Kentucky, USA
Hammad Rizwan
riamaad.rizwar dal..co
Deportment of Computer Science Dolhousie University Halifaz Canada
Hassan Sajjad
Department of Computer Science Dolhousie University Holfaa, Canada
Peizhong Ju
Deportment of Computer Science University of Kentucky Kentucky, USA
A.B. Siddique
Department of Compuler Science University of Kenlucky
Reviewed OpenReview: https: httpe://openrevieul
openreview. net/ forum? id= AukyThBBuuu
Abstract
Pervasive polysemanticity in large language models (LLMs) undermines discrete ron-concept attribution, posing significant challenge for model interpretation and control. We systematically analyze both encoder and decoder-based LLMs across diverse datasets, and observe that even highly salient neurons for specific semantic concepts consistently exhibit polysemantic behavior. Importantly, we uncover consistent pattern: concept- conditioned activation magnitudes of neurons form distinct, often Gaussian-like distributions with minimal overlap. Building this observation, hypothesize that interpreting and intervening concept-specific activation ranges enable more precise interpretability and targeted manipulation in LLMs. To this end, we introduce NeuronLeng novel range-based interpretation and manipulation framework that localizes concept. attribution to activation ranges within a neuron. Extensive empirical evaluations show that range-based interventions enable effective manipulation of target concepts while causing substantially less collateral degradation auxiliary concepts and overall model performance compared to neuron-level masking.
1 Introduction
Neuron interpretation aims to uncover how individual neurons encode semantic concepts and contribute to model outputs. Recent work has made significant progress in this direction by identifying neurons that are
Code be found https://github http:///w....aaaaaaaaaaataatreaa

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strongly associated with specific concepts model behavior (Dalvi et al. 2019a Antverg & Belinkov
2022
Conmy et al. 2023 Marks et al.) 2024h. Common approaches include maximal activation analysis (Foote et al. 2023 Frankle& & Carbin 2019). which links neurons to inputs that produce the highest activations, probe-based 2019a by that employ auxiliary classifiers to assess neuron- associations, and
methods (Dalvi et al.
probeless approaches Antterg & Belinkov p022 that infer such associations directly from neuron activations.
These methods typically rely, explicitly or implicitly, on discrete neuron-to-concept mappings, assuming that entire neurons encode single concepts. However, neurons frequently exhibit polysemanticity; the ability to encode multiple, seemingly unrelated concepts (ecomtte et al. 2024 Marshall & Kirchner, 2024). Given this heterogencous encoding of concepts, traditional approaches can lead to unintended consequences when manipulating neurons, as changes intended for one concept may inadvertently affect others encoded by the same neuron, and suboptimal interpretations of concepts (Sajjad et al. 2022
To better understand how polyscmanticity manifests at the neuron level, we conduct a systematic analysis of neuron activations in both encoder- and decoder-based LLMs across multiple datasets. Focusing on neurons identified salient for specific concepts, we observe that these neurons consistently exhibit polysemantic behavior, often responding to multiple concepts. Through qualitative and quantitative analysis, we further discover that minimal overlap across different concepts. This observation suggests that although individual neurons are polysemantic, the activations associated with a given concept tend to concentrate within a specific band of activation magnitudes. That is, each concept is typically expressed within a characteristic activation range of a neuron, even when the same neuron participates in encoding multiple concepts.
neuronal activation magnitudes form Gaussian-like distributions, with
Building on this observation, we introduce NeuronLens, range-based attribution framework for neuron interpretation and manipulation, illustrated in Figure | Rather than attribuling entire neuron to a single concept, NeuronLens identifies and maps specific activation ranges within neuron's 's distribution to individual concepts. Specifically, for a given concept, NeuronLens calculates an activation range that captures the majority of concept-conditioned activations, enabling precise attribution, which in turn allows for interventions that operate selectively within this range while leaving other aclivation regimes of the neuron unaffected. To causally validate our approach, we run extensive concept-erasure experiments. Across settings, our method roduces unintended interference on auxiliary concepts by up to 25 percentage points (-14 on Llama) than full neuron masking. Additionally, our range-based interventions preserve general capabilities of models, maintaining or improving performance on MMLU benchmark in most settings, while incurring only minor increases in language modeling perplexity on Wikipedia text.
Our work makes the following contributions. (1) We show that neuronal activations in LLMs form concept- conditioned, Gaussian-like distributions, with often exhibiting limited overlap across concepts. (2) We empiri- cally demonstrate that concept-specífic activation ranges are consistently identifiable within polysemantic neurons, providing more fine-gra -reegrained handle for neuron-level interpretation than discrete neuron-to-onncen attribution. (3) We introduce NeuronLens to interpret and causally validate concept-specifie activation ranges for targeted interventions, cnabling precisc manipulation of target concepts while reducing unintended interference compared to neuron-level masking.
2 Neuron Interpretation Analysis
2.1
Preliminaries
Neuron. We refer to the output of an activation as a neuron. In transformer model, we consider neurons of hidden state vectors of different transformer layers. Formally, given hidden state vector h' E Rd of size d produced by layer 1, h/ denotes its j-th neuron, i.c., the j-th component of h².
Concept. A concept E C is a high-level semantic category that groups each input instance (or components of every instance), where C is the set of all concepts. For example, in a language task, a sentence can be categorized into four types: declarative, interrogative, imporative, and exclamatory, where each type is a concept. Words in sentence can also convey concepts such as nouns, verbs, adjectives, adverbs, and more. We study settings where all inputs are labeled with concepts.
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I feel pretty pathetic most of the time.
I feel honored by it.
Dataset Samples
Concept Interpretation via NeuronLens
Tradi tional Concept Interpretation
Mode1 M
Pel,
Figure 1: Different concepts induce distinct, Gaussian-like like activation distributions within the same neuron, enabling range-based concept attribution and intervention.
Saliency Ranking. A saliency ranking orders the importance of neurons based some saliency metric. For hidden state vector h', denote the value of the saliency metric for the j-th neuron with respect to concept sg. The saliency ranking (ra(1),ra(2),.
.r)()) is permutation of the indices of neurons (1, d), where reli) < (r) if 59 > st. The saliency metric is usually predefined, such as absolute neuron activation values.
Concept Learning. Given hidden state vector h' as input, the associated concept can be the output of an appended neural network (e.g., several fully connected layers). The parameters of this appended neural network be trained using training samples labeled with concepts.
2.2 Datasets and Models
Datasets. For various experiments in this work, we utilize the following datasets: sentiment analysis (IMDB (Maas al. 2011)), ), (SST2 (Socher al., 2013)), emotion detection (Dair-Ai/ Aitt-AiiEmmoisss (Saravia et al. 2018)), news classification (AG-News (Zhang et al. 2015)). and article content categorization (DBPedia- the MMLU benchmark (Hendrycks et al. 2021 ) to evaluate the
14 (Zhang al.] 2015 Morcover,
general capabilities of LLMs and Wikipedia texts (Foundation) for open-ended generation. Details of concepts tested, along with examples, are provided in Appendix O Table 33 and Table 34
Models. This work employs both encoder and decoder-based models, including pretrained Llama-3.2-3B Grattaliori, 2024). fine-tuned BERT (Devlin et al. 2019), DistilBERT (Sanh et al. 2020 and GPT-2 [Radford al. 2019
2.3 Salient Neurons Extraction
We record activations for training samples of different concepts to perform neuron interpretation. Specifically, if we want to interpret neurons of h' (hidden vector at layer l), we perform a forward pass using the training dataset and store the values of h' and the associated concepts of all samples into set H!. The set H' is further partitioned into Hi for all concepts
C. Such preparation common in the relevant literature (Dalvi al. 2019c Antverg & Belinkov 2022).
We explore standard neuron saliency approaches, namely max activations Frankle & Carbin 2019 prob- less (Antverg & Belinkov] 2022), and probe analysis (Dalvi et al.] 2019b). Details of these approaches are provided in Appendix C To evaluate these saliency methods, we employ a concept erasure task that masks the neurons identified as salient by each method and measures their ability to suppress a target concept while minimally affocting auxiliary concopts. Table in Appendix [ provides the results for this experiment. Notably, all evaluated saliency methods exhibit degradation in auxiliary concepts when salient neurons for target concept are masked. One explanation for such deterioration in auxiliary concepts is due to the
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Sartind- teite tallagemation fancydwing rena 5h thothtpppo
Percenine Kuurrn in
Figure 2: Overlap in top 30% salient neurons in GPT-2 model across classes various datasets.
polysemantic neurons. That is, if individual neurons encode multiple concepts, interventions targeting a specific concept can inadvertently disrupt other concepts co-represented within the same neurons.
While any saliency method can be used to identify influential neurons, adopt mar activation ranking throughout this work because it provides the strongest targeted suppression as compared to other evaluated methods while exhibiting comparable degradation auxiliary concepts.
3 Polysemanticity
Polysemanticity often arises when models must represent more features than their capacity allows or due to specific training paradigms (Anthropic 2023). Training methods like subword tokenization, designed to reduce vocabulary size and model complexity, lead to context-dependent token splits, causing activations
to encode multiple meanings (Sennrich et al.) 2016 Elhage et al., 2022 Meng et al., 2022). Additionally, Lecomte al. (2024) show that even with sufficient capacity, weight initializations can induce polysemanticity by placing neurons near multiple conceptual regions. Irrespective of its cause, the polysemanticily of neurons, including salient neurons that encode multiple concepts, challenges the discrete neuron-to-concept attribution paradigm, which maps concept to entire neuron.
Polysemanticity in Salient Neurons. To study polysemanticity in salient neurons, we consider neuron to be polysemantic if it. appears salient for more than one concept. We calculate the overlap between the top 30% salient neurons (i.e., max activations) in a model across different classes of diverse datasets. Figure 2 presents the results on GPT-2 model using five different datasets. We observe a considerable overlap salient neurons between concepts (i.e., classes). For instance, in the case of two-class dataset, IMDB, the overlap in salient neurons is more than 60%, showing high degree of polysemanticity.
4 Neuronal Activation Patterns
The prevalence of polysemanticity in salient neurons raises natural follow-up question: how do multiple concepts manifest within the activations of single newron? To investigate this question, we analyze the activation patterns of salient neurons extracted via maximal activation, including those that exhibit polysemantic behavior. Specifically, we examine neuron activations conditioned on individual concepts across multiple datasets. Similar to Gurnee el. al. (2024). we observe that neuronal activations form Gaussian-like distributions. Importantly, our findings further indicate that for a given salient neuron, activations associated with different concepts form distinct Gaussian-like distributions with limited overlap, suggesting that concepts tend to occupy characteristic regions within a neuron's activation spectrum. In the following, we present qualitative and quantitative analyses of the concepu-conditioned activation patterns.
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Top Neuron 2587
Top Neuron 1525
0.0
Probasitty
0.2
0.01
60
Middle Neuron 1625
Middle Neuron 1251
Probaaltt Dentty
1.0
2.0
Probabirty
0.5
0.0
0.5
Bottom Nouron 103
Bottom Neuron 2927
Frocebiity Dersty
WhH
Prosubitty Dersty
Spara
L.-
1.
0.0
0.0
0.
1.0
Figure 3: Neuronal activation patterns of six neurons in the Llama model on AG-News. Top: neurons sampled from the highest-activating group; middle: from the mid-ranked; bottom: from the lowest-activating group.
Table 1: Skewness, kurtosis, and Kolmogorov-Smimnox test results across various datasets for Llama model.
Dataset
Skewness
Kurtosis
KS-Test
Seanfordulpimmdi
0.0003
3.9251
0.9995
fancyzhx/ /dbphh//ddpdddaa__
0.0068
3.9916
0.9133
dair-ai/ /elotiooo
0.0000
3.1609
0.9988
fancyshx/ag news
-0.0020
3.4079
0.9995
stanfordnlp/sst2
-0.0034
3.5890
0.9993
4.1 Qualitative Analysis
To visually demonstrate that neuron activations for concept follow Gaussian-like distribution, extract model activations described in 2 Using saliency ranking re for the datasct, examine neurons from different ranking positions in the Llama model on the AG-News dataset. In Figures 3 visualize these distributions using Kernel Density Estimation (KDE).
It reveals that the activations are Gaussian-like for different concepts, where salient neurons demonstrate distinct activation patterns with limited overlap, middll--ranked neurons show a higher degree of overlap than the top ones, whereas non-salient neurons (bottom two) exhibit the highest overlap activation distributions, Additional analysis for different types of polysemantic neurons for the GPT-2 model is provided in Appendix For example, figure G in Appendix D visualizes two distinct types of polysemantic neurons. One maintains partially separable activation patterns despite being polysemantic; the other exhibits completely overlapping activations. Figure 7 Appendix D we present a broader analysis of 7 neurons from the polysemantic subset for 14 total classes. We observe that most polysemantic neurons exhibit limited overlap between concepts at the distributional level with distinct means.
Text Analysis. We complement the activation analysis with qualitative text analysis that examines representative samples associated with different regions of concept-conditioned activation distributions. Specifically, for selected neurons showcased in Figure 3 analyze samples drawn from the lower boundary, mean, and upper boundary of their activation distributions. Table 2 presents representative examples for neurons 1525 and 2587, which are predominantly associated with Sci/Teet and Business concepts, respectively. For neuron 1525, samples near the mean the distribution focus on core Sci Tech terminology, while samples near the upper boundary reflect mix of Sei/Tech and Business concepts (c.g., "subscription plans"). In contrast, samples near the lower boundary tend to blend Sci/Tech, Sports (c.g., "marathon"), and World themes. A similar pattern is observed for neuron 2587: samples near the mean focus Business topics,
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Table 2: Semantic concepts are highlighted by class color: World (Blue), Sports (Green), Business (Orange), and Sci/Tech (Purple). For each neuron, examples are shown from the lower boundary, mean, and upper boundary of the neuron activations, illustrating how semantic content varies across activation magnitudes. The table shows shortened texts; full reference texts are provided in Appendix table
Neuron
Point
Boundary Affinity
Representative Examples (Concepts Highlighted)
"Gene Tweaking Turns... Racers...single gene turned...marathon racers that could run hours."
Lower Boundary
World /
overscas...balltt
via
Sports
mall... DDF....DD.......ee Department."
"IBM's supercomputer breaks world's fustest...NEC's Earth Simulator...bawee Yokohama, Japan."
"...powerful httpp..miicopproesoor...aaapp
video game consoles and high-
definition T-Vs."
" AMD...Dual-C. Opteron.. faster than single-core chips...ftt in existing server designs."
1525
Mean
Sci/ /e/h
"Arm reveals multimedia... Miciopoooe.eiroooeeeeessoooo desiguer..mulliimeet technology for mobile electronics."
"aass. Flat-Rate Pricing Plans for US Wi-Fi Holspot..subscripton plans for usc. connectivity."
Upper Boundary
"NTT Tokyo Stock Exchange...roocuring mobile phone handsets made by Motorola Inc."
Busineas
"Omnipod..coopant of all sizes...nhanneeents... Web-based client.... persistent- chat feature."
"Put in, Coach!. Coach joins stock market reacts."
be0...leather the as global "The London Stock Exchange plans...Asia headquarters in Hong Konng..eycing stock listings abroad."
Lower Boundary
World / Sports
"Hallbuuton closes higher... Army's decision..shares closed higher..conttract to supply troops Iraq."
"UBS...capite markets unit for $265 million in cash, strengthening,.. brogkeeennnn...bokrrggeeee business"
"Applied Materials.. shares were off... trading... wavered around breakseven... resulta announced."
2587
Mean
Business
(Brown-Forman..3
carnings aggressive marketing boosted sales of
premium spirits."
*Arm to p9y...9110m in cash and shares tra.ttranssttorlleeeell designer for systems- on-a-chip"
Upper
"Oracle sales rise database demand... focus... obtaining CRM and ERP software..."
Sel/Tech
Boundary
"The..information technolog....rrdncivvvy gains, business software, and telecommunications... ..."
transition toward World-related content near the lower boundary (c.g., "Asia headquarters"), and shift toward Sci/Tech concepts near the upper boundary (e.g., "database demand"). These examples indicate that changes in activation magnitude correspond to semantic transitions, providing qualitative evidence that different concopts occupy characteristic regions within a ncuron's activation spectrum. Additional details of the text analysis are provided in Appendix
4.2 Quantitative Analysis
For cach neuron, we collect activation values over samples associated with concept e E C' and compute summary statisties that capture deviations from normality. Specifically, we measure skewness and kurtosis (Joanes & Gill 1998), and assess goodness-of-fit of-fit to normal distribution using the Kolmogorov Haanogoroo--Sminov Smirnov (KS) test (Masscy Jr. 1951p. Table [ presents the results across all neurons in Llama model. The average skewness is close to 0 across all datasets, indicating strong symmetry (ideal normal distribution: 0), and the average kurtosis is close to 3, nearly identical to the expected value for a normal distribution (3.0).
To assess normality, while accounting for practical significance, we employ the KS test with an effect size threshold of 10%. This approach tests whether the distribution remains within reasonable bound of normality, rather than testing for perfect normality, which is overly strict for real-world data. For each

