Automated Interpretability Metrics Do Not Distinguish Trained and Random Transformers
Authors: Thomas Heap
Tim Lawson
Lucy Farnik
Laurence Aitchison
Venue: ICLR 2026 Poster (2026)
Keywords: Sparse Autoencoders
SAEs
LLMs
interpretability
Primary area: interpretability and explainable AI
TL;DR: SAEs trained on random transformers achieve similar automated interpretability scores to trained models, showing that more targeted measures are needed.

Abstract: Sparse autoencoders (SAEs) are widely used to extract sparse, interpretable latents from transformer activations.
We test whether commonly used SAE quality metrics and automatic explanation pipelines can distinguish trained transformers from randomly initialized ones (e.g., where parameters are sampled i.i.d. from a Gaussian).
Over a wide range of Pythia model sizes and multiple randomization schemes, we find that, in many settings, SAEs trained on randomly initialized transformers produce auto-interpretability scores and reconstruction metrics that are similar to those from trained models.
These results show that high aggregate auto-interpretability scores do not, by themselves, guarantee that learned, computationally relevant features have been recovered.
We therefore recommend treating common SAE metrics as useful but insufficient proxies for mechanistic interpretability and argue for routine randomized baselines and targeted measures of feature 'abstractness'.

AUTOMATED INTERPRETABILITY METRICS Do NOT DISTINGUISH TRAINED AND RANDOM TRANSFORMERS
Thomas Heap
Tim Lawson
Lucy Farnik
University of Bristol
University of Bristol Bristol, UK
University of Bristol Bristol, UK
Bristol, UK thomas. hwwp@br ac.
Laurence Aitchison University of Bristol Bristol, UK
ABSTRACT
Sparse autoencoders (SAEs) are widely used to extract sparse, interpretable latents from transformer activations. We test whether commonly used SAE quality metrics and automatic explanation pipelines can distinguish trained transformers from ran- domly initialized ones (e.g.. where parameters are sampled i.i.d. from Gaussian). Over a wide range of Pythia model sizes and multiple randomization schemes, we find that, in many settings, SAEs trained on randomly initialized transformers pro- duce auto-interpretability scores and reconstruction metrics that are similar to those from trained models. These results show that high aggregate auto-interpretability scores do not, by themselves, guarantee that learned, computationally relevant features have been recovered. We therefore recommend treating common SAE metrics as useful but insufficient proxies for mechanistic interpretability and argue for routine randomized baselines and targeted measures of feature 'abstractness'.
INTRODUCTION
Sparse autoencoders (SARs) are a popular tool in mechanistic interpretability research, with the aim of disentangling the internal representations of neural networks by learning sparse, interpretable
features from network activations (Elhage et al.) 2022 Sharkey et al. 2022 Cunningham et al.) 2023
Bricken et al. 2023). An autoencoder with a high-dimensional hidden layer is trained to reconstruct
activations while enforcing sparsity (Gao et al. 2024 Templeton et al. 2024 Lieberum
Licberuu et al. 2024
with the aim of discovering the underlying concepts or 'features' learned by the network (Park
eet al. 2023 Wattenberg and Viégas 2024). Developing better SAEs relies quantitative evaluation metrics like auto-interpretabiiity scores that measure agreement between generated explanations and
activation patterns (Bills et al. 2023 Paulo et al. 2024 Karvonen al. 2024a).
For an interpretability method to be considered robust, its evaluation metrics should distinguish features learned through training from artifacts arising from the data or model architecture. A key sanity check is therefore to compare the method's 's output on a trained model against strong null model, such one with randomly initialized weights (Adebayo al., 2020). We apply this sanity check to SAEs and find that several common quantitative metrics do not always clearly distinguish between the trained and randomized settings. In particular, found that SAEs trained on transformers with random parameters can yield latents with auto-interpretability scores (Bills et al.) Paulo et al. 2024) that are surprisingly similar to those from a fully trained model.
2023
This result raises important questions about what we can glean from applying these metrics of SAE quality. High auto-interpretability scores alone do not guarantee that an SAE has identified complex, learned computations. Instead, such scores may sometimes reflect simpler statistical properties of the training data (Dooms and Wilhelm, 2024) or architectural inductive biases that are present even without training. Indeed, one could argue that randomly initialized network still performs basic form of computation. such as preserving or amplifying the sparse structure of its inputs (Section 4). From this perspective, SAEs might faithfully interpret this simple, inherent computation.