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neuron, normalize the activations to zero mean and unit variance, then compute the KS statistic against standard normal distribution. The KS statistic represents the maximum absolute difference ween the empirical and theoretical cumulative distribution functions. Using a threshold of 0.1 (allowing a maximum 10% deviation from normal), we find that close to 100% of the neurons exhibit practically normal distributions. The combination of near-ideal skewness and kurtosis values, visual confirmation through KDEs, and our effect. size-based KS tests provides strong evidence that the concept-conditioned activations follow approximately normal distributions.
Additionally, we report layer-wise quantitative statistics for the GPT-2 model in Appendix As layer depth increases, kurtosis steadily converges toward the Gaussian reference value of 3.0, skewness remains near zero, and the 10% proctical-normallity score stays close to 1 across the network. A qualitative, layer-wise Gaussian-lis activation patterns are present
analysis in Appendix|F2. | shows that while across all layers, early layers exhibit substantial overlap between concepts.
Beginning as early as layers 5 6, distinct concept-specifie Gaussians emerge and become progressively more separable in later layers, transitioning toward class-specific semantics.
Additional statistics for pairwise AUROC are provided in Appendix D.1 Table 5
5
NeuronLens
Given that neuronal activations exhibit approximately Gaussian-liks like,, concept-conditioned distributions with distinct means, we can interpret and intervene on neurons more precisely by operating activation ranges rather than attributing entire neurons to individual concepts. This perspective applies regardless of whether neuron is monosemantic polysemantic, it leverages neuron's activation spectrum. To operationalize this idea, we propose NeuronLens that enables range-based attribution and targeted intervention on neurons.
5.1 Range- based Attribution
NeuronLens computes attribution ranges with respect to a concept using means and standard deviations of the neuronal activations. Specifically, given H' that denotes the set of hidden state vector h h1(x)) at layer for all training samples Ze associated with concept E C, it can calculate the empirical average μ E R and standard deviation σ 2 0 of the activation values of the j-th neuron h² for all samples associated with the concept as:
11.0=
Σ h'EH:
04
12)2
H
H
b'EH'
Then, it can attribute the activation range of the j-th neuron at layer I to the concept as:
AR(Lj.c) = [p - Txo, u+
where > 0 is hyperparameter which controls the attribution scope. Intuitively, a smaller yields a narrower, conservative range, while a larger 7 expands coverage at the risk of including less concept-specific activations. AR is defined as attribution range associated with a given concept c.
5.2 Causal Validation
Causal validation is among the most faithful ways to assess attribution correctness (Feder et al. 2021 Vig et al. 2020), it directly tests whether intervening on the attributed component produces the intended behavioral change, while mcasuring unintended side effects. Following prior intervention-basee validation Dalvi et al. 2019b Dai et. al.) 2022 Dalvi el al.) 2019c Morcos et al., 2018), we utilize concept erasure as a diagnostic intervention to determine the causal effect of identified ranges within neurons for given concept. The core idea is that an attribution is salient to concept, climinating should result in the degradation of model's performance on that concept while causing minimal disruption to other concepts. Formally, given concept-learning model M that maps any input instance (or part of instance) to
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concept ((x E C, an ideal intervened model Mideal after crasing target concept C should satisfy the following property:
M(x) if M() =; M(a) if (() #c.
Mideal
(1)
NeuronLens enables precise manipulation of target concepts via identified activation ranges in contrast to the manipulation and ablation of complete neuron activations. Specifically:
((z) (1(x)) otherwise
1f E AR((.
h)(x)
(2)
where o(.) is user-specified range-gated activation operator applied only when 1)(x)) A.(), In general, 0 can be any function, instantiated for causal intervention (e.g., clamping, zeroing, scaling) or for aatiiiionnnttiiuttionn (e.g., masking or tagging activations). For concept erasure, the gated function o(.) zeroes out which is line with Dai et al. (2022); Antverg & Belinkov (2022). Some studies have argued that zeroing out neurons is an overly aggressive intervention that can lead to catastrophic degradation in model performance. In Appendix G compare alternative activation interventions, including the dampening method (Suau ct al. 2024) and mean replacement (Suau et al. 2021). Erasure experiments in the main text ablating specific range of neuronal activation are referred as range-based masking, whereas ablating the complete neurons is referred as neuron masking.
5.3 Experimental Setup
We incorporate our intervention at the penultimate layer. Ablation for layer selection is provided in the Appendix F The training details for the fine-tuned models are provided in Appendix
Metrics. We causally validate the attribution using two metries: prediction accuracy and the model's predictive probability
proxy for confidence score. First, baseline measurements of both accuracy and confidence for all concepts C without any intervention (unmodified model) are established. Post-intervention measurements are recorded for the target concept and auxiliary concepts (other concepts in the dataset) d. The offectiveness and precision of attribution are asscssed through two key metrics: (1) the performance degradation for concept c, and (2) the extent of unintended impact on auxiliary concepts c'. Throughout our analysis, we denote the accuracy and confidence metrics for concept as Acc and Conf respectively, while corresponding measurements for auxiliary concepts d' are represented as CAcc and CConf. For evaluating the effect of the interventions LLMs latent capabilities, utilize perplexity (PPL) and zero-shot accuracy on MMLU.
Hyperparameter. The value of T is set to 2.5, assuming a fully Gaussian distribution. This threshold corresponds to coverage of approximately 98.76% of the distribution, providing a slightly conservative bound for range-based interventions. Ablations for varying T are presented in Appendix [ The results indicate that targeted concept deteriorates up to 2.4-2.7 then platcaus, while auxiliary concepts begin to degrade further.
5.4 Results and Analysis
Table presents results for the concept erasure task across five benchmark datasets (Class-wise detailed results provided in Appendix Jh, demonstrating the effectiveness of range-based masking compared to traditional neuron masking. Results in Table 3 show that multi-class classification tasks with finc-grained labels, such as AG-News, Emotions, and DBPedia-14, exhibit more pronounced effects under intervention. Basge-based masking results in significant degradation of primary task performance while preserving auxiliary concept accuracy. This is particularly evident in results for AG-News. On binary classification tasks (IMDB, SST2) provided in Appendix J (Table 25 and (24). both masking approaches show moderate performance drops in targeted concepts. This suggests higher redundancy for coarser binary concepts.
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Table 3: Evaluation selected models on AG-News, Emotions, and DBPedia-14 14 datasets using activation range and neuron masking techniques. Performance metrics are calculated using class level 10% trimmed means at the class level. Metrics are detailed in $ 5.3 For GPT-2 and BERT 50% and for Llama-3.2-1 30% neurons are selected.
Dataset
Neuron Masking
Activation Range Masking (ARM)
Acc
Conf
CAcc
CConf
AAcc
AConf
ACAcc
ACConf
AAce
AConf
ACAcc
&CConf
AG-News
0.948
0,9229
0.948
0.929
-0.271
40.59U
0.012
-0074
.0261
-0.390
0.013
-0.009
BERT
Emotions
0.894
0.834
0.917
0.876
00.29
-0.633
0.013
-02655
0..79
-0.335
0.014
-0.069
DBPedia-14
0.992
0.991
0.950
0.983
-0.028
-0.78G
0.000
-0.017
-0.015
40.766
0.000
-0.000
AG-News
0.945
0.933
0.945
0.933
-0.871
-0.877
-0.155
-0.163
-0.849
-0.862
-0.063
-0. -0.223
GPT-2
Ematioon
0.905
0.802
0.930
0.919
-0.735
-0.738
-0.103
-0.103
-0.737
-0.739
-0,044
-0.046
DBPedia-1e
0.993
0,990
0,990
0,988
-0.810
-0.144
-0.177
-0.722
-0.825
-0.015
AG-News
1,000
0.744
1,000
0.744
-0.934
-0.725
-0.650
-0.572
.0.935
-0.725
-0.484
-0.454
Llama
Emotions
0.815
0.472
0.823
0.477
-0.795
-0.470
-0.096
-0.429
-0.797
-0.489
-0.094
-0.404
DBPedia-14
1.000
0.533
1.000
0.563
-0.992
-0.528
-0.912
-0.445
-0.986
-0.527
-0.663
-0.354
Table 4: Evaluation of latent capabilities of Llama model after applying neuron and range-based masking. Base PPL = 7.007; Base MMLU = 0.53.
Dataset
Neuron Masking Perplexity
ARM
MMLU
Perplexity
MMLU
AG-News
12.757
0.510
8.022
0.533
Emotions
11.630
0.526
8.063
0.526
DBPedia-11
12.230
0.507
7.903
0.535
GPT-2, despite being fine-tuned but trained in autoregressive manner, shows substantially higher vulnerability with major drops in AG-News (Ance = -08449) and DBPedia-14 (Aace = -0.782). This increased sensitivity may be attributed to its autoregressive training objective, which potentially leads to more sequential and less redundant concept encodings. The Llama-3.2-3B model, evaluated in a few-shot. setting without task-specific training, experiences the most severe degradation across all datasets (often exceeding -0.90), suggesting that pre-trained representations without task-specific fine-tuning are more vulnerable to full neuron interventions.
General LLM Capabilities. To ensure that approach does not compromise general model capabilities, evaluate the impact of range-based masking on language modeling quality and broad knowledge and reasoning performance using the MMLU benchmark. Table presents the comparative performance of neuron masking and activation range masking. Full neuron masking leads to notable increases in perplexity, whereas range masking results in substantially lower perplexity increases.
terms of MMLU accuracy, neuron masking consistently reduces performance across all settings, while range masking preserves or improves performance in most cases, with degradation observed in only one instance.
Alternative activation interventions. We also experiment with alternative activation interventions. Details of the alternative approaches are provided in Appendix G While dampening and mean replacement methods aim to manipulate without moving too far from the original representation, they exhibit limitations when applied to neurons. Specifically, neuron dampening increases perplexity by 2.9-3.7 points and often degrades MMLU accuracy (up to -0.045), whereas range-based dampening confines perplexity increases 0.5-0.8 points and occasionally improves MMLU (up to +0.035). Similarly, mean replacement leads to substantial degradation when applied to neurons (perplexity increases of 7.4 8.8), while range-based replacement reduces the impact to below 0.7 points.
However, both dampening and mean replacement methods suffer from rigid static suppression or substitution, failing to account for concept-specific activation dynamics. To address this issue, we introduce a novel adaptive dampening technique. This method modulates suppression in proportion to each activation's deviation from its class-conditional mean, enabling suppression guidod by sampled observations. Adaptive dampening achieves the strongest balance across all metrics: perplexity remains low (0.41-0.61), MMLU is maintained improved (up to +0.03), and collateral damage to auxiliary concepts is minimized (CAce drops
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Ag-Neev
Emotions
DBPadia-14
8096
0094
5074
Accurncy (%)
Accuracy (9)
80%
60%
60%
Accuracy
2096
Horg: Marving
heurs Nasking KAC
20%
2074
Rarge Maciin Clkcs
haaeon Maslan CAaE
090
10
20
30
d0
0R
40
600
70
80
072
Percentage Masked
Percentage Masked
Percentage Masked
Figure 4: Accuracy on the GPT-2 model as a function of the percentage of masked neurons, comparing activation range-based masking (green) and neuron-level masking (orange).
consistently below -0.3, often under -0.15), outperforming dampening, mean replacement, and zeroing out approaches.
These results demonstrate that precise intervention in specific activation ranges enables substantially more targeted concept manipulation while preserving auxiliary concepts, highlighting how conceptual information is encoded within specific activation patterns rather than isolated to individual neurons, underscoring the importance of activation ranges in capturing neuron-concept relationships.
Percentage Masking Effect. As the masking rate increases, the advantage of range-based masking over the neuron-masking baseline becomes increasingly pronounced. Beyond critical masking level, the baseline exhibits a sharp performance collapse, whereas our method avoids this cliff and degrades smoothly all the way to full masking (100%). This arises from two factors: (1) models have a large number of polysemantic neurons, and higher masking rates increase the chance of ablating them, and (2) blocking blocking/manipullann maaipulatiiniiinggggg a higher percentage of the model's neurons creates a signíficant deviation from the original model's ' behavior. For low-activation neurons with respect to the concept of interest, discrete neuron masking (i.e., completely masking out neuron) becomes unreliable, as shown in Figure 4 with steep performance drop after masking 50% of neurons. Notably, our range-based method offers finer-grained attribution, preserving model behavior under extensive masking. The relatively stable results on auxiliary concepts when using aane--bascd masking at a high percentage of neurons reduce the need to find an optimum threshold for the number of neurons lo ablate, which is critical to neuron masking.
Additional results and discussion for activation stecring are provided in Appendix M We find that activation range gating is also helpful in activation steering.
Additional results for Sparse Auto (ncoders(SAEs)) are provided in Appendix N We find that activation range masking substantially outperforms full feature masking, ARM produces similar deterioration to full feature masking, while producing lower drops in complementary concepts, and substantially lower perplexity increase.
6 Related Work
While we have discussed closely related approaches in 2 we briefly review additional relevant techniques here. Circuit discovery identifies groups of neurons that jointly encode concepts, providing structured view of model behavior (Marks et al. 2024 Conmy et al. 2023 Olah et al. 2020). However, extracting circuits is computationally intensive and lacks fine-grained neuron-level attribution. Gradient-based methods autribute predictions to input features by tracking gradients through the network, with integrated gradi- ents (Sundararajan et al.) 2017; Dai et al., 2022) being a widely used approach. However, they struggle with polysemanticity, as they do not disentangle overlapping concepts within neurons. Causal analysis methods intervene on internal components to assess their role in encoding concepts. Causal tracing measures the
effect of corrupting activations on model performance (Vig et. al. 2020 Meng et. al.] 2022), while causal
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mediation analysis quantifies information propagation through neurons (Vig et al. 2020 Although effective, these techniques require costly perturbation experiments. Beyond neuron-level analysis, representation-leve methods examine hidden states and their relationship to model outputs and concepts (Veldhoen et al.) 2016 Tenney et al.) 2019 Liu et al. 2019p. Sparse probing (Gurnee et al. 2023b) compresses hidden reprosentations into sparse, interpretable subspaces. While prior work has advanced interpretability, most. methods rely on discrete couroe-to-concept mappings, which fail to account for polysemanticity (Sajjad et al.; 2022p. Our work extends activation-based approaches by characterizing concept-conditione activation distributions and identifying concept-specift activation ranges that enable more precise attribution and intervention than discrete neuron-ta-concep. mappings.
While related efforts have analyzed polysemanticity by grouping neuron activation behaviors, they typically rely on clustering heuristics rather than modeling the underlying distributional structure of activations. La Rosa et al. (2023) record activations of vision models across concepts and apply K-means clustering to cluster activations per concept using a fixed number of clusters across settings. Kopf al. (2025) study polysemanticity by collecting inputs that elicit the highest activations for neuron, clustering these top- activating imputs, and using LLM to summarize the resulting clusters measure of polysemanticity. In contrast, we study polyscmanticity through the distributional analysis of concept-conditioned activations and derive concept-specific activation ranges that facilitate fine-grained, range-based interpretations.
2022 a complementary line of work Motivated by the superposition view of representations Elhage et al.
builds on sparse dictionary learning (Olshausen & Field 1997 Lee et al. 2006 and trains sparse autoencoders
(SAEs) to learn sparse, monosemantic features from model representations YYun et al., 2021 Cunningham et al. 2023 By constructing learned sparse basis, SAFs often yield features that casier to interpret than those obtained by analyzing the original model representations. NeuronLens is complementary to this line of work. Rather than learning an additional basis, NeuronLens interprets and intervenes on existing model representations by exploiting ranges in concept-condittonee activation distributions, enabling efficient analysis of polyscmantic neurons without additional training. Morcover, NeuronLens can complement SAEs by applying activation-range analysis to SAE features that are nol completely monosemantic. While SAEs can be powerful, training them can be compute-intense intensive and poses practical challenges, including dead, duplicate, missing features and sensitivity to initialization (Bricken et al. 2023 Bussmann et al. 2024 Paulo & Belrose 2025 Beyond SAEs, our Lunge-basces analysis is compatible with broader efforts in interpretation
and control, such as representation engineering (Zou et al. 2025) and steering (Tan et al.. 2024
Chalney et al.,
2024 Subramani et al. 2022). We view integrating activation-range analysis into these control paradigms as a promising direction for future work.
7 Conclusion
In this work, we revisited neuron-level interpretability through the lens of polysemanticity and showed that, despite encoding multiple concepts, individual neurons exhibit predictable activation behavior. Through systematic qualitative and quantitative analysis, we demonstrated that concept-conditiones neuron activations form Gaussian-lik distributions with limited overlap. Building on this observation, we introduced NeuronLens, range-based framework that attributes concepts to activation ranges rather than entire neurons. Leuron enables more precise concept manipulation by selectively intervening within these ranges while substantially reducing unintended interference with auxiliary concepts. Our causal evaluations show that range- based masking consistently outperforms full neuron masking and preserves general model capabilities, including broad knowledge and reasoning performance measured by MML.U, as well as language modeling quality.
8 Acknowledgment
This work was supported in part by the National Science Foundation (NSF) under grant IIS-2401685 and UNITE Research Priority Area at the University of Kentucky.
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A Limitations
While NeuronLens can disentangle polyscmanticity to degree using identified Gaussian-like distributions, it is unable to completely disentangle concepts encoded in the polysemantic neurons, as concept-specifi activation distributions still partially overlap. We also present results primarily from the penultimate layer, and not the intermodiate or carlier layers; however, we provide ablations and rationale for this choice Appendix F