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While some SAE features from trained models clearly arise from learned computation, the commonly used aggregate metrics are often insufficient for determining whether a given SAE has learned these more complex features. These results have important implications for mechanistic interpretability research. In particular, we suggest that more rigorous methods to distinguish between artifacts and genuinely learned computations needed, and that interpretability techniques should be carefully validated against appropriate null models.
Finally, we speculate about why these patterns might emerge. At a high level, there are two hypotheses: (1) the input data already exhibits superposition, and randomly initialized neural networks largely preserve this superposition; and (2) randomly initialized neural networks amplify or even introduce superposed structure to the input data (e.g.. given dense input generated i.i.d. from a Gaussian). We present toy models to demonstrate the plausibility of these hypotheses in Section but defer conclusions as to the mechanism responsible to future work.
2 RELATED WORK
Sparse dictionary learning Under a different name, 'superposition' in visual data is one of the foundational observations of computational neuroscience. Olshausen and Field (1996 1997) showed that the receptive fields of simple cells in the mammalian visual cortex can be explained as result of sparse coding, i.e., representing a relatively large number of signals (sensory information) by simultaneously activating a small number of elements (neurons). Coding theory offers perspective on efforts to extract the 'underlying signals' responsible for neural network activations (Marshall and Kirchner 2024).
Sparse dictionary learning (SDL) approximates a set of input vectors by linear combinations of a relatively small number of learned basis vectors. The learned basis is usually overcomplete: it has greater dimension than the inputs. SDL algorithms include Independent Component Analysis (ICA), which finds a linear representation of the data such that the components are maximally statistically independent (Bell and Sejnowskil 1995; Hyvärinen and Ojal 2000). Sparse autoencoders (SAEs) are
simple neural network approach (Lee al.) 2006 Ng] 201 Makhzann and Frey, 2014). Typically, an autoencoder with a single hidden layer that is many times larger than the input activation vectors trained with an objective that imposes or incentivizes sparsity in its hidden layer activations to try to find this structure. A latent is single neuron (dimension) in the autoencoder's hidden layer.
Mechanistic interpretability Recently, has become common to understand 'features' or concepts in language models as low-dimensional subspaces of internal model activations (Park et al.) 2023 Wattenberg and Viégas 2024: Fingels et al. 2024). If such sparse or 'superposed" structure exists, expect to be able to 'intervene on' 'steer' the activations, i.e., to modify or replace them to express different concepts and so influence model behavior (Meng et al. [002]] Zhang and Nanda
2023 Heimersheim and Nanda 2024: Makelov 2024 O' Brien et al. 2024).
SAES are a popular approach for discovering features, where one typically trains a single autoencoder to reconstruct the activations of a single neural network layer, e.g.. the transformer residual stream (Shakey et al.) 2022]; Cunningham et al. 2023; Bricken et al. 2023). Many SAE architectures have been suggested, which commonly vary the activation function applied after the linear encoder (Makhzani and Frey 2014; Gao et al. 2024 Rajamanoharan et al., 20245) Lieberum et al. 2024).
SAEs have also been trained with different objectives (Braun al. 2024]] Farnik et al. (0255) and
applied to multiple layers simultaneously (Yun et al. |021 Lawson et al. |024; Lindsey et al. 2024).
Besides reconstruction errors and preservation of the underlying model's performance, SAES have been evaluated according to whether they capture specific concepts (Gurnee et al. 2023 Gao et al.) 2024) or factual knowledge (Huang et al.]| [2024] Chaudhary and Geiger 2024), and whether these
can be used to 'unlearn' concepts (Karvonen et al. 2024b).
Automatic neuron description SAES often learn tens of thousands of latents, which are infeasible to describe by hand. Yun et al. 12021) find the tokens that maximally activate dictionary element from a text dataset and manually inspect activation patterns. Instead, researchers typically collect latent activation patters over text dataset and prompt large language model to explain them (e.g.
Bills et al. 2023] Foote et al. 2023). These methods have been widely adopted (c.g. Cunningham
al. 2023 Bricken al. 2023 Gao et al. 2024
Templeton et al. 2024
Lieberum al. 2024
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Bills al. (2023) generate explanation for the activation patterns of language-model model neuron over examples from a dataset, simulate the patterns based on the explanation, and score the explanation by comparing the observed and simulated activations. This method is commonly known as auto- interpretability (as in self-interpretingg) |aaulo et al. (2024) introduce classification-
measures of the fidelity of automatic descriptions that are inexpensive to compute relative to simulating activation patterns and an open-source pipeline to compute these measures. Choi et al. (2024) use best-of-k sampling to generate multiple explanations based on different subsets of the examples that maximally activate a neuron. Importantly, they fine-tune Llama-3. 1--8---Bsstie on the top-scoring explanations to obtain inexpensive 'explainer' and 'simulator' models.