B Impact Statement
This work advances neural network interpretability by providing fine-grained understanding of concept encoding in language models. The proposed NeuronLens framework enables precise control of model behavior, benefiting research in model safety and reliability. While this improved understanding could potentially be misused, the work's theoretical nature and focus on interpretability methods make immediate harmful applications unlikely.
C Details of Saliency Methods
Max Activations (Frankle & Carbin 2019). Max activations extract high neural activations as a saliency ranking metric relying upon the rationale that maximally activating neurons are salient as these neurons play a critical role in controlling the model's output, highlighting their importance for a concept c. To identify them, the column-wise mean of absolute neuronal activations in H H is defined in $2.3 is computed, given that high negative activations also carry significant signals (Voita et al. 2023 The magnitude of the means is then considered as a ranking for concept c.
Probe Analysis (Dalvi et al. 2019b). Probe analysis trains a linear classifier on the hidden representations Hi to distinguish between concepts. The learned model weights are then utilized as a saliency ranking. This process involves learning a weight matrix E [dd where d is the hidden dimension and |c] is the number of concept classes. The absolute weight values of each row in the weight matrix are used as ranking for the importance of each neuron for given concept. To prevent the emergence of redundant solutions characterized by minimal variations in the weights, the probe is trained using the elastic regularization technique.
Probeless (Antverg & Belinkov 2022). Probeless examines individual neurons, without the need for auxiliary classifiers, using the element-wise difference between mean vectors. The dement-wiso difference between mean vectors is computed as r = Ecdec 1q(c) q(c'), where r E Rd and d is the hidden dimension. The final neuron saliency ranking is obtained by sorting descending order.
C.1 Causal validation
To causally validate the aforementioned approaches, we apply erasure; the results for this are provided Table The results show that degradation of the target concepts is highest when using max activations.
D
Qualitative and Quantitative Analysis on GPT-2
D.1 Qualitative
Following similar experimental setup used in $ here we provide the results for the GPT-2 model. Figure and 6 show these visualizations. Figure similar to the Llama, shows that while the activations are Gaussian-like for different concepts, salient neurons demonstrate distinct activation patterns with limited
overlap.
Figure [ we identify and visualize two distinct types of polysemantic neurons that appear in the salient sets across all classes, when 5% salient set was selectod, in the datasct. The first type, exemplified by neuron 480, maintains partially separable activation patterns despite being polysemantic, suggesting some degree of class-specific behavior. In contrast, the second type, represented by neuron 675, exhibits completely
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Table 5: Distribution of pairwise overlaps across quartiles (Q1 Q4) for different models and datasets. Q1 includes overlaps <25%, and Q4 includes overlaps 75%. The final row for Llama-3-33 3-ma- reports results for all data aggregated for overlap analysis.
Model
Dataset
Q1 (< 26%)
Q2 (25% 50%)
Q3 (50%
73%)
Q4 (> 75%)
DB-14
85,90%
6.15%
4.19%
3.76%
BERT
Emations
56.11%
17.87%
14.18%
11.83%
AG NeNs
52.26%
20.01%
14.80%
12.93%
DB-14
66.70%
13.53%
10.39%
9.38%
GPT-2
Emotions
21.95%
25.72%
26.46%
25.87%
AG_ News
22.94%
26.97%
25.74%
24.35%
DB-14
26.75%
25.41%
24.17%
23.67%
Llama-3-3.2B
Emotions
4.41%
19.72%
34.13%
41. 74%
AG News
17.93%
26.46%
27.53%
28.00%
43.38%
20.15%
18.46%
18.01%
Table 6: Performance drops relative to Baseline configuration (i.e., unaltered model's performance) for three techniques: Probeless, Probe, and Max. All values show the difference from Base values. Results are for Emotions dataset on the GPT-2 and Llama-3-3.2B model using 30% salient neurons of each method. Metries are detailed in 5.3
Model
Probeless
Probe
Max
AAcc
AConf
ACAce
ACConf
AAce
AConf
ACAce
ACConf
AAce
AConf
ACAce
ACConf
GPT-2
-0.524
-0.510
-0.06
-0.866
-0.052
-0.036
-0.018
-0.049
-0.735
-0.739
-0.103
-0.103
Llama
-0.751
-0.292
-0.751
-0.272
-0.177
-0.145
-0.169
-0.096
-0.805
-0.500
-0.511
-0,399
overlapping activation patterns across all classes, making it hard to disentangle. To further investigate this phenomenon, Figure presents a broader analysis of neurons from the polysemantic subset, identified using a 5% saliency threshold (top 5% salient neurons selected for a concept).
D.2 Quantitative
Skewness and Kurtosis, and practical normality results on GPT-2 model are provided in Table 7 The results are similar to those observed on the Llama model in the main text.
Table provides results regarding neuron activation overlap various concepts using AUROC, considering all possible pairwise combinations of concepts within datasets.
E
Qualitative Text Analysis
After recording the activations and calculating distributions, we calculate the activation range (as defined 5.1 for each class in the dataset under consideration. We then extract 10 text examples at each of three points along the computed activation range for each concept: the lower bound (minimum of the activation range), the midpoint (mean of the activation range), and the upper bound (maximum of the activation range).
We then pass these text examples to an LLM judge (Gemini Pro) along with class labels and boundary position information. The LLM judge then extracts the underlying semantics shared the examples from one of the labels of the dataset, along with highlighted keywords. We present representative examples from each set for one selected class, along with LLM highlighted keywords and semantics in the Table 2 the full relevant texts for the table provided in Table
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Table 7: Skewness, kurtosis, and Kolmogorov- test results across various datasets. GPT-2 model
Dataset
Skewness
Kurtosis
KS-Test
stanfordnlp/ /imdb
0.0011
3,6639
1,0000
fancyslxx /bppddaa 14
-0.0007
3.9360
0.9782
dair-al /emotion
0.0015
3.0198
0.94416
fancyzhx/ /ag news
-0.0013
3.2060
0.9918
stanfordulp/ssst
-0.0083
3.2038
1.0000
Probability Density
Top Neuron 481
Top Neuron 447
100
200
300
400
500
100
200
300
400
500
600
700
Activation Value
Activation Value
Middle Neuron 132
Middle Neuron 387
-30
-30
-10
Activation Value
Activation Value
Bottom Neuron 721
Bottom Neuron 365
-15 -10
10
-20
-10
10
Activation Value
Activation Value
Figure 5: Neuronal Aclivation Patterns of six neurons AG-News dataset class 1. Neurons 481 and 447 are the highest activating neurons, neurons 132 and 387 are middle-ranked neurons, and neurons 721 and 365 are the lowest activating neurons.
F
Layer Ablation
F.1 Statistical Results
We analyze concept level activation distributions across all 12 layers of GPT-2, measuring kurtosis (where a value of 3.0 indicates a Gaussian distribution), skowness (where O indicates symmetry), and practical normality in Table g
These results show that kurtosis values converge toward 3.0 (the Gaussian ideal) as layers progress, skewness values remain near across all layers, and practical normality scores close to across all layers. Importantly, if the activations were not clustered into continuous intervals and were in disconnected islands of activations, these would be reflected in the score for the practical normality and other statistical metrics.
F.2 Qualitative Results
We expanded our visualization approach, shown figs. S to demonstrate an interesting progression: while all layers exhibit Gaussian- distributions on the class level, class concepts aren't. separated in the activation spectrum Gaussians of the early layers. This aligns with the
all layers the model. The visualizations
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Probability Density
Cless
Class
Class
Gaussian fits
-50
Activation Value
Neuron 675
10
20
30
40
50
Activation Value
Figure 6: Neuronal Activation Patterns comparison of neurons 480 and 675. The plots show class-specific activity patterns with fitted Gaussian curves. Both neurons were part of salient set of all classes when top 5% salient selected on AG-News dataset.
Activation Box Plot Classes
Clsss
Alzum
Atimal
Natural
Officche de"
Normatee Actiwaloo Velee (0-1 saae
Hanc:
Vilbge
64
87
264
373 Neuron Index
467
480
461
Figure 7: Box plot of neural activation of 11 polysemantic neurons (Lee neurons in the salient group for all classes, percentage selected: 5% top salient) for 4 randomly selected classes out of 14 classes of DBPedia-11 dataset.
understanding that lower layers capture more basic features rather than high-level semantic features like class. However, distinct concept-level Gaussian distributions begin forming as early as layers 5-6, becoming increasingly separable in deeper layers.
F.3 Masking Results
In Table 10 and Table D we provide results of applying both approaches on all layers of GPT-2 model on Emotions dataset. From the results we can see that: Early layers (1-3) show highly variable and often severe impacts: Layer 1 exhibits minimal effects (A 001133, ACAcc 0.064), while Layers 2-3 show extreme degradation AAAce -0.7, ACAce > 0.55.. Middle layers (4-8) demonstrate inconsistent behavior with high variance in impacts. Layer 12, however, achieves an optimal balance: it maintains substantial primary task
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tou
5.4
-00
Neae
Coear Sa
Oedi NEw
Ordaa Now 91:
130
-0
-01
touen
Eatoon Near 399
Satee Noure
bubce Neare
14-
254
Figure 8: Neuronal Activation Patterns of six neurons AG-News datasel. Layer 1
Figure 9: Neuronal Activation Patterns of six neurons on AG-News dalaset. Layer 2
Deat
AGR
Abdle
1.2
135
Neuronal Activation Patterns of six nen- rons on AG-News dataset. Layer 3
Figure 10:
Figure 11: Neuronal Activation Pattorns of six neu- rons on AG-News dataset. Layer
impact AAAce = 0.571) while minimizing auxiliary concept interference (ACAcc = -.060).. This pattern holds true for both neuron masking and range masking techniques, with range masking showing slightly better preservation of auxiliary concepts (ACAce -0.045). The mid-range primary task degradation combined with minimal auxiliary impact makes Layer 12 the most suitable for targeted interventions, offering better control and specificily compared to earlier layers.
G Alternative Activation Interventions
In the main text, we primarily presented results using "zeroing out" stratogy for neuron manipulation. This approach was chosen to compare neuron manipulation against range-based manipulation. However, zeroing out is considered a suboptimal strategy (Suau et al.) 2024). The primary concern with standard zeroing-out approaches that they distort the activation distribution significantly, diverging from that of the original model. However, our range-bases method selectively zeroes out only a narrow slice of the activation spectrum, thereby mitigating the adverse effects associated with hard erasure.
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to Newon
Meuse 114
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Figure 12: Neuronal Activation Patterns of six neu- rons AG-News datasel. Layer 5
Figure 13: Neuronal Activation Patterns of six neu- rons on AG-News dalaset. Layer 6
300
4:11
115
317
P06
6.33
Allee
Figure 14: Neuronal Activation Patterns of six nen- rons on AG-News dataset. Layer 7
Figure 15: Neuronal Activation Patterns of six neu- rons on A AG-News dataset. Layer
In this section, we explore alternative, more optimized strategies for concept removal. We also introduce a novel range-based scaling strategy that has demonstrated superior results.
Below, we explore various activation intervention strategies, comparing traditional neuron-level approaches with the nuanced rasee technique. Our comprehensive evaluation reveals that rasge-based manipulations consistently outperform neuron interventions across multiple metrics, with significantly less disruption to the model's general capabilities.
Among all techniques examined, our novel adaptive dampening approach emerges as the most effective, maintaining targeted concept suppression while minimizing collateral impact on auxiliary concepts and preserving overall language modelling capabilities. This pattern holds true across different intervention methods including zeroing out, dampening, and mean replacement rtteggie
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Ta CR1
Neuuce 201
Dest
0000
-1
DM
Eer Smuse 937
Ceet: RANcE 122
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0.5
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Figure 16: Neuronal Activation Patterns of six neu- rons AG-News datasel. Layer 9
Figure 17: Neuronal Activation Patterns of six neu- rons on AG-News dataset. Layer 10
2009
353
334
Cor
350
312
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Figure 18: Neuronal Activation Patlerns of six neu- rons on AG-News dataset. Layer 11
Figure 19: Neuronal Activation Patterns of six neu- rons on AG-News dataset. Layer 12
G.1 Dampening
In their work, Suau et al. 2024) propose using a dampening function rather than setting neuron activations to zero outright. This approach, referred to as DAMP, corresponds to a specific choice of the intervention function (xx) = ax, where S a S 1. In this formulation, the activations of selected ncurons are scaled down by a factor a instead of being completely suppressed. Here, & represents neuron activation. The rationale behind dampening is that fixed intervention (like zeroing out) can disrupt the LLM's inference dynamics, especially when a large number of neurons (k) are involved, thereby limiting its effectivenoss. Dampening offers a less destructive intervention by allowing contextual signals to continue passing through the network. This, in turn, permits intervention on a larger set of expert neurons, potentially achieving strogge mitigation of the targeted concept.
Table 12 presents a comparative analysis of two intervention strategies, neuron masking and activation range masking, when employing the Dampening technique with 0.5. The evaluation spans 14 classes and
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utilizes the metrics: accuracy (Acc), confidence (Conf), class-wise accuracy (CAcc), class wise confidence (CConf), alterations in perplexity (PPT.), and MMLU score.
A consistent trend emerges across the primary metrics (Acc, Conf, CAcc, and CConf), where activation range masking demonstrates superior performance over neuron masking. Interventions based on activation ranges lead to a notably smaller decline in the accuracy and confidence associated with auxiliary concepts. For example, in Class 3, while neuron masking results in an accuracy drop of -0.974 in the targeted class and auxiliary class accuracy decrease of -0.411, activation range masking, despite comparable accuracy reduction in the targeted class (-0.970), shows less severe impact auxiliary class accuracy (-0.283). This pattern of activation range masking better preserves auxiliary class performance, is evident across all evaluated classes.
Examining the broader effects on language modeling capabilities reveals significant distinctions ween the two approaches. Neuron masking results in a considerable rise in perplexity (PPL), with increases ranging from +2.891 to +3.732 across all the classes. Furthermore, it tends to cause more pronouncod negative shifts in MMLU scores, reaching low as -0.045. Conversely, activation range masking results in substantially smaller increments in perplexity, falling within the +0.546 to +0.810 range, and frequently results in improved or minimally altered MMLU scores, with gains up to +0.035.
G.2 Mean Replacement
Another approach of activation replacement discussed in the urrratureeSuaa et al. 2021 is replacing it with the mean activation value. We provide the results for this type of replacement in Tables 13
14 15
The mean replacement strategy corresponds to setting the intervention function to x(x) = fu, where j is the mean activation of the neuron x computed over a general nexl-token prediction task on the Wikipedia (Foun-
dation).
we assess the effect of mean replacement using both neuron masking and activation range masking on the DB-Pedia dataset. In every class, neuron masking results in more severe degradation than range-based masking across all auxiliary and general metrics.
In Tables 15
Across metrics (Acc, Conf, CAce, and CConf), activation range masking consistently outperforms neuron masking. The degradation in accuracy and confidence of auxiliary concepts is significantly lower under rango-based interventions. For instance, in Class 3, neuron masking causes a drop of -1.00 in Acc and -0.766 in CAcc, whereas aclivation range masking yields similar Acc drop (-1.000) but substantially smaller decline in CAcc (-0.538). A similar pattern repeats across all classes; for example, in Class 0, neuron masking results in CAcc of -0.685 while activation range masking yields -0.551. In Class 7, neuron masking shows a CAcc of -0.491 compared to -0.267 for activation range masking.
Beyond auxiliary class performance, we observe substantial differences in how the two masking methods affect general language modelling capabilities. Neuron masking leads to a large increase in perplexity (PPL), ranging from +7.413 to +8.791 which is catastrophic, across classes, and induces more negative shifts in MMLU scores (as low -0.035 for Class 9, and also for Class 0 with -0.025, Class 1 with -0.030, Class 10 with -0.020, and Class 13 with -0.025). In contrast, activation range masking results in substantially smaller increases in perplexity (+0.397 to +0.687) and often yields improved or near-zero changes in MMLU scores (up to +0.020 for Class 6, and several positive values like +0.015 for Class 1, Class 8, and Class 9).
G.3 Adaptive Dampening
We propose a novel replacement method in which the intervention function Φ(x) applies runtime-controlled dampening based on the distance of the observed activation x from the center of a predefined activation range. Specifically, the dampening factor a(x) is linearly scaled according to the distance of from the mean μ of the neuron's activation distribution, within the range [p fu-2255, μ 2.50].
Let 3 [0, 1] denote the maximum dampening factor applied at the range boundaries. Then:
a(x)= = 3. xx 1r-wl ml
and d(x)= = a(x) x.
2.50
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This ensures that when = μ (the center of the range). a(x) = 0 and the activation is fully suppressed via ((x) 0. At the boundaries (x = μ + 2.5a), a(x) = 8, and the activation is minimally dampened. Values within the range are scaled proportionally based on their normalized distance from the mean. This adaptive dampening mechanism suppresses values near the mcan while preserving those closer to the range edges.
The dampening factor 3 can be optimized for different neurons based on the concept information that neuron provides. For this work, we use 8 = 0.5 across all neurons.
In Table 16 evaluate the adaptive dampening variant of the replacement function. This approach outperforms both neuron masking and static activation masking across all metrics.
In auxiliary class metrics, adaptive dampening yields much smaller degradation. Auxilary class accuracy (CAce) and confidence (CConf) show significantly reduced drops compared to other methods. For example, in Class 0, CÂce drops only -0.215 compared to -0.685 under neuron masking and -0.551 under hard activation masking. The effect is consistent across classes, with most CAce and CConf drops staying well below -0.3, and in many cases below -0.15.
Language modeling metrics show this approach to be exceptionally efficient. Perplexity increases are minimal, remaining within +0.408 to +0.609, substantially lower than all hard hard-masking variants. MMLU deltas also stay close to zero, with several classes showing improvement (e.g., Class 8: +0.030, Class 4: +0.015). Notably, no class suffers significant MMLU degradation.