Polysemanticity Lecomie et al. (024)) noted that neurons may become polysemantic incidentally. polysemantic neuron (basis dimension) of network layer represents multiple interpretable concepts (Elhage et al. 2022 Scherlis et al., 2023); unsurprisingly, individual neurons in randomly initialized network may be polysemantic. By contrast, our work studies superposition (Elhage et al. 2022 Chan 2024), which pertains to the representations learned across whole network layer opposed to any individual neuron. In particular, superposition allows a network layer as a whole to represent a larger number of (sparse) features than the layer has (dense) neurons by sparse coding (only a few concepts are active at time, i.c., a given token position).
Training only the embeddings Zhong and Andreas (024) showed that transformers learn prising algorithmic capabilities when only the embeddings are trained and no other parameters. These results demonstrate that the behavior of randomly initialized transformer can be shaped to surprising extent by training only a few parameters. However, our setting is very different: besides considering SAEs, we randomize all the parameters, including the embeddings, in our 'Step-O' and RRe-randomized incl. embeddings" variants. Our 'Re-randomized excl. embeddings' variant uses pre-trained embeddings, but we do not train those embeddings with fixed, randomized weights. Instead, we freeze the pre-trained embeddings and randomize the other weights (Section
Random transformers for board games Karvonen et al. (202cc) found that SAEs were consid- erably better at extracting meaningful structure from chess games using pre-trained transformers, as opposed to those with random weights. However, the data from board games is wildly different from language data. In particular, there is reason to expect that language is sparse (e.g.. particular concept such as 'serendipitous" appears only rarely), , and that this sparse structure is 'aligned' with conceptual meaning, In contrast, in board games, this is not necessarily true: useful concept such as a knight fork does not necessarily turn up sparsely in board games.
Random one-layer transformers Bricken et al. (2023) found that iuto-interpretability scores discriminated effectively between random and trained one-layer transformers. Similarly, we found that auto-interpretabbliiy scores for randomized models were relatively low for smaller models (e.g.. Pythia-70m) but that the gap was narrowed for larger models (e.g., Pythia-6.9b).
RESULTS
We trained per-layer SAEs on the residual stream activation vectors of transformer language models from the Pythia suite, with between 70M and 7B parameters (Biderman et al. (023). We compared SAEs trained on different variants of the underlying transformers:
Trained: The usual, trained model.
Re-randomized incl. embeddings: All the model parameters, including the embeddings, are re-initialized by sampling Gaussian noise with mean and variance equal to the values for each of the original, trained weight matrices.
Re-randomized exel. embeddings: As above, except the embedding and unembedding weight matrices are not re-initialized, i.c., are the same as the original, trained model.
Step-0: For Pythia models, the step0 revisions are available, which are the original model weights at initialization, i.c., before any learning (Biderman et al. 2023).
Control: The original, trained model, except where the input token embeddings are replaced at inference time by sampling i.i.d. standard Gaussian noise for each token, such that
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Fuzz ROC Curves (100M tokens)
Layer
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Randomized incl omb (AUC = 0087)
Figure 1: 'Fuzzing' ROC curve vs. layer for Pythia-6.9b (100 latents sampled per SAE). The trained model (gray line) and randomized variants (colored) overlap, whereas the control (black) is near chance (dotted). This suggests aggregate AUROC alone insufficient to attribute latents to learned computation. See Figure for other metrics/model sizes and Appendix B for multiple random seeds.
given token does not have consistent embedding vector. For this variant, expect auto-interpretability to perform at the level of chance.
For primary experiments, trained SAEs 100M tokens from the RedPajama dataset (Weber et al. 2024) using an activation buffer size of 10M tokens (see Appendix Cfrr a subset of experiments billion tokens). For models with fewer than 410M parameters, we trained an SAE at every layer; for Pythia- 1b, we trained SAEs at every second layer; and for Pythia-6.9b, we trained SAES every fourth layer.
that demonstrate similar results with SAEs trained
Unless otherwise stated, we trained k-sparse autoencoders (also known as TopK SAEs; Makhzani and 32. We confirm that
Frey|2015 Gao et al. 2024). with an expansion factor of 64 and sparsity our results are robust with respect to these hyperparameters by training SAEs on Pythia 160m with 1ithia-160m between 16 and 128, and sparsities of 16 and 32 (Figure 18).
expansion factors equal to powers
The training implementation is based on Belrose et al. (2025). ) our evaluations are based on Caden
al. (025)) and Karvonen et (2024a).