H Training Details
For BERT, DistiBBERT, and Llama, we utilize pretrained models. Since BERT, and DistilBert are not inherently trained as a conversational agent, we use top-performing fine-tuned models from the Hugging Face repository. For the Llama model, few-shot prompt completion is employed to predict class labels. This involves providing small number of training samples from the dataset to guide the model's predictions.
For GPT-2, we fine-tune the pretrained model across all datasets for three epochs. The input sequence is constructed by concatenating the text with <sep>> token, followed by the class label, and ending with an <eos>> token. During training, the loss back-propagated only for the class label token, while all other tokens are assigned a skip label (-100). Additionally, all class labels are added to the model's dictionary as special single-token entries.
In the case of BERT-base based models, record the activation of the CLS token. In the case of GPT-2 and Llama models, we record the last token output when the class token is being predicted. The intervention is applied to the appropriate token on the residual stream.
For trained models (BERT, DistiBBERT, and GPT-2), a higher proportion of neurons (up to 50%) can be ablated with a relatively minor impact on primary task performance and minimal interference with auxiliary concepts. This suggests substantial neuronal redundancy, wherein multiple neurons appear encode overlapping features.
Dataset Preprocessing for Llama For Llama process whole datasets in few-shot settings and only curate 2000 samples per class, where the model prediction was correct.
H.1 Computation Details
All experiments, including activation extraction and interventions on large language models (LLMs), were conducted using an NVIDIA RTX 3090 GPU equipped with 24GB of VRAM. 64GB RAM.
Hyperparameter Ablation
The results for ablating r on GPT-2 model using the AG-News dataset are shown Table 17 For the target concept, values 0.3 - 2.4 show decreasing confidence, stabilizing at 7 2.4 (accuracy 0.6126). Beyond 2.4, negligible additional degradation occurs, indicating we've captured the complete target concept.
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activation range. Importantly, while target performance stabilizes after T = 2.4, auxiliary task performance declines after T = 2.7. Complement accuracy stays above 0.93 until then before dropping to 0.8795 at 4.5. This aligns with normal distribution properties where 95-99% of values fall within 2255 standard deviations.
J Class Wise Results
Here we provide the complete results for the selected models for all datascts. IMDB (Table 24). SST2
(Table 25 AG-News (Table 23 Emotions (Table 26 and DBpedia-14 (Table 27
K
Fine Grained Concepts
We evaluate the extent to which our interventions suppress the model's ability to predict finer-grained concepts, including pronouns, delimiters (brackets), terminal punctuation, and subject-verb agreement (Chauhan et al, 2025; Gurnee ct al., 2023at Rai et al. 2025 Miller et al. 2025 Marks et al.). We use set of pronouns spanning masculine, feminine, and neutral foruns: he/ hee/ she/ her, and they /heem For terminal punctuation, we analyze periods (.), question marks (?), and exclamation marks (!). For delimiters, we consider the opening braces { (, and [, and for subject- verb agreement, we analyze both singular and plural verb forms, specifically do/does and go/goes.
For each concept, we curate a dataset from WikiText by extracting 500 samples and manually verifying the data. We use 400 samples to rank neurons and estimate activation ranges. The remaining 100 samples form the held-out test set. We initially evaluate ranking methods discussed in the main text. Our initial experiments suggested that probe-based ranking was not effective. To reduce compute overhead, we analyse max and probeless rankings on pronoun concepts. We find that overall results are similar, shown in Table 18. but the max ranking yields a higher perplexity score. We therefore adopl probeless ranking for all reported results on fine-ge grained concepts.
The results for pronouns, delimiters (brackets), terminal punctuation, and subject-verb agroement are shown respectively. The results mirror the findings from the main-text experiments: the degradation of the chosen concept is comparable to full neuron ablation, while the auxiliary concepts are better preserved. This reduced collateral impact is reflected in the lower perplexity, indicating fewer unintended side effects.
in Table 19 20
21 22
L Range Masking Algorithm
A Algorithm 1 outlines the overall computation pipeline.
M Model Activation Steering:
Here we compare our method, NeuronLens, with model steering on the Llama 3 3.2B. Steering uses a dataset in which the target concept present, along with an auxiliary dataset which the concept absent (Chen) et al. 2025 Pai al. ).25). In our setting, the intervened concept class serves as the "presence" dataset, while all other concept classes form the "absence" dataset. Following the recommendations of Chen et al. (2025) we apply steering at the model's mid-layer (layer 14). To apply steering, a difference- -nneeecee---mmassss-- means vector is constructed using the average activations of targeted and auxiliary concepts. To apply steering at inference time, vector r is scaled by a scaler a, and added to the residual stream. In this setting, we evaluated different. values of a and found a = -2 to be most precise in target concept removal.
x' tasr
In comparison, we apply range-bacc based removal using a conservative setting of T = 1 across the full representation vector. Results showing comparison for activation steoring and ARM are reported in Table 28 The results suggest that steering some concepts causes only minimal degradation in the targeted behavior (Namely class 0), which indicates that clear steering direction does not exist. for this concept. For other concepts, performance
(Namely
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Algorithm Range-Based Based Masking (Activation Range Masking)
Require: Model M; labeled dataset D = {( ys)]; larget layer index concept o; top fraction p; range width T; range- gated operator o(-); ranking function R(j, Hy, HH*e)) R.
Let [d] (l,...,df denote neuron indices.
Ensure: Modified model M' where layer applies range-based masking in its forward.
Offline: collect concept-conditioned activations
3: De + [11, 9:) E D: : y/ =]
4: D-c t ((1),3)) E D: 9 #c}
Run M on De and store hidden vectors at layer
H1 t {x,));) (I,, Mi) De)
Run M D-c and store hidden vectors at layer l:
H's < (z,) (z,, yi) D-c)
Offline: rank and select neurons
10: for j do
11:
R
12: end for
13: S. t indices of top-p fraction by of
14: Offline: compute attribution ranges
15: for all S. do
16:
Mi +- THE Enem: hi
17:
6 [HTT Eneng (hj 121)2
18:
AR(1,,c)) + [ug Toj Hj + Toj]
19: end for
20: Update layer apply range-gated intervention in forward
21: For any input v, compute h' t (x)
22: for all S. do
23: if (x) E AR((j,,c) then
24:
(z) t (z)
25: end if
26: end for
27: Return (x:) and continue the forward pass of M normally.
is comparable to activation range masking; however, activation steering significantly increases perplexity scores.
M.1 Directional Ablation:
We also compare NeuronLens with dircctional ablation as describod indAriiii et al., 2024
+'+ TTT
(4)
We apply projection removal to only one layer, layer 14. Table 30 reports the results. We find that directional ablation fails in this setting, eliminating both target and auxiliary classes and resulting a very high perplexity.
M.2 Range Gated Steering:
To access the effect of combining NeuronLens with activation steering, we perform a preliminary experiment of using activation stecring instead of zero or mean ablation our rango-based framework (i.c, only stcoring components if their activation falls within the activation range of the target concept). In this setting, for the calculation of the mean values targeted and auxiliary concepts, utilize the last contextualized token
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instead of the average token representation (which is the default setting for activation stecring). We compare this approach with activation steering.
We present the results of the experiment in Table 29 The results show that base activation steering is able to precisely manipulate the targeted concept, but at a substantial cost perplexity. Our activation range-based steering (Activation Steering) produces similar results but bounds the perplexity to much lower value.
N
Comparison with Sparse Autoencoders
Sparse Autoencoders (SAEs) learn to represent a concept using a sparse linear combination neuron activations, where neurons are encouraged to be monosemantic nature. NeuronLens can be seen as unrollig neuron activation to identify monosemantic activation ranges, each belonging to unique concept, with limited overlaps. NeuronLens and SAEs can be viewed complementary methods: SAEs may serve first step toward disentangling polysemanticity, while NeuronLens can further provide fine-grained monosemantic ranges on top of the SAE representations. In this section, we conduct a preliminary experiment on concept removal SAE features, comparing full feature blocking with activation-range masking applied to SAE features.
N.1 Experimental Setup
Since pretrained SAEs are not available for the models tested in our main experiments, we utilize pretrained GemmaScope SAEs on Gemma-2-2B. Experiments are conducted on our largest tested dataset (DBPedia) for two variants SAEs:
Gemma Scope (Width 16k / 10_ 285)
Gemma Scope (Width 65k / 10 197)
We initially record the activated SAF features using samples from the training set, and then filter these based on their frequency of activation. feature is only considered for ablation if it activates for at least 10% of the training for a given concept. From this subset, we select the top 20% highest-activating features and apply either full feature masking or activation-range masking to test causality. Details the selected features in both settings are provided in Table 31
N.2 Results
Table 32 summarizes the experimental results. Compared to feature masking, activation-range masking produces smaller reduction in complementary hatientttr//uuillaaryy aaxiliaayy concepts and significantly lower perplexity scores, while achieving a drop on the targeted concepts that is comparable to full feature masking.
These patterns suggest that SAE features are not strictly monosemantic, and they still contain neurons that capture mixtures of related concepts. While NeuronLens is effective in interpreting original polysemantic neurons, it can also be used to disentangle SAE-learned representations, achieving more precise concept removal with substantially lower impact on overall model fluency.
Dataset Details
In Table 33
we provide samples from the benchmarking datasets used the main paper. Table 34 we
provide samples from the finer-grained concepts tested in K
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Table 8: Full text for samples shown in Table for Neuron 1525 (Sci/Tech) and Neuron 2587 (Business).
Neuron
Point
Boundary Affinity
Representative Examples
"Gene Twesking Turns Couch Potato Mice Into Racers Altering a single gene turned ordinary mice into marathon racers that could run for hours and huge amounts of food without getting fat, a team of researchers reported on Monday."
"Insecure elections marching ever closer Friday's St. Louis Post-Dispatch reports on a controversial decision by Missouri's Secretary of State: the state Missouri will allowing soldiers stationed overseas to cast ballots via e-mail. Their abeentee ballots will be scanned and converted to PDF files, which will be emailed to the Defense Department, printed out, and then faxed to Missouri. I'm in favor of helping soldiers vote; this is democracy, everyone should be able to vote. Yet I'm deeply skeptical of this proposal, for reasous: The plan depends on e-mailed ballots being printed and faxed by the Defense Department but does not provide any safeguards against soldiers being sanctioned for how they have voted; The transmission method is inherently technically insecure
Lower Boundary
World / Sports
"IBM's supercomputer breaks the world's fastest computer's ... Technology India: London, Nov 8 A new supercomputer being constructed by IBM has broken all supercomputing records after demonstrating double the power of the long-reigning supercomputing champion, NEC's Earth Simulator, based at Yokohama, Japan."
"Sony, IBM, Toshiba say powerful chip to start production 2005 SAN JOSE, Calif. A long-awaited microprucessor developed by IBM, Sony, and Toshiba will go into production next, year and start appearing in video game consoles and high-definiiin T-Vs in 2006."
1525
" "AMD Gives Details on Dual-Core Opteron Advanced Micro Devices has given more details on its fortcoming dual-core microproceserr chip. The Opteron-based design is said to be 30-55 percent faster than AMD #39;s single-core chips, but it will fit existing server designs."
Mran
Sci/Toch
" "Arm reveals Neon multimedia extension technology Microprocessor designer Arm Ltd. has developed a new multimedia technology called Ncon that will help improve the performance of mobile electronics devices that process multiple tasks, the company said Mouday."
"IPsss Introduces New Flat-Rate Pricing Plans for Wi-Fi Hotspot ... REDWOOD SHORES, Calif., Nov. 17 iPass Inc. today announced new monthly and annual flat-rate subscription plans for use of the Company's ' US Wi-Fi connectivity."
"NTT DoCoMo Rises on TSE on Reported Deal with Motorola Tokyo, Aug, 23 (Jiji Press]-NTT DoCoMo firmed the Tokyo Stock Exchange Monday morning following a media report that it will start procuring mobile phone handsets made by Matorola Inc."
Upper Boundary
Business
"Update: Omnipod heefs instant messaging service Omnipod, which provides hosted instant message (IM) services companies of all sizes, proparing several enhancements to its platform, including the additions of Web-based elient, telephony component and a persistent-chat feature, Omnipod's chief executive officer said."
"Put Me in, Coach! Coach joins the 500, and others stand benefit from the leather in the weather."
"London Stock Exchange eyes Asia HQ The London Stock Exchange plans to set up an Asia headquarters in Houg Kong to tap the growing number of mainland corporates eycing listings abroad, local newspaper reports."
Lower Boundary
World / Sports
"Halliburton closes higher Army's 's decision pay DALLAS (CBS.MW)- Halliburton's shares closed higher Wednesday after the Army Materiel Command reversed its decision withhold percent of its futurn payments to the company under contract to supply and support US troops in Iraq."
"UBS Buys Schwab Unit for 8265 Min SENEVA/NEV YORK (Reaters). Swiss based hanking giant UBS AG has agrend to buy Charles Schwab Corp's capital markets unit for 8265 million in cash, making UBS a leading player on the U.S. Nasdaq exchange, the companies said on Tuesday."
"Applied Materials Applied Materials (AMAT: news, chart, profile) shares were two cents to $16.05 in trading before the bell Wednesday and had wavered around break-even in late trading Tuesday after the results were aunounced,"
Mean
Business
2587
"Brown-Forman Earnings Jump 67 Percent Srown-Forman Corp. which sells products ranging from Jack Daniels whiskey to Lenox china, on Thursday posted a better-than- expected 67 percent jump in earnings as aggressive macketing boosted sales of premium spirits and new wines."
" "Arm hands over $910m for US chip firm Arm is to pay 8910m (501m) in cash and shares for Artisan, the US- trausistor-level designer for systems-on----the chairman Sir Robin Saxby said conference call: quot:This will combination " "Oracle sales
Arm
on database demand com September 14, 2001, 2:26 PM PT. This fourth priority's 's main focus has been improving or obtaining CItM and ERP software for the past year and half."
Upper Boundary
Sci/Toch
"Coming: IT that adapts to users' requirements The march of information technology into the workplace has been grected with a mix of and resistance, For all their promise of productivity gains, computers, husiness software, and telecommunications gear have disrupted processes the core of company's identity."
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Table 9: Statistical analysis of different layers showing skewness, kurtosis, and Kolmogorov- Smirnov test. results. GPT2 model. AG-News Dataset
Layer
Kurtosis
Skewness
Practical Normality
3.9314
0.0430
0.7913
3.7622
-0.0091
0.9525
3
3.4109
-0.0143
0,9870
3.5582
-0,0073
0.9801
5
3.6145
0.0051
0,9730
6
3.5318
0.0086
0.9769
3.3461
0.0083
8
3.2763
0.0037
0,9870
3.2207
0.0039
0.9860
10
3.2057
0.0029
0.9899
11
3.2105
-0,0002
0.9912
3.2061 12
-0.0014
0.9919
Table 10: Evaluation of layer selection on GPT-2 model on the Emotions dataset using neuron and range masking techniques. 20% Neurons selected. Here, Ace represents class accuracy, Conf denotes class prediction probability, and CAcc and CConf refer average accuracy and average class prediction probability across other classes, respectively. The Base Values indicate the baseline model performance, while Activation Range Mosking and Neuron Masking show deviations from the baseline performance.
Layer
Base
Masking
Activation Range Masking
Conf
CConf
AAce
AConf
ACAce
ACConf
AConf
ACAce
ACCouf
0.957
0.904
-0.074
0.002
Cless
0.933
0.932
0.931
0.913
-0.011
-0.056
-0.090
-0.116
0.001
-0.003
-0.004
-0.001
Class 2
0.901
0.885
0.334
0.924
-0.206
-0.195
-0.052
-0.092
-0.019
-0.015
-0.001
-0.002
Class
0.926
0.924
0.932
0.919
-0.128
-0.152
-0.051
-0.090
-0.004
-0.005
-0.001
-0.002
Class
0.885
0.867
0.938
0.927
-0.055
-0.084
-0.081
-0.083
-0.016
-0.009
0.002
-0.001
Class 0.851
0.786
0.934
0.924
-0.249
-0.217
-0.055
-0.094
0.016
0.013
-0.004
-0.005
0.957
0.004
-0.805
-0.380
-0.133
-0.096
Class 0.933
0.932
0.931
0.913
0,053
-0.003
-0.819
-0,781
-0.011
40.049
-0.110
-0.145
Class 0.901
0.865
0.934
0.924
-0.868
-0.737
-0.515
-0.519
-0.365
-0.337
-0.077
-0.126
2
0.924
Class 0.885
0.867
0.938
0.927
-0.729
-0.777
-0.461
-0.463
-0.042
-0.077
-0.078
-0.116
Class 0.851
0.756
0.931
0.924
-0,843
0.769
-0.511
-0.508
-0.229
+0.188
-0.106
-0.163
Class
0.970
0.957
0.915
0.904
-0.890 0.896
-0.901
-0.821
-0.832
-0.617
+0.688
-0.517
-0.341
Class
0.933
0.932
0,931
0.913
-0.901
-0.916
40.835
+0,832
-0.568
-0.607
-0.609
-0.330
Class 2
0.901
0.863
0.931
0.924
-0.808
-0.845
-0.838
-0.851
-0.605
-0.600
-0.589
-0.619
3
Class
0.924
Cless 4
0.886
0.867
0.938
0.927
-0.80
-0.811
-0.849
-0.857
-0.502
-0.522
-0.513
-0.544
Class
0.851
0.756
0.931
0.924
0.022
0.081
-0.865
-0.881
-0.155
-0.124
-0.561
-0.396
Class
0.970
0.957
0.915
0.904
-0.650
-0.703
-0.698
-0.764
-0.608
-0.621
-0.499
-0.510
Class
0.933
0.932
0.913
-0.845
-0.884
-0.667
-0.725
-0.491
-0.519
-0.450
-0.491
Class 2
0.901
0.865
0.934
0.924
-0.858
-0.824
-0.772
-0.809
-0.488
-0.497
-0.506
-0.523
0.919
0.663
-0.739
-0.546
Class 4
0.886
0.867
0.938
0.927
-0.239
-0.514
-0.754
-0.797
-0.304
-0.307
-0.452
-0.471
Class
0.851
0.786
0.934
0.924
-0.612
-0.463
-0.692
-0.765
-0.047
-0.038
-0.525
-0.541
Class
0.970
0.957
0.915
0.904
-0.838
-0.852
-0.492
-0.630
-0.695
+0.688
-0.554
-0.555
Class
0.933
0.932
0.913
-0.563
-0.683
-0.714
-0.552
-0.564
-0.605
-0.599
Class 2
0.901
0.865
0.931
0.924
-0.702
-0.700
-0.634
-0.690
-0.472
-0.170
-0.607
-0.605
Class
0.926
0.924
0.932
0.919
-0.361
-0.507
-0.615
-0.092
-0.567
-0.575
-0.538
-0.339
Cless
0.885
0.867
0.938
0.927
-0.873
-0.841
-0.525
-0.650
-0.668
-0.653
-0.594
-0.991
Class
0.851
0.786
0.934
0.924
-0.637
.0.573
-0.588
-0.681
-0.069
-0.022
-0.548
-0.553
Class 0
0.970
0.957
0.915
0.904
-0.720
-0.775
-0.829
-0.830
-0.484
-0.499
-0.318
-0.322
Class
0.933
0.932
-0.871
-0.887
-0.750
-0.768
-0.176
-0.195
-0.499
-0.199
Class 2
0.901
0.865
0.934
0.024
-0.895
-0.860
-0.735
-0.773
-0.680
-0.638
-0.335
-0.348
Class
0.926
0.924
0.932
0.919
-0.803
-0.884
-0.772
-0.793
-0.488
-0.431
-0.379
-0.381
Class 0.885
0.867
0.938
0.927
-0.621
-0.669
-0.743
-0.794
-0.430
-0.435
-0.247
-0.222
Class
0.851
0.786
0.934
0.924
-0.143
-0.086
-0.808
-0.831
-0.114
-0.070
-0.474
-0.478
30