Auto-interpretability Feature explanations that identify a concept can be input to a classifier that predicts whether the concept appears in the text inputs. Such classifier may be evaluted by traditional metrics, like the area under the receiver operating characteristic (ROC) curve (AUROC). |Pallo et al. (2024) proposed 'fuzzing' and 'detection' classification tasks evaluate feature explanations. For "fuzzing' scoring, both positive and negative examples of tokens (i.e., with non-zero and zero activation values, respectively) for given latent delimited with special characters, and language model is prompted to identify which examples have been correctly delimited for the latent given its explanation. For 'detection' a language model is asked identify which examples contain activating tokens for each feature. Bills et al. (2023) originally proposed 'simulation' scoring, based on the correlation between predicted and observed activations, but this method is expensive compute.
Except where noted, we report 'fuzzing" scores as measure of auto-interpretability because this measure has been demonstrated to correlate with simulation scoring (Paulo et al. 2024). We include similar AUROC curves for the 'detection' scoring method in Appendix B For each trained SAE (i.e., underlying model, variant, and layer), we randomly sampled 100 features to obtain auto-interpretabbliiy scores. The implementation is based Paulo al. (2024). We use the Meta-Llama-3.
model to generate explanations and make predictions (larger than the 8B models used by Choi al. (2024) and open-source, unlike Bills al. 2023)
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We found that the auto intr-iettrprttabiiityy scores were far more similar between the trained and ran- domized models than with the control (Figures [ and 2. The similarity between the ROC scores for trained and randomized transformers demonstrates that 'fuzzing' auto-interpretabiiiiy alone, applied to SAE latent explanations, may not meaningfully distinguish between these underlying models.
Evaluation We considered standard SAE evaluation metrics alongside the auto interpettepttt AUROC for Pythia models with between 70M and 6.9B parameters. As above, broadly found that the randomly initialized and re-randomized models (Figure blue, green, orange lines) were more similar the trained model (Figure [ gray lines) than our control (Figure 2 black lines).
Notably, the cosine similarity between the original activation and the SAE reconstruction and explained variance are often far lower for the random control than the other models, and its re- construction errors tend to increase across layers while the remaining variants decrease. For the random control, this can perhaps be explained by the fact that a Gaussian is the highest entropy distribution with fixed mean and variance (Jaynes] 2003); we speculate that Gaussian vectors are the 'least structured', in some sense, and thus hardest for SAEs to reconstruct. As Gaussian-distributee activations are propagated through successive layers, we would expect the activations to become less Gaussian and perhaps more 'sparse', i.e., easier to reconstruct (Section 4
Interestingly, the randomized variants (blue and orange lines) are more similar to the trained model than the variant at initialization (green line). This is especially evident we look at the L norm values in larger models. We speculate that this pattern arises because parameter norms may differ greatly between a trained model and its state at initialization. In contrast, our randomization procedure was specifically designed preserve parameter norms with respect the trained model. The scale of parameters at different layers may be important, e.g., to control the growth of activations as they progress through the residual stream (Liu et al.) 2020). In the AUROC plots, we find that for all but the control variant, AUROC increases with model size, We speculate that features become more specific SAE size increases: in smaller SAEs, each latent must explain more of the input, making classification tasks easier for larger SAEs.
Figure 2 (row five) shows the cross-entrops (CE) loss score, or loss recovered, against model layer. This is the increase in the loss when the original model activations are replaced by their SAE reconstructions, divided by the increase when the activations are replaced by zeros ('ablated"). The results show that the 'trained' variant SAES perform similarly to others from the literature (e.g.. Kissane et al., 2024) Rajamanoharan et al. 2024a| Mudide et al. 2024). Importantly, the CE loss score only makes sense for the trained variant: for any of the randomized variants, the loss is very poor, regardless of whether the original or reconstructed activations are used.
Latent explanation complexity Despite sometimes similar auto-interpretabilit scores and eval- uation metrics, we had expected that SAES applied to trained vs. randomized transformers would discover qualitatively different features. In particular, we expected SAEs trained on the randomized variants to learn relatively simple features based characteristics of the input text, but not more complex, abstract features as with trained transformers (Templeton et al.) 2024). For qualitative examples, we provide random sample of features and the corresponding maximally activating dataset examples for each variant of Pythia-6.9b in Appendix J and more detailed information in Appendix L
Anecdotally, we have observed that a significant proportion of SAE latents have non-zero activations only on single token small number of distinct tokens within a text dataset (e.g.. Lin and Bloom 2024; Dooms and Wilhelm 2024). Hence, a simple measure of the complexity of an SAE latent given a set of maximally activating examples is the degree which the latent activates on a single token ID or multiple distinct IDs. Specifically, we quantify the number of token IDs in terms of the entropy of the observed distribution of latent activations over tokens: the greater the entropy, the more 'spread out' the latent activations, and the less token-specific the latent. We take this distribution to be the total latent activation per token across the set of maximally activating examples used to generate explanations for autooinnerpretabbliyy. We show the relationship between entropy and 'fuzzing' AUROC score for individual latents in Appendix H
We include the entropy of the observed distributions of latent activations over token IDs in the last row of Figure?? [ The negative control variant displays a consistently high entropy, which is to be expected given that the embedding for given token ID is sampled i.i.d. from Gaussian on each
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Figure 2: Comparison of sparse autoencoder performance across Pythia models (70M to 6.9B parameters). The different SAE variants show remarkably similar trends across model scales, with larger models exhibiting more consistent behavior across layers. All variants save for control achieve comparable performance despite fundamentally different initialization approaches.

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Figure 3: An example of the effect of a randomly initialized neural network on superposed input data. We take 10K samples of n, sparse input features from Lomax distribution with shape a = and scale À = 1 and project these to na = 2 dense input features by an i.i.d. standard normal matrix. Then, we pass the dense outputs two-layer MI.P with ReLU activation and hidden size of 4ng and recover n, sparse outputs by the inverse of the previously generated projection matrix.
occurrence of the token, i.e., a token does not have a consistent embedding vector (Section 3). For the trained variant, the entropy increases across layers, i.c., the further into the model, the less likely the maximally activating examples for each latent contain activations concentrated on single token. This is also expected: at later layers, we expect more abstract features that are less similar to token embeddings. Finally, the entropy for randomized models tends to be lower than for either the trained or control variants, indicating that latents are activated specifically at one or a few IDs.
In combination with the preceding results, this suggests that standard SAE quality and auto- interpretability metrics are missing an important aspect of SAE features: their 'abstractness', While the token distribution entropy is not direct measure of 'absstrcttess', it suggests that the randomized variants, viewed in the context of their similar auto-interpretabiliiy scores to the trained variant, remain able learn simple, single-token features. However, unlike the trained variant, the features of the randomized variants do not become more complex as the layer index increases.
A TOY MODEL OF SUPERPOSITION IN RANDOM NETWORKS
We speculated in Section that the apparently high degree of sparsity and interpretability in the activations of randomized transformers might be because the input data exhibits superposition, which neural networks preserve, neural networks somehow amplify or even introduce superposition into the input data. In this section, we examine both possibilities through the lens of toy models. We find some evidence support each potential cause, but we leave the question of which predominates in the case of randomized transformers and the results detailed in the main text to future work.
4.1 MATRIX MULTIPLICATIONS PRESERVE SUPERPOSITION
First, we consider a simplified model to demonstrate that multiplication by a weight matrix W preserves superposition. Imagine that we generate superposed input data by first generating n, i..dd 'sparse' features from a heavy-tailed Lomax distribution ~ Lomax A). We can project the nigher-dimensional, sparse down to lower-dimensional, dense æ with a matrix D, then add Gaussian noise with small variance Σ, x ~ Nx; Dz, E). Importantly, if we multiply by some matrix W. then x' Wa is also superposed: it is generated by the same model as æ, except with different noise covariances and mappings from to namely x' ~ (2; W Dz, WZWT).
We can see that the same intuition might extend to neural networks with nonlinearities by visualizing the results of passing the dense activations through simple feed-forward network (MLP). Figure 3 shows an example where n, = sparse features are projected down to na = 2 dense features, and the MLP outputs appear superposed despite the non-linearity. Moreover, it suggests that NNs might amplify superposition rather than only preserving it: comparing the inputs (Figures Baland (bb to the outputs (Figures 3d and 3c), there fewer points between the 'arms' of the outputs.
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Figure 4: The mean max cosine similarity (MMCS) between the features learned by standard SAE (decoder weight vectors) and the -lta-generating features against the L' penalty coefficient in the training loss, following Sharkey et al. (2022). There is a 'Goldilocks zone' where SAEs near-perfee near-perfectly recover the gata-gene features, given enough latents to represent them.