---

Published in Transactions on Machine Learning Research (04/2026)
Table 11: Evaluation of layer selection on GPT-2 model on the Emotions dataset using neuron and range masking techniques. 20% Neurons selected. Here, Acc represents class accuracy, Conf denotes class prediction probability, and CAcc and CConf refer to average accuracy and average class prediction probability across other classes, respect ively. The Base Values indicate the baseline model performance, while Aclivation Runge Masking and Neuron Masking show deviations from the baseline performance.
Layer
Class
Base Values
Neuron Masking
Activation Range Masking
Acc
Conf
CAcc
CConf
AAc
AConf
ACAce
ACConf
AAcc
AConf
ACece
ACConf
Class 0
0.970
0.957
0.915
0.901
-0,908
-0.901
-0.752
-0.753
-0.527
-0.538
-0.492
-0.198
Class 1
0.933
0.932
0.933
0.913
-0.854
-0.895
-0.743
-0.729
-0.481
40.509
-0.330
-0.338
Class
0.901
0.865
0.931
0.921
-0.806
-0.835
-0.767
-0.763
-0.451
-0,122
-0.336
-0.355
7
Class 3
0.926
0.924
0.932
0.919
-0.756
-0.819
-0.641
-0.666
-0.445
-0.457
-0,331
-0.316
Class 4
0.885
0.867
0.938
0.927
-0.626
-0.618
-0.810
-0.817
-0.341
-0.335
-0.521
-0.332
Class 5
0.851
0.786
0,931
0.924
0,106
0.147
-0.810
+0.811
0.102
0.107
-0,517
-0.353
Class 0
0.970
0.957
0.915
0.904
-0.776
-0.791
-0.209
-0.291
-0.191
-0.312
-0,082
-0.111
Class
0.933
0.932
0.931
0.913
-0.585
-0.667
-0.412
-0.441
-0.591
-0.644
-0.199
-0.227
Class
0.901
0.865
0.934
0.924
-0.692
-0.716
-0.489
-04496
-0.560
-0.562
-0.468
-0.486
8
Class 3
0.926
0.924
0.932
0.919
-0.657
-0.714
-0.415
-0.464
-0.468
-0.503
-0.230
-0.266
Class 4
0.885
0.867
0.938
0.927
-0.501
-0.509
-0.531
-0.509
-0.201
-0.234
-0.258
-0,290
Class 5
0.851
0.786
0.934
0.924
-0.092
-0.050
-0.634
-0.647
0.065
0.058
-0.279
-0.308
Class 0
0.970
0.957
0.915
0.904
-0.759
-0.768
-0.311
40.351
-0.610
.0.661
-0.307
-0.328
Class
0.933
0.932
0.931
0.913
-0.570
-0.713
-0.319
-0.346
-0.908
-0.910
-0.267
-0.298
Class
0.901
0.865
0,934
0.924
-0.200
-0.504
-0.635
-0.643
-0.995
g
Class 3
0.926
0.924
0.932
0.919
-0.810
-0.834
-0.501
-0.502
-0.759
-0.772
-0.502
-0.516
Class 4
0.885
0.867
0.938
0.927
+0.358
-0.357
-0.476
-0.481
-0,387
-0.566
-0.519
-0.327
Classs 5
0.851
0.786
0.934
0.924
-0.133
-0.101
-0.546
-0.554
0.106
0.104
-0.450
-0.462
Class 0
0.970
0.957
0.915
0.904
-0.733
-0.741
-0.105
-0.126
-0.624
-0.659
-0.146
-0.163
Class 1
0.933
0.932
0.931
0.913
-0.389
-0.671
-0.178
-0.209
-0.899
-0.911
-0.25-1
-0,285
10
Class
0.901
0.885
0.934
0.924
-0.230
-0.513
-0.116
-0.224
-0.699
-0.735
-0.409
-0.451
Class 3
0.926
0.924
0,932
0.919
-0,434
-0.687
-0.081
-0.133
-0.898
-0,905
-0,401
-0.455
Class 4
0.885
0.867
0.938
0.927
-0.489
-0.506
-0.188
-0,256
-0.140
-0.186
-0,063
-0,102
Class
0.851
0.786
0.934
0.924
-0.306
-0.243
-0.157
-0,240
0.063
0.010
-0.095
-0.
Class 0
0.970
0.957
0.915
0.904
-0.358
-0.496
-0.382
-0.414
-0.301
-0.441
-0.121
-0.148
Class 1
0.933
0.932
0.931
0.913
-0.800
-0.857
-0.078
-0.123
-0,858
-0.875
-0.128
-0.162
Class
0.901
0.865
0.934
0.924
-0.897
-0.861
-0.416
-0.450
-0.901
-0.864
-0.484
-0.500
Class 3
0.926
0.924
0,932
0.919
-0,923
-0.921
-0.427
-0,470
-0.913
-0.914
-0.354
-0.393
Class 4
0.885
0.867
0.938
0.927
-0.152
-0.212
-0.039
-0.075
-0.210
-0.239
-0.181
-0.204
Class
0.851
0.786
0.931
0.924
-0.028
-0.131
0.002
-0.159
Class
0.970
0.957
0.915
0.904
-0.550
-0.603
-0.013
-0.003
-0,512
-0.594
0.005
0.012
Class 1
0.933
0.932
0.931
0.913
-0.526
-0.545
0,001
0.012
-0,521
-0.538
-0.005
-0.004
Class 2
0.901
0.863
0.931
0.924
-0.416
-0,102
0.002
0.006
-0.419
-0.407
0.007
0.006
12
Class 3
0.926
0.924
0,932
0.919
-0.561
-0.376
-0.007
0.003
-0.561
-0.572
0,000
0.005
Class 4
0.885
0.867
0.938
0.927
-0.655
-0.658
-0.042
-0.034
-0.657
-0.659
-0.011
-0.003
Class
0.851
0.786
0.931
0.924
-0.672
-0.300
-0.297
-0.718
-0.672
-0.266
Table 12: Dampening intervention results on L.lam-3.2-3B (DBPedia-14): comparison of neuron and
range masking. 30% neurons were selected. Dampening factor used is a 0.12.. Acc represents class accuracy, Conf denotes class prediction probability, and CAce and CConf refer to average accuracy and average class prediction probability across other classes, respectively. The Base Values indicate the baseline model performance, while Neuron Masking and Activation Range Masking show deviations from the baseline performance. PPL A and MMLU A show changes in perplexity and MMLU scores, respectively.
Class
Base Valuea
Neuron Masking
Activation Range Masking
Acc
Con'
CAce
OCCof
AAcc
AConf
ACecc
ACConf
APPL
AMMLU
Aecc
AConf
ACAcc
ACConf
APPL
AMMLU
Clus
L.000
0.576
1.000
0.563
.0.919
-3.545
-0-281
-0.309
3.161
-0.020
-0.924
-0.545
-0.276
-0.285
0.840
-0.010
Class
1,800
0.396
1.003
0.567
-1.98
-3-467
-0/2:6
-0 (7-
3.578
-0015
-0.85
-0.026
-0-1935
-0.206
0.725
0.015
Class
L.000
0.441
1.000
0.575
.0.644
-0.201
-0.461
-0.323
2,891
-0.330
-0.500
.0.350
-0.345
-0.282
0.718
0.005
Class
L,000
0.461
1000
0.573
.0974
-0.419
-0.411
-0 .0.346
3,036
.0.25
-0.370
.0.438
-0.282
-0.283
0.653
0.010
Clas
L.00
0.589
1.00
0.541
-0.382
-0.597
-0.367
-0.317
2.907
0.000
-0.553
-0.507
-0.334
-0.284
0.891
0.020
Class
1,000
0:330
1.003
-0990
-0396
40/23H
40.246
3,500
0,010
-0.970
-0.187
0.810
0.015
Class
1,000
1.003
-0.638
-0.203
0.697
0.585
-1:210
-0.382
-0:306
-0.226
3,057
0.417
-0:11
-0418
-0.335
3/001
0,0.0
-0.346
40.416
-0.200
.0.187
0.624
0.035
Cluss L.00
0.526
1000
.0.820
-0.465
-0.327
.0.264
3.388
-0.090
-0.500
-0.
-0.218
-0.189
0.596
0.000
Class
L.00
0.505
1000
.0.001
-0.466
-0.389
.0.314
0.732
0,000
-0.006
00.655
-0.207
.0.198
0.695
-0.05
Clas
1.000
0.187
0.560
-0873
-0.432
<0/122
-0
3,070
-0.090
-0.965
-0.027
-0.355
.0.205
0.594
-0.015
Class 12
1,000
0.373
1000
0.563
.0.728
.0.53
282
.0 22.22
3.400
0.040
00.21
0.451
.0.190
.0.163
0.595
0.035
Clus 13
1,000
0.567
1.000
0.564
.0.961
.0.537
J0 0.226
.0.189
2,936
0.000
-0.955
.0.536
-0.157
-0.150
0.672
0.005
31

---

Published in Transactions on Machine Learning Research (04/2026)
Table 13: Mean replacement intervention results on Llama-3.2-3B (AG-News): comparison of neuron and range masking. 30% neurons were selected 30% neurons were selected. Mcan Activation μ is used as a replacement value. Ace represents class accuracy, Conf denotes class prediction probability, and CAcc and CConf refer to average accuracy and average class prodiction probability across other classes, respectively. The Base Values indicate the baseline model performance, while Neuron Masking and Activation Range Mosking show deviations from the baseline performance. PPL A and MMLU À show changes in perplexity and MMLU scores, respectively.
Class
Base Values
Neuron Masking
Activation Range Masking
Avc
Conf
CAcr
OCCon
AAc
ACrmf
ACArr
ACConf
APPL.
AMMLU
Acc
ACouf
ACAcn
ACCOnf
APPL
AMMLU
Class
1.000
0,718
1.000
0.116
-1.000
-0.716
-0.271
-0.114
20.715
-0.003
-0.979
-0.715
-0.268
-0.331
0.474
0.020
Class
1.000
0.474
1.000
0.533
.0.332
-0.400
-0.414
-0.308
22, 22,853
-0,005
-3.814
-0.454
-0.360
-0.427
0.445
-0.010
Chass
1.000
0.489
1.000
0.523
-1.000
-0.455
-0.376
-0.498
28.255
0.005
-0.968
-0.467
-0.340
-0.419
0.594
0.010
Class
1,000
0,375
1.000
0.355
-1.000
-0.372
40.45
-0.312
22 854
0.000
-0.975
-0.371
+0.350
40.400
0.413
0.030
Table 14: Mean replacement intervention results on Llama-3.2-3B (Emotions): comparison of neuron and range masking. 30% neurons were selected 30% neurons were selected. Mean Activation l is used as replacement value. Acc represents class accuracy, Conf denotes class prediction probability, and CAce and CConf refer to average accuracy and average class prediction probability across other classes, respectively. The Base Values indicate the baseline model performance, while Neuron Masking and Activation Range Mosking show deviations from the baseline performance. PPL. A and MMLU A show changes in perplexity and MMLU scores, respectively.
Class
Bas Vulues
Nouron Marking
Activation Range Musking
Acc
Coul
CAcc
CCouf
A.Acc
ACCun
ACAcc
ACCunf
APPL
AMMLU
AAAcc
AConf
ACAcc
ACCunl
APPL
AMMLU
Class
1,000
0.531
1.000
0.545
.0:302
-0.801
-0.303
7.205
-0:905
-0.330
-0.692
-0.487
0.404
-0.015
1.000
0.469
0.576
-0.954
-0.643
-0.478
7.126
-0.961
-0.441
-0.877
-0.451
-0.025
1.000
0.038
0.510
-0.650
-0.703
-0.441
7.275
-0.005
-0.702
-0.563
-0.643
-0.423
0.507
0.030
1,000
0.627
L.000
0.583
-0.800
-0.033
-0.550
-0.412
6.903
0.010
-0.503
-0.903
-0.461
-0.354
0.482
-0.010
Class
1.000
0.857
1,000
0.540
-0.7081
-0.506
40.582
-0.416
6,956
0.025
-0.750
-0.302
.0.562
-0.397
0.507
-0.005
Table 15: Mean replacement intervention results on Llama-3.2-3B (DBPedia-14): comparison of neuron and range masking. 30% neurons were selected 30% neurons were selected. Mean Activation l is used replacement value. Acc represents class accuracy, Conf denotes class prediction probability, and CAce and CConf refer to average accuracy and average class prediction probability across other classes, respectively. The Base Values indicate the baseline model performance, while Neuron Masking and Activation Ronge Masking show deviations from the baseline performance. PPT. A and MMLU A show changes in perplexity and MMLU scores, respectively.
Cloos
Bao Valuee
Neuron Macking
Activattio Range Masking
Cont
CAce
AAcc
ACouf
ACAcc
ACConf
APPL
AMMLU
AConf
ACAcc
ACCouf
AMMLU
Class
L.000
0.576
1.000
0.563
-1.000
.0.576
.0.855
.0 00.564
7.681
.0.22
-1.000
-0.576
-0.561
.0.545
0.657
-0.005
Closs
L.00
0.526
1.000
0.567
-1.000
-1.526
-0.554
-0 155
8-457
-0.050
-1.000
-0.525
-0.556
-0.517
0.583
0.015
Clos
L.000
0.441
1.000
0.575
.0.985
-3.461
.0.607
-0 00.566
7.567
-0.015
-0.905
-0.640
-0.574
-0.538
0.520
-0.010
Class
1,800
0.461
1000
0.573
-1000
-0-161
-0-706
-0:561
8-000
-0.15
-1.000
-0. 40.851
-0.538
-0.534
0.543
0.010
Class
L.00
0.530
1000
0.541
-1.000
.0.888
.0.724
-0.528
8,230
0.010
-0.355
.0.538
-0.502
-0.503
0.565
0.065
Class
1.000
0.359
1.003
0.565
-1.000
-0.359
-0.666
+0 .0.551
7.753
0.010
-1.000
-0.530
-0.582
-0.510
0.552
0.05
Class
1,800
0.810
1.003
-0.85
-0:341
40005
-0.336
0.020
1,000
0.585
1.003
-0.502
-0.010
-0.996
-0.035
0.285
-0.98
-0.305
-0.422
8.79L
4.021
-0.406
Class
L.000
0.497
1000
-1.000
-0.497
-0.719
-0.548
7.938
0.025
-1.000
-0.097
-0.447
-0.459
0.397
-0.05
Class 12
1,800
0573
1.00
0.563
-0.90
-0.572
-0,29
-0.58
8/016
-0008
-0.806
-0.57
-0.377
-0.484
0.425
0.000
Class 13
1,000
0.567
1.000
0.561
.1.000
.0.365
.0.22
.0.53
7.543
0.025
-0.938
0.506
-0.341
.0.481
0.464
-0.010
32

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Published in Transactions on Machine Learning Research (04/2026)
Table 16: AdapLive dampening intervention results on Llama-332-3B 14) comparison of neuron and range masking. 30% neurons were selected. Adaptive Dampening factor used B = 0.5. Acc represents class accuracy, Conf denotes class prediction probability, and CAcc and CConf refer to average accuracy and average class prediction probability across other classes, respectively. The Base Values indicate the baseline model performance, while Neuron Masking and Aclivalion Range Masking show deviations from the baseline performance. PPL A and MMLU A show changes in perplexity and MMLU scores, respectively.
Class
Base Values
Activation Range Masking
Acc
Conf
CAce
CConf
AAcc
AConf
ACAcc
ACConf
APPL
AMMLU
Class 0
1,000
0.576
1.000
0.563
-0.927
-0.543
-0.215
-0.217
0.487
-0.015
Class
1.000
0.526
1.000
0.567
-0.791
-0.451
-0.134
-0.109
0.543
0.000
Class 2
1.000
0.441
1.000
0.575
-0.828
-0.380
-0.277
-0.215
0.540
-0.010
Class 3
1.000
0.461
1.000
0.573
-0.958
-0.432
-0.230
-0.214
0.492
0.010
Class
1.000
0.839
1.000
0.541
-0.346
-0.579
-0.261
-0,218
0.521
0.015
Class 1.000
0.339
1.000
0.568
-0.900
-0.319
-0.140
-0.116
0.609
-0.015
Class
1.000
0.810
1.000
0.545
-0.336
-0.613
-0.130
-0.122
0.524
-0.110
Class
1.000
0.595
1.000
0.562
-0,243
-0.388
-0.108
-0.080
0.408
0.005
Class
1.000
0.417
1.000
0.574
-0.440
-0.414
-0.152
-0.088
0.465
0.030
Class 9
1.000
0.526
1.000
0.567
-0.799
-0.459
-0.182
-0.131
0.445
0.005
Class 10
1.000
0.505
1.000
0.569
-0.894
-0.451
-0.222
-0.130
0.513
-0.010
Class 11
1.000
0.497
1.000
0.569
-0.836
-0.420
-0.308
-0.155
0.440
-0.005
Class 12
1.000
0.573
1.000
0.563
-0.720
-0.451
-0.172
-0.095
0.444
0.025
Class 13
1.000
0.567
1.000
0.564
-0.941
-0.530
-0.142
-0.098
0.502
0.010
Table 17: Performance metrics for varying values.
Acc
Conf
CAcc
CConf
0.9021
0.8858
0.9452
0.9358
0.8439
0181855
0.9424
0.9327
0.7801
0.7486
0.9263
0.7295
0.6950
0.9340
0.9174
0.6834
0.6482
0.9337
0.9093
0.6424
0.6141
0.9331
0.9000
2.1
0.6184
0.3926
0.9327
0.8910
2.4
0.6126
0.5858
0.9314
0.8846
2.7
0.6024
0.5798
0.9280
0.8800
3.0
0.5971
0.5776
0.9234
0.8777
3.3
0.5963
0.5786
0.9173
0.8753
3.6
0.5970
0.5794
0.9097
0.8729
3.9
0.5976
0.5802
0.9020
0.8698
4.2
0.5967
0.5798
0.8908
0.8642
4.5
0.5967
0.5798
0.8795
0.8577
Table 18: Evaluation of Llama on selected concepts for max and probeless rankings (top 20% neurons).
Concept
Probeless
AAce
AConf
ACAce
ACConf
APPL
AAcc
AConf
ACAce
ACConf
APPL
Pronouns
-0.79
-0.51
-0.55
-0.17
8.94
-0.73
-0.45
-0.24
-0.13
2.03
33

---

Published in Transactions on Machine Learning Research (04/2026)
Table 19: Evaluation of Llama on pronoun concepts using activation range masking (he, his, she, her, they, them)
Concept
Neuron Masking
Activation Range Masking
Ace
AConf
ACAce
ACConf
APPL
AAcc
AConf
ACAcc
ACConf
APPL
He
-0.59
-0.10
2.67
+0.95
-0.60
-0.07
0.14
0.91
She
-0.96
-0.61
-0.21
-0.11
1.80
-0.97
-0.61
-0.15
0.03
0.68
He/His
-0.80
-0.50
-0.12
-0.00
1.52
-0.80
0.46
0.00
0.17
0.32
She/Her
-0.77
-0.50
-0.28
-0.17
1.61
-0.69
0.43
-0.03
0.05
0.36
They -0.50
-0.27
-0.40
-0.30
2.80
-0.61
-0.31
-0.09
-0.03
0.67
They/Then
-0.42
-0.22
-0.34
-0.25
1.79
-0.38
-0.19
0.03
0.42
Table 20: Evaluation of Llama on terminal punctuation concepts using activation range masking (?, !, .).
Sctting
Masking
Activation Range Masking
AAce
AConf
ACAce
ACConf
APPL
AAce
AConf
ACAce
ACConf
APPL
? ve 1,
-0.94
-0.58
-0.23
-0.13
1.72
-0.89
-0.54
-0.09
-0.04
0.54
vs ?,
-0.38
-0.36
-0.59
-0.32
1.70
-0.42
-0.33
-0.18
-0.08
0.27
1,
-0.83
-0.45
-0.55
-0.38
1.04
-0.66
-0.37
-0.14
-0.04
0.21
Table 21: Evaluation of Llama on delimiter (brackets) concepts using activation range masking "( (1P
Setting
Neuron Masking
Activation Range Masking
AAce
AConf
ACAce
ACConf
APPL
AAce
AConf
ACAcc
ACCouf
APPL
ve 6
-0.75
-0.43
-0.18
-0.26
1.53
-0.72
-0.40
-0.10
-0.14
0.47
( vs G
-0.90
-0.62
-0.13
-0.14
1.69
-0.83
-0.60
0.01
0.02
0.28
( [s l
-0.44
-0.41
-0.41
-0.37
1.86
-0.50
-0.12
-0.16
-0.11
0.48
Table 22: Evaluation of Llama on subject-verb agreement concepts using activation range masking (do, does, has, have)
Setting
Neuron Masking
Activation Range Masking
AAcr
AConf
ACAce
ACConf
APPL
AAce
AConf
ACAce
ACConf
APPL
do -0.46
-0.55
-0.28
does
-0.95
-0.11
-0.17
-0.07
1.48
-0.92
-0.38
0.15
0.01
0.55
has
-0.33
-0.20
-0.36
-0.18
1.29
-0.45
-0.22
-0.04
-0.02
0.21
have
.0.90
-0.47
-0.29
-0.08
1.14
.0.86
-0.44
-0.11
0.08
0.26
34