4.2 DO RANDOM NNS PRESERVE OR AMPLIFY SUPERPOSITION?
We investigated this suggestion by generating toy data with the same procedure as Sharkey al. 2002), i.c., sampling ground-truth features a hypersphere and generating correlated feature coefficients such that only a small number are active (Appendix L.Tp. We then passed these inputs to two-layer MLP initialization and trained SAEs both the inputs and outputs individually. As a control, we used Gaussian-distribusee distributed inputs with a mean and standard deviation equal to the superposed toy data. We used standard SAEs with L sparsity penalty (Appendix 1.2
Following Sharkey et al. 2022). ) we confirmed that SAES can recover the ground-truth features that generated the data (Figure In particular, we measured the mean max cosine similarity (MMCS): for every data-geneaating feature, we found its maximum cosine similarity with the features learned by the SAE (its decoder weight vectors) and took the average over data-generating features. However, the MMCS only applies to the MLP inputs, where we have access to the data-genent geeeeeennnernnggggge features a different approach is required analyze the MLP outputs. To this end, we took the ability of SAEs to achieve low reconstruction error with high sparsity as proxy for the degree to which the training data exhibits superposition. Specifically, we vary the 7,- penalty coefficient to obtain Pareto frontiers of the explained variance against sparsity measures (Figure 5a
As expected, we found that SAEs achieved much greater sparsity at a given level of explained variance for the superposed inputs relative to the Gaussian control (Figure Bal orange and blue-green). Interestingly, the difference between the superposed outputs, i.e., the outputs of the MLP given the superposed inputs, and the Gaussian outputs is much smaller, with only slightly greater sparsity at a given level of explained variance. This suggests that the outputs randomly initialized MLPs have relatively high level of sparsity insensitive to the input distribution. We consider other sparsity measures and hyperparameters in Appendix[]
4.3 Do TOKEN EMBEDDINGS EXHIBIT SUPERPOSITION?
To the extent that randomly initialized neural networks preserve amplify superposition, our results (Section 3) could be explained by the degree to which the inputs to transformer language models exhibit superposition. We study this question by applying the procedure described in Section 4.2 to language data. In particular, we train SAFS on pre-trained GloVe word vectors, the embedding matrices of Pythia models, the results of passing these inputs to randomly initialized two-layer MLP, and Gaussian controls. The setup is unchanged from Section 4.2, except that the number of data points is fixed by the number of word embeddings or tokens, and we use a single random seed.
We find that the gap between the Pareto frontiers of the GloVe word vectors and the corresponding Gaussian controls (Figure 5b) is smaller than that observed for the toy superposed datasets described again that the Pareto frontiers for both inputs improve when they are passed to a randomly initialized two-layer MLP, emphasizing the possibility that random NNs 'sparsify" their inputs (i.e., increase the degree of apparent superposition).
in Section 4.2 (Figure Sap. More interestingly,
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(b) 300-dim. GloVe vectors (Pennington al. 2014).
Figure 5: Pareto frontiers of the explained variance against the 7,7 norm divided by the square root of the L norm (sparsity) for datasets perhaps exhibiting superposition, Gaussian controls with the same mean and variance, and the corresponding outputs when these are passed to randomly initialized two-layer MLP (Section 4.2 Each point denotes a choice L penalty coefficient.
LIMITATIONS
In this work, demonstrate that tutt-interprettbblityy measures can produce apparently meaningful, interpretable results for SAES trained on randomly initialized models, which are unlikely to exhibit computationally interesting features. Given the impossibility of testing across all datasets and model architectures, we strategically focused on the Pythia family of models, widely adopted in mechanistic interpretability research (e.g., Paulo and Belrose 2025 Ghilardi et al. 2025 Mueller 2024), and the RedPajama- V2 dataset, representing typical pre- training data for language models and SAES.
While we used the default model for generating explanations in the EleutherAl auto-interpretability framework (Caden et al., 2025). exploring alternative models could yield valuable insights into aggregate behaviors and the quality of generated explanations. Importantly, we do not claim that SAEs fail to capture information from trained Transformers above and beyond randomly initialized transformers; only that aggregate auto-interpretability measures do not necessarily indicate the existence of interesting underlying features.
CONCLUSION
In this work, we applied sparse autoencoders to both trained and randomly initialized transformers and evaluated them with a suite of common quantitative metrics. Our central empirical finding is that, under certain conditions, these metrics particularly aggregate auto-interpretabiliiy scores can be surprisingly similar in both settings. While we observe that features derived from trained transformers are qualitatively more complex and abstract, especially later layers, these aggregate metrics often fail to capture this distinction.