---

Published in Transactions on Machine Learning Research (04/2026)
Table 23: Evaluation of selectod models on the AG-News datasct using neuron and range masking techniques. Ace represents class accuracy, Conf denotes class prediction probability, and CAcc and CConf refer to average accuracy and average class prediction probability across other classes, respectively. The Bose Values indicate the baseline model performance, while Activation Range Masking and Neuron Masking show deviations from the baseline performance. For GPT-2 50% and for Llama-3.2-00 30% neurons selected.
Model
Class
Base Values
Neuron Masking
Activation Range Masking
Ace
Conf
CAce
CConf
Acc
AConf
ACAce
ACConf
AAcc
AConf
ACAce
ACConf
Cloes
0.945
0.936
0.919
0.927
-0.205
-0.387
0.004
-0.076
-0.198
-0.589
0.007
-0.010
BERT
Class
0.988
0.910
-0:225
-0.659
-0.194
40.650
0.003
-0.012
Cloes 2
0.905
0.881
0.962
0.945
-0.300
-0.536
0.014
-0.079
-0.298
-0.542
0.014
-0.009
Class
0.913
0.965
-0.354
-0.377
0.026
40.379
0.025
-0.005
Claes 0.955
0.951
0.911
0.928
-0.920
-0.936
-0.251
-0.221
-0.919
-0.925
-0,019
-0,008
GPT-2
Class 0.986
0.981
0.931
0.917
-0.926
-0.931
-0:253
-0.257
-0.912
-0.916
-0,054
-0.069
Clazs 0.897
0.886
0,960
0.919
-0.696
-0.737
-0.110
-0,132
-0,678
-0.725
-0.097
-0.306
Cluss 0.940
0.916
0.946
0.939
-0.940
-0.916
-0.024
-0.037
-0.887
-0.882
-0.080
+0.510
0.936
1.000
0.680
-0.995
-0.934
-0.530
-0.345
L.amm--.2-3B
Class
L.000
0.742
1.000
0.744
-0.870
-0.680
-0.615
-0.599
-0.875
-0.681
-0.515
-0.503
L.000
0.655
0.773
-0.895
-0.646
-0.795
-0.655
Class
1.000
0.642
1.000
0.778
-0.975
-0.641
-0.698
-0.630
-0.975
-0.640
-0.420
-0.459
Table 24: Evaluation of selected models on the IMDB dataset using neuron and range masking techniques. Here, Acc represents class accuracy, Conf denotes class prediction probability, and CAcc and CConf refer to average accuracy and average class prediction probability across other classes, respectively. The Base Values indicate the baseline model performance, while Activation Range Masking and Neuron Masking show deviations from the baseline performance.
Model
Class
Base Values
Neuron Masking
Activation Range Masking
Acc
Conf
CAce
CConf
AAce
ACouf
ACAcc
ACCouf
AAcc
ACouf
ACAcc
ACConf
Class Class
0.930
0.901
-0.169
-0.352
0.081
-0.066
0.059
0.035
BERT
0.926
0.901
0,930
0.908
-0.211
-0.355
0.057
-0.001
-0.206
-0.361
0.056
0.025
Class 0
0.965
0.941
0.940
0.922
-0.535
-0.922
0.050
0.057
-0.005
-0.901
0.055
0.046
CPT-2
Class
0.940
0.941
-0.620
-0.667
0.055
0018
-0.610
0.015
0.027
1.amm-3.2-3B
Class 0
1.000
0.619
1.000
0.500
-0.648
-0.448
-0.515
-0.287
-0.640
-0.446
-0.502
-0.278
Chus
1.000
0.500
1.000
0.619
-0.877
-0.410
-0.273
-0.304
-0.873
-0.409
-0.265
-0.303
Table 25: Evaluation of selected models on the SST2 dataset using neuron and range masking techniques. Here, Ace represents class accuracy, Conf denotes class prediction probability, and CAcc and CConf refer to average accuracy and average class prediction probability across other classes, respectively. The Base Values indicate the baseline model performance, while Activation Range Masking and Neuron Masking show deviations from the baseline performance.
Model
Class
Base Values
Neuron Masking
Aclivation Range Masking
Acc
Conf
CAce
CConf
AAcc
AConf
&CAce
ACConf
AAce
AConf
ACAce
ACConf
BERT
Class Chess
0.890
0.882
0.930
0.925
-0.058
-0.308
0.029
-0.047
-0.075
-0.329
0.031
0.036
0.930
0.925
0.890
0.582
-0.043
-0.318
0.033
-0.045
-0.045
-0.330
0.030
0.050
GPT-2
Class
0.950
0.978
-0.158
0.010
0.009
0.010
Class
0.981
0.978
0.950
0.937
-0.187
-0.223
0.011
0.053
-0.176
-0,216
0.011
0.046
Llama-3.2-11
Class
1.000
0.620
1.000
0.890
-0.532
-0.459
-0.420
-0.424
-0.532
-0.456
-0.404
-0.415
Class
1.000
0.630
-0.289
-0.379
-0.326
-0.315
-0.284
-0.376
-0.306
-0.301
35

---

Published in Transactions on Machine Learning Research (04/2026)
Table 26: Evaluation of selected modols on the Emotions dataset using neuron and range masking techniques. Here, Acc represents class accuracy, Conf denotes class prediction probability, and CAcc and CConf refer average accuracy and average class prediction probability across other classes, respectively. The Base Values indicate the baseline model performance, while Activation Range Masking and Neuron Masking show deviations from the baseline performance.
Model
Masking
Range Masking
Ace
Conf
CAcc
CConf
AAcc
AConf
ACAcc
ACConf
AAcc
AConf
ACAce
ACConf
Class 0
0.960
0.935
0.901
0.851
-0.241
-0.718
0.013
-0.266
-0.222
-0.718
0.012
-0.055
Cluss
0.942
0.904
0.305
0.861
-0-223
-0.691
0.028
-0.254
-0.213
-0. 00.692
0.032
-0.064
BERT
Class 2
0.824
0.723
0.926
0.889
-0.371
-0.533
0.016
-0.284
-0.352
-0.533
0.018
-0.115
Cluss
0.927
-0.247
-0.664
0.010
-0.256
0.012
-0.057
Class 0.884
0.837
0.022
0.880
-0.406
-0.646
0.012
-0.251
-0.402
-0.648
0.012
-0.066
0.591
-0.090
Class 0.968
0.956
0.903
-0.895
-0.751
-0.125
-0.1224
-0-1099
-0.008
Cles 0.939
0.938
0.925
0.908
-0.879
-0.882
-0.019
-0. -0.099
-0.879
40.880
-0.016
-0.015
Class
0.9022
-0.736
-0.0221
-0.022
Chus 3
0.910
0.905
0.932
0.921
-0.713
-0.714
-0. -0.006
-0.007
-0.715
-0.716
-0.002
-D. -0.001
Class
0.860
-0.753
-0.240
-0.133
Class 0.857
0.798
0.932
0.923
-0.587
-0.601
-0.301
-0.908
-0.587
-0.601
-0.280
-0.289
Cluss 0.950
0.550
0.782
0.455
-0.950
-0.5447
-0.655
-0:405
-0.945
-0.547
-0.571
-0.378
Class 0.905
0.498
0.804
0.173
-0.855
-0.495
-0.743
-0.433
-0.867
-0.499
-0.60T
-0.101
Llama-3.2-3B
Chuss 0.785
0.4221
0.827
0.483
-0.785
-0.420
-0.771
-0.454
-0.785
-0.4220
-0.658
-0.436
Class 3
0.790
0.482
0.833
0.476
-0.760
-0.477
-0.635
-0.423
-0.755
-0.477
-0.544
-0.4022
Chuss
0.780
0.487
0.829
0.476
-0.780
-0.486
-0.534
-0.365
-0.780
-0.486
-0.444
-0.324
Class
0.536
0.296
0.855
0.498
-0.417
-0.284
-0.751
-0.465
-0.429
-0.282
-0.653
-0.434
36

---

Published in Transactions on Machine Learning Research (04/2026)
Table 27: Evaluation of selected models the DBPedia-14 dataset using neuron and range masking techniques. Here, Acc represents class accuracy, Conf denotes class prediction probability, and CAcc and CConf refer to average accuracy and average class prediction probability across other classes, respectively. The Base Values indicate the baseline model performance, while Activation Range Masking and Neuron Masking show deviations from the baseline performance.
Model
Class
Base Values
Neuron Masking
Activation Range Masking
Acc
Conf
CAce
CConf
AAce
AConf
ACAce
ACCouf
AAce
AConf
ACAce
ACConf
Class 0.972
0.966
0.992
0.991
-0.052
-0.702
-0.011
-0.076
-0.000
Class
0.987
0.988
0.991
0.950
-0.030
-0.778
0.000
.0.017
.0.118
-0.770
0.000
-0,000
Class
0.987
0.985
0.991
0.990
-0.239
-0.814
-0.015
-0.217
Class 0.591
0.997
0.990
0.969
-0.008
-0.766
0.000
-0.019
-0.001
-0.731
0.000
-0.000
Class 0.984
0.983
0.991
0.950
-0.058
-0.777
-0.018
-0.032
-0.761
0.000
-0.000
Class
0.995
0.995
0.990
0.969
-0.007
-0.795
0.000
-0.017
-0.001
-0.771
0.000
-0.000
BERT
Class
Class 7
0.975
0.994
0.974
0.994
0.992
0.990
0.991
0.969
-0.121
-0.028
-0.807
-0.789
0.000
-0.015
-0.017
-0.112
-0.010
-0.767
-0.803
0.0000
0.000
-0.001
-0.000
Class
1,0000
1.000
0.990
0.980
-0.001
-0.808
-0,022
0.000
-0.772
-0.000
Class
0,999
0.998
0.990
0.969
-0.004
-0.837
0.000
.0.019
.0.001
-0.811
0.000
-0.000
Class 10
0.991
0.993
0.990
0.969
-0.025
-0.846
0.000
-0.016
-0.005
-0.831
0.000
-0.000
Class 11
0,997
0.997
0.990
0. 9.9999
-0.013
-0. 20.751
0.000
-0.017
-0.001
-0.726
0.000
-0.000
Class 12 0.990
0.990
0.990
0.969
-0.018
-0.772
0.000
-0.017
-0.005
-0.755
0.000
-0.000
Class 13
0.991
0.991
0.990
0. 9.9999
-0.009
40.740
0.001
-0.017
-0.001
-0.721
0.000
-0.000
Class
0,985
0.977
0,980
0.989
-0.860
-0.877
-0.136
-0.800
40.899
-0.017
Class 0.995
0.992
0.990
0. 1.968
-0.500
-0.567
-0.180
-0,192
-0.600
-0.511
-0.023
-0021
Class
0.985
0.980
-0.890
-0,213
-0.80
-0.9002
-0.100
Class 3
0.995
0.995
0.390
0.987
-0,900
-0.9331
-0.145
-0.143
-0,000
-0.9227
-0.008
-0.017
Class 0.970
0.969
0.992
0.980
-0.002
Class 0.995
0.993
0.980
0.988
-0.315
-0.446
-0.122
-0.192
-0,200
-0,433
-0.013
-0.025
CPT-2
Class 0.965
0.964
0.992
0.960
-0.925
-0.932
-0.052
-0.082
-0,910
-0.928
-0.006
-0.007
Class 1.000
0.998
0.989
0.967
-0.815
-0. (0.865
0..003
-0.008
-0.775
-0.846
-0.026
-00577
Class 1.000
1.000
0.989
0.987
-0.995
-0.990
-0.144
-0.188
-0,900
-0.932
-0.026
-0.555
Class 1.000
1.000
0.989
0.967
-0.975
-0.979
-0.250
-0,208
-0.955
-0.958
-0.020
-0.049
Class 10 0,995
0.993
0,990
0.968
-0.395
-0. -0.085
-0.045
-0.053
-0.590
-0.675
-0.005
-0.011
Class 11 0.985
0.984
0.990
0.968
-0.210
-0. 33.533
-0.094
-0,118
-0.135
-0.396
-0.015
-0.331
Class 12
0,990
0.988
0.990
0. 969
-0.930
-0. 938
-0. 293
-0,309
-0.855
-0.880
-0.013
-0.029
Class
1.000
0.999
0.989
0.987
-0.985
-0: 20.986
-0.393
-0.416
-0.945
-0.981
-0.018
-0.044
Class
1.000
0.586
1.000
0.559
-0.990
-0.584
-0.473
-0.990
-0.584
-0.441
Class
1.000
0.533
1.000
0. 16566
-1.000
-4. 52.528
80.
-0.446
-0.970
-0.528
-0.706
-0.371
Class 1.000
0.467
1.000
0.568
-0.995
-0.462
-0.963
-0.477
-0.995
-0.461
-0.838
-0.432
Class 1.000
0.460
1.000
0.569
-0.995
-0.459
-0.981
-0.486
.0.995
-0.459
.0.815
-0.420
Class 1,000
0.828
1.000
0.139
-0.965
-0.800
-0.981
-0.454
-0.955
-0,808
-0.852
-0.412
Class 1.000
0.349
1.000
0.568
-1.000
-0.348
-0.862
-0.429
-0.989
-0.347
-0.585
-0.346
Llama-3,2-3B
Class 1.000
0.509
1.000
0.511
-1.000
-0.767
-0.972
-0.149
-1.000
-0.767
-0.736
-0,366
Class 7
1.000
0.599
1.000
0.558
.0.855
-0. -0.588
-0.918
-0.410
-0,860
-0.586
-0.489
-0.274
Class 1.000
0.420
1,000
0.572
-1.000
-0.420
-0.957
-0,467
-1.000
-0.420
-0.660
-0.335
Class
1.000
0.527
1.000
0.563
-1.000
-0.524
-0.842
-0,435
-0.995
-0.523
-0.352
-0.320
Class 10
1.000
0.505
1.000
0.565
-0.995
-0.503
-0.9007
-0.484
-1.000
-4.50
-0.389
-0.322
Class 11 1.000
0.505
1.000
0. 0.565
-0.975
-0.501
-0. 862
-0,116
-0.970
-0.301
-0.579
-0.313
Class 12
1,000
0.560
1.000
0.561
-0.980
-0.545
-0.8122
-0.417
-0.975
-0.544
-0.496
-0.310
Class
1.000
0.587
1.000
0.559
-0.990
40.584
-0.722
-0.406
-0.985
-0.584
-0.588
-0.337
Table 28: Comparison of Activation Stocring(using mean diff:Average representation) and Activation range masking on Llama-3.2-33 (AG News). Acc represents class accuracy, Conf denotes class prediction probability, and CAcc and CConf refer to average accuracy and average class prediction probability across other classes, respectively. The Base Values indicate the baseline model performance, while Neuron Masking and Actination Range Masking show deviations from the bascline performance. PPL A and MMLU A show changes in perplexity and MMLU scores, respectively.
Class
Base Values
Activution Steerinx
Activation Range Musking
Conf
&Conf
3CConf
3/Ace
ACConf
Class
1,000
0,712
1,0080
0.442
-1.033
-0.199
-0.158
-0.053
1.253
0.000
-0.550
-0.280
-0.15%
-0.185
0.503
0.015
Class
1.000
0.472
1.000
0.529
.0.583
-0.390
-0.011
0.041
0.955
0.065
-0.975
-9.465
-0.335
-0.265
0.703
-0.010
Clasx
1,000
0.188
1.000
0.522
-1.000
-0.416
-0.550
-0.011
0.890
0.000
-0.912
-0-176
-0.257
-0:217
0.573
-0015
Class 3
1,000
0.3844
1,000
0.561
.0.981
-0,320
0.033
-0.015
1.364
0.015
.0.887
-0.367
40,332
-0.360
0.684
0.003
37