This result does not imply that SAEs trained on real models fail to learn meaningful computational features. Rather, it reveals a limitation in our current evaluation methods. High aggregate auto- interpretability scores are insufficient proof for the discovery of complex, learned computations: they may instead reflect simpler structure inherent in the data model architecture that is preserved even by random weights. Our analysis of token distribution entropy, while preliminary, serves proof-of-concept it successfully revealed differences in feature 'abstractness' that aggregate iuto-interpreetabiliy scores missed. Future work should focus on developing more robust metrics that quantify the computational significance of the features SAEs discover. Our work reaffirms

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the importance of benchmarking interpretability techniques against strong. appropriately constructed null models, such as the randomly initialized transformers used here. Without such baselines, is difficult to confidently attribute discovered features to the process of learning.
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14

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A BROADER IMPACT
This work investigates a method currently used for mechanistic interpretability of LLMs, yielding results that challenge certain assumptions about sparse autoencoders. By demonstrating that SAEs can produce similar aggregate auto-interpretability scores for both random and trained transformers, our findings raise important questions about what these SAE evaluation methods are actually capturing.
By better understanding the metrics of SAE quality, we hope that this work will contribute to more informed search better SAE-like methods and thus help make these models more interpretable and to mitigate the potential harm these models could cause. Since our work is an empirical study of the capabilities of a presently used method, and it shows that the method provides interpretation of both random and trained transformers, we think the risk that this work could lead to negative social impact is minimal.
B AUTO-NTERPPRTTABIIIYY ROC CURVES
Figures 6
g
12 show similarity between 'fuzzing' AUROC for the trained and randomized
SAEs for the 70M, 160M, and IB models. Figures 7 9 T3 show the similarity between 'detection'
AUROC for the trained and randomized SAEs for the 70M, 160M, and 1B models.
B.1 PYTHIA 70M
Fuzz ROC Curves (100M tokens)
Layer
Layer 2
Layer
Layer
Tiee Poniin
Layer 5
Layer 6
Troe Pootte
0.63) (AUC= Step
0.44)
0.49)
Figure 6: ROC curves for 'fuzzing' auto-interpretabiliiy for Pythia- 70m over 100 SAE latents. These results demonstrate the similarity in performance between the SAE variants, well as the overall degradation performance as the layer index increases.
15

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Delection ROC Curves (100M tokens)
Layer
Layer 2
Layer
Layer 4
Trac Pootiive
Layer 5
Layer
Toe Posiive
Positive Rate
False Positive Rak:
Trined (AUC 0.58)
Control (AUC = 0.48)
Randomizee cmb (AUC= 0.50)
0.50)
Step (AUC 0.61)
Figure 7: ROC curves for 'detection' auto-interpretabiliiy for Pythia-70m over 100 SAE latents. These results demonstrate the similarity in performance between the SAE variants, as well as the overall degradation in performance as the layer index increases.
B.2 PYTHIA 160M
Fuzz ROC Curves (100M tokens)
Layer
Layer
Layer
Layer
Thme Postivv Raie
Layer 8
Layer 10
Layer
The Posieve
Folsc Positive Rale
False Positive Rale
False Posilve Rate
Trained (AUC 0.63)
Control (AUC = Q:45)
Randomized inct emb (AUC 0.55)
Randomized exel emb (AUC = 0.56
Step (AUC 0.73)
Figure 8: ROC curves for 'fuzzing' auto-interpretability for Pythia- 160m over 100 SAE latents. These results demonstrate the similarity in performance between the SAE variants, as well as the overall degradation in performance the layer index increases.
16

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Delection ROC Curves (100M tokens)
Layer
Layer 2
Layer 4
Layer
Thm Proditve
Layer 8
Layer
Layer
Toe Posiivve
Positive Rate
False Positive Rake
False Positive
Trined (AUC 0.57)
Control (AUC = 0.49)
Randomizze cmb (AUC= 0.55)
0.54)
Step (AUC 0.68)
Figure 9: ROC curves for 'detection' cittriittepretabilitty Pythia- 160m over 100 SAE latents. These results demonstrate the similarity in performance between the SAE variants, as well as the overall degradation in performance as the layer index increases.