---

Published in Transactions on Machine Learning Research (04/2026)
Table 29: Comparison of Activation Steering(Using Last token representation) and Activation Range (Activation Steering) on Llama-3.2-3B (AG News). Acc represents class accuracy, Conf denotes class prediction probability, and CAcc and CConf refer to average accuracy and average class prediction probability across other classes, respectively. The Bose Values indicate the baseline model performance, while Activation Steering and Activation Range (Activation Steering) show deviations from the baseline performance. PPL A and MMLU A show changes in perplexity and MMLU scores, respectively.
Closs
Raw Values
Activation Strering
Activation Range (Activation Strering)
Acc
Coaf
CAce
OCoof
3.Acc
&Conf
ACAce
ACConf
APPL
2MMLU
AAcce
AConf
2CAcc
ACConf
aPPL
AMMLU
Class
1,000
0.715
1000
0.481
-0.103
-0.539
-04025
0,302
3.817
-0.900
-0.578
-0.003
0.181
1,225
-01005
1.000
0.473
-1.000
-1.00
0.008
-1.000
-1.000
40.07
0287
-1.000
-1.00
Table 30: comparison of Directional Ablation and Activation range masking on Llama-3.2-3B (AG News). Ace represents class accuracy, Conf denotes class prediction probability, and CAce and CConf refer to average accuracy and average class prediction probability across other classes, respectively. The Base Values indicate the baseline model performance, while Newron Masking and Activation Range Masking show deviations from the baseline performance. PPL A and MMLU A show changes in perplexity and MMLU scores, respectively.
Cloos
Rasn Valuue
Directional Ablation
Activation Range Masking
Acc
Conf
CAce
CCeaf
AAce
ACouf
ACAce
ACCoaf
APPL
AMMLU
AAce
ACont
ACAce
ACCoaf
4PPL
AMMLU
Class
1.000
0.716
1.000
0.444
-1 -1.000
.0.716
-0.017
-0.42
962.454
-0.210
-0.950
-0.639
-0.185
-0.197
0.503
0.015
Class
2.000
0.473
1.000
0.521
-1.000
00.73
-1.000
.0.21
1068 310
-0.206
.0.915
00.86
-0.138
-0.257
0.703
-0.100
Chuss
2.000
0.485
1.000
0.625
-1.000
-0.48
-0.996
-0.522
1025 228
-0.206
-0.912
-0.472
-0.287
-0.268
0.575
-0.015
Class
1.000
0:387
-1.000
-0387
+0979
-0362
-0.225
-03M2
Table 31: SAE feature selection statistics for DBPedia.
SAE
Avg. Activating (Per Sample)
Unique Activating (Per Concept)
Consistent (Per Concept)
Features Ablated (Per Concept)
16k /
285
226
1024
409
81 / 16K
65k / 197
153
1318
296
/ 65K
Table 32:
Comparison of full feature masking and activation-range masking applied to SAE features on DBPedia.
Method
SAE
AAcc
AConf
ACAcc
ACConf
APPL
Feature Masking
16k / 10 285
-0.323
-0.153
-0.169
-0.129
9.510
Activation Range Masking
16k
10
285
-0.322
-0.153
-0.140
-0.095
0.958
Feature Masking
65k
10
197
-0.392
-0.236
-0.175
-0.216
6.203
Activation Range Masking
65k
10
197
-0.391
-0.235
-0.098
-0.129
0.335
38

---

Published in Transactions on Machine Learning Research (04/2026)
Dataset
Sample Text
Label
IMDB
Before Dogma 95: when Lars used movies as art, not just story. A beautiful painting about love and death. This is one of my favorite movies of all time. The color... The music... Just perfect.
Positive
SST2
badly-rendered egi effects.
Negative
AG News
Wall St. Bears Claw Back Into the Black (Reuters) Reuters Short-sellers, Wall Street's dwindling of ultra-cynics, seeing green again.
Business
Emotions
feel romantic too.
Love
Dbpedia-14
Abbott Farnham Abbott Limited was a British coachbuilding business based in Company Farnham Surrey trading under that name from 1929. A major part of their oulput under sub-contract to motor vehicle manufacturers. Their business closed in 1972.
Table 33: Samples from Standard Text Classification Benchmarks
Type
Context Sample
Target
Pronoun Resolution
Story highlights Authorities also threaten to arrest Musharraf if Myriam Steinberg was 40 and fresh off breakup when Following the airport shooting, Steube doubled down I can't express how extremely pleased we are with Jenny and From 1 July 2018, the Tax Office advising Australians that
he
she
his
her
they
No other appliance company has wider scope of solutions, nor the experience to back
them
Delimiters (Brackets))
Q: the sum of separating vectors always separating? If St mathcal
Let w be
(
abstract: 'Franson's Bell experiment with energy-time entanglement
Punctuation
During pregnancy, tried to gather much information on how painful labor might actu- ally be
We are excited that you will be atlending the Madison Mission Trip Ever noticed how close ribbit and rabbit sounded
?
Subject- Verb Agreement
In physics, the fundamental interactions, also known as fundamental forces, are the interactions that
do
Asexual reproduction a type of reproduction that An allocution, or allocutus, is a formal statement made to the court by the defendant who has
does
The Abencerrages Abencerrajes (from the Arabic for Saddler's Sou) were family or faction that said to
have
Table 34: Samples for Fine- GrainG Concept Completion
39


Reviews:
[{"review_id": "1830zOV5eP", "reviewer": "Reviewer_EVJs", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper shows that many “salient” neurons in LLMs are polysemantic, but the activation magnitudes associated with different concepts often form distinct, roughly Gaussian-like distributions with little overlap. The authors then propose a interpretability method NeuronLens, which attributes concepts to activation _ranges_ within a neuron and uses range-based interventions to manipulate a target concept while causing less collateral damage than masking whole neurons  . Experiments and causal “concept erasure” evaluations show that range-based interventions can manipulate target concepts while causing substantially less collateral damage to other concepts and overall model performance than full neuron masking.", "claims_and_evidence_yn": "Yes", "claims_explanation": "1. It would be good to provide more evidence on the core assumption (“Gaussian-like, low-overlap concept-conditioned distributions”). For example, a quantitative overlap measure across concepts per neuron (e.g., overlap coefficient / AUROC between concept-conditioned distributions). It would also be good to show the fraction of neurons where overlap is small enough to make range-gating meaningful.  \n2. For neuron activations, it would be better to provide a breakdown by layer/model/dataset. Also document clear failure cases (high-overlap or multimodal).  \n3. The paper is well written with clear motivation and the authors have conducted detailed and comprehensive experiment analysis. In addition to GPT-2, the authors conduct analysis on recent LLMs, i.e., Llama 3 model series.", "audience_yn": "Yes", "audience_explanation": "This paper tackles the problem of interpretability in Large Language Models. I think this should be of interest to the general TMLR community.", "requested_changes": "1. In section 2.1, can the authors provide more details on the definition of \"Concept\"? For example, why is there only four concept type for a sentence? Can the authors provide concrete examples of concepts.  \n2. I think the related work could be made to be more complete. There are many works on mechanistic interpretability that tries to tackle similar problems, it would be good to add more discussion on related works and highlight the novelty/contributions of this work as compared to others.  \n3. Minor but there are some typos: in the caption of table 2 \"full reference texts are provided in Appendix ??\".  \n4. It would be good to clarify the full neuronlens pipeline end-to-end with algorithmic detail, for instance, pseudocode would be helpful for demonstrating the method better.  \n5. How is the proposed method Neurolens related to sparse auto-encoders (SAEs)? SAEs also seem to be a popular approach for LLM interpretability. It would be good to add some discussions on this in the paper.", "broader_impact_concerns": "I do not have broader impact concern on this paper.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "cTI4KVM0in", "reviewer": "Reviewer_mxe6", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The paper argues that despite widespread polysemanticity, individual neurons in LLMs exhibit concept-conditioned activation ranges that are approximately Gaussian and often separable, enabling more precise interpretation and control than discrete neuron-to-concept mappings. It introduces NeuronLens, which estimates per-concept activation ranges per neuron and applies range-gated interventions only within those ranges. Across multiple models and datasets, range-based edits suppress target concepts with substantially less collateral damage to auxiliary concepts than whole-neuron masking, while better preserving general capabilities.\n\nStrength:\n1. The paper is well-written and easy to follow.\n2. The proposed method is well-motivated and is analyzed comprehensively.\n3. The numerical experiments show promising performance of NeuronLens.\n\nWeaknesses:\n1. The paper omits discussion of sparse autoencoders (SAEs), a prominent approach to addressing polysemanticity. It would help to situate NeuronLens relative to SAEs. In SAE-based views, disentangled concept features are represented as sparse linear combinations of neuron activations. By contrast, NeuronLens operates at the level of individual coordinates, attributing concepts to neuron-specific activation ranges. One way to reconcile these views is to note that range-based attribution can be seen as examining the per-neuron projections of a concept-direction: different concept directions induce different coordinate-wise activation distributions, which manifest as distinct ranges. \n2. Evaluations center on class-level semantics. The concept encoded by neurons are usually finer-grained. It is unclear whether the separability assumptions and the NeuronLens also work for these finer-grained concepts.\n3. Comparisons focus on neuron masking; stronger baselines (direction/subspace editing, causal tracing variants) would better situate gains. Also the authors can consider evaluating the method on SAE related benchmarks.", "claims_and_evidence_yn": "Yes", "claims_explanation": "1. The claim that concept-conditioned activations often form approximately Gaussian, separable ranges within a neuron is supported by consistent KDE visualizations, skewness/kurtosis near-normality, and a practical-normality KS criterion across several models and datasets.\n2.  The effectiveness of NeuronLens is proven by numerical experiments. The results show effective targeted suppression with notably less collateral degradation than whole-neuron masking.", "audience_yn": "Yes", "audience_explanation": "The paper offers a fresh coordinate-level perspective on polysemanticity, together with a practical framework, NeuronLens, for concept intervention. These findings are interesting to researchers in interpretability, controllability, and safety of LLMs.", "requested_changes": "1. Clarifying how range-gating complements or differs from SAE, e.g., when coordinate-level gating is preferable to editing along learned feature directions.\n2. Adding some experiments regarding intervention of finer-grained concepts.\n3. Adding direction/subspace editing comparators and a causal-tracing style targeted intervention where feasible.\n4. It will make the method of higher practical value if the authors could provide some (heuristic) algorithms for selecting the optimal $\\tau$ (besides extensive hyperparameter tuning).", "broader_impact_concerns": "NA", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "ZXSP8T3Sq1", "reviewer": "Reviewer_5PyT", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "In this work, the authors present observations regarding neuron polysemanticity—the tendency of individual neurons to activate in response to multiple, seemingly unrelated concepts. Through statistical analysis of a concept-level annotated dataset, they report that the activation distributions of these neurons exhibit Gaussian-like properties. Leveraging this observation, they define concepts as ranges capturing the bulk of the activation distribution and introduce NeuronLens, a neuron interpretation method. The authors position NeuronLens as an alternative to existing masking-based approaches, claiming improved interpretability.\n\nStrengths:\n\n- The paper addresses a highly relevant and valuable topic: the analysis of patterns in the functioning of pre-trained networks. The statistical analysis section presents a simple yet insightful observation that warrants attention and further exploration.\n- The breadth of experimental effort is commendable, with evaluations conducted across three models and three datasets, supplemented by extensive ablation studies. The inclusion of detailed versions of existing tables in the appendix further enhances the reproducibility and transparency of the work.\n- Additionally, the visualization of neuronal activation patterns is effective\n\nWeaknesses:\n\n- Overall, the paper suffers from issues related to clarity and rigor. While some of the concerns are minor and do not fundamentally undermine the integrity of the study, others significantly hinder the comprehension of the presented work (see the \"Requested Changes\" section for specific examples).\n- The experimental validation provided to justify the performance of the NeuronLens method is somewhat unsatisfactory. In my view, the default approach for assessing performance in neuron identification experiments should involve mean replacement, as the objective should be to neglect the neuron’s effect rather than to \"attack\" it. Although the authors address this point, their response is limited to a specific setup where accuracy reaches 1 and disturbed accuracy drops to -1 for most classes. More broadly, many experimental setups yield curious results, with numerous instances of below-zero accuracies, which raises questions about the robustness and generalizability of the findings.\n- Upon examining Figure 4, it appears that while NeuronLens identifies neurons associated with a substantial drop in accuracy for the relevant concept compared to neuron masking, it also causes greater deterioration in auxiliary concepts.\n- Additionally, NeuronLens operates by probing each neuron individually. However, there is strong reason to believe that polysemanticity should be interpreted as arbitrary directions in the latent space [1]. As such, I would find it more compelling to frame the method as neuron-independent—for instance, by searching for concept representations as multivariate Gaussian distributions within the latent space.", "claims_and_evidence_yn": "No", "claims_explanation": "The statistical analysis presented in the manuscript is, in my assessment, sufficiently supported by the evidence. However, I remain unconvinced by the claims regarding the superiority of NeuronLens. For further details, please refer to the \"Weaknesses\" section of this review.", "audience_yn": "Yes", "audience_explanation": "Definitely yes, the study of neuron polysemanticity is undeniably a topic of significant current interest in the field. This paper offers compelling and insightful observations that contribute to a deeper understanding of the phenomenon.", "requested_changes": "Minor:\n\n- The metric for accuracy is presented as a percentage in Figure 4 but scaled between -1 and 1 in the tables. It would be advisable to standardize the representation.\n\n- There is notable redundancy in several statements. For example: \"However, neurons frequently exhibit polysemanticity; the ability to encode multiple, seemingly unrelated concepts (Lecomte et al., 2024; Marshall & Kirchner, 2024)\" vs. \"Work in polysemanticity (Lecomte et al., 2024; Marshall & Kirchner, 2024) shows that individual neurons learn multiple seemingly unrelated concepts.\" Another example:  \"Related Works\" section overlaps with content in Paragraph 2 of the Introduction.\n\n- Descriptions of experimental results are occasionally verbose without adding substantive information. For instance: \"Full neuron masking leads to notable increases in perplexity, exceeding 3 points in the best case, whereas range masking results in a maximum increase of only 1.1.\"\n\n- Typos: lines under Table 4 are misaligned; a reference to the appendix is missing in Table 2; Sentence \"Figures figs. 8 to 19 in Figure 4\" in section F.2 unclear\n\n- Consider adding arrows (↑/↓) in tables to indicate whether higher or lower values are better. Some best results are not bolded for emphasis, while others are (Example Table 3 or Table 5).\n\n- The Related Works section could be strengthened. Even if not directly related to NeuronLens, it would be valuable to discuss Sparse Autoencoders [2] and LogitLens [3], given their methodological similarities to the proposed approach.\n\nMajor:\n\n- Provide a clearer definition of what distinguishes a salient neuron from a non-salient one. The current approach appears to rely on thresholding maximum activation values, but the choice of thresholds (50%, 30%, 20%, and 5%) lacks justification and seems arbitrary.\n\n- Explicitly define what constitutes a concept in the experiments. Is it associated with a token (as suggested by probability distribution plots), a word (as implied by Table 2), or sentences (as indicated by Table 7)?\n\n- Include mean replacement as a baseline in the experimental evaluations to facilitate more robust comparisons.", "broader_impact_concerns": "N/A", "additional_comments": "[1] Elhage, Nelson, et al. \"Toy models of superposition.\" arXiv preprint arXiv:2209.10652 (2022).\n\n[2] Cunningham, Hoagy, et al. \"Sparse autoencoders find highly interpretable features in language models.\" arXiv preprint arXiv:2309.08600 (2023).\n\n[3] Nostalgebraist. \"Interpreting GPT: The logit lens.\" Blog Post (2020).", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)