B.3 PYTHIA 410M
Fuzz ROC Curves (100M tokens)
Layer
Layer
Layer
Layer 10
The Posiive
Layer 14
Layer 18
Layer 22
Layer 24
Tne Pesibve Rale
Fabe Posiéve Rate
Folse Positvv Rale
Folse Pesitive Rute
Folse Posiiive Rade
0.65)
0.44)
0L65)
Randemized exd cmb(AUC= 0.63)
Step 0 (AUC 0.77)
Figure 10: ROC curves for 'fuzzing' auto-interpretabiliiy for Pythia-410m over 100 SAE latents. These results demonstrate the similarity in performance between the SAE variants, as well as the overall degradation in performance as the layer index increases. The nuto-interpretability scores here fail to distinguish between trained and randomized models.
17

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Delection ROC Curves (100M tokens)
Layer
Layer 2
Layer
Layer 10
Thm Pooii
14
Layer 18
Layer
Layer 24
Toe Postiee Rate
Positive Rate
False Positive Rak:
False Positive
False Positive
Trined (AUC 0.60)
Control (AUC = 0.49)
Randomized cmb (AUC 0.64)
0.60)
Step 0.70)
Figure 11: ROC curves for 'detection' auto-interpretability for Pythia-410m over SAE latents. These results demonstrate the similarity in performance between the SAE variants, as well as the overall degradation in performance as the layer index increases.
B.4 PYTHIA-1B
Fuzz ROC Curves (100M tokens)
Layer
Layer
Layer
Layer
Postiive Raie
Layer 9
Layer 11
Layer
Layer
Posilvve
Folse Positive Rale
False Positive Rale
False Posilve Rate
Falsc Postlive Rote
Trained (AUC 0.77)
Control (AUC = Q.19)
Randomized inst emb (AUC 0.82)
Randomized exel emb (AUC 0.83)
Step (AUC 0.84)
Figure 12: ROC curves for 'fuzzing' auto-interpretabiliiy for Pythia-Ib over 100 SAE latents. These results demonstrate the similarity performance between the SAE variants, although here we do not observe overall degradation in quality.
18

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Delection ROC Curves (100M tokens)
Layer
Layer 3
Layer
Layer
Thm Pooiiive
Layer 9
Layer 11
Layer
Layer 15
Toe Possite
Positive Rate
False Positive Rak:
False Positiv
False Positive
Trined (AUC 0.72)
Control (AUC = 0.51)
Randomized cmb (AUC= 0.77)
0.76
Step (AUC 0.79)
ROC curves for 'detection' auto-interpretabiliiy for Pythia- lb over 100 SAE latents. These results demonstrate the similarity in performance between the SAE variants, although here do not observe an overall degradation in quality.
Figure 13:
B.5 PYTHIA 6.9B
Detection ROC Curves (100M tokens)
Layer
Layer
Layer
Layer
True Positie Raie
Layer 17
Layer 21
Layer
Layer
The Pooiin Rad
Folsc Positive Rale
False Positive Rale
False Posilve Rate
Falsc Postive Rote
Trained (AUC 0.72)
Control (AUC = 0.54)
Randomized inct emb (AUC 0.83)
Randomized excl emb (AUC 0.82)
Step (AUC 0.84)
Figure 14: ROC curves for 'detection' auto-interpretabiliiy for Pythia-6.9b over 100 SAE latents. These results demonstrate the similarity in performance between the SAE variants.
19

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C EFFECT OF INCREASED TRAINING DATA
For our primary experiments, we trained SAES on 100M tokens (Section We verified that our results were not explained by lack of sufficient training data by repeating a subset of these experiments with SAEs trained on IB tokens from the RedPajama dataset (Figure 15
70M
410M
(R3)
Explainel Varietze
64
10-
Coxine Similwitt
0.9
0.
67
06
10
L1 Nocn
AUROC (Pun))
07
66
Tokon Dieributio Emirpyy CE (Tained) AUROC (Datectio)
0.8
6.
1.00
0.94
0.6
0.
02
9
Layer
Layer
Step O
Control
Trined
Randomized exel mmb
Randomized indl emh
Figure 15: Evaluation metrics for SAEs trained with one billion tokens on the Pythia- 70m and 410m models. These results correspond to columns of Figure which show the same evaluation metrics for SAEs trained on 100M tokens, and qualitatively similar behavior.
20

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D EFFECT OF DECREASED TRAINING DATA FOR PYTHIA-1B 1B
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potto-ibdaaqeed put-04 2-1 Rustemiaddrd unc 10M po (p6e54 Tistned
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Comparison Token (100IM, 100M.
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Figure 16: Evaluation metrics for SAEs trained with 1M and IB tokens on Pythia- lb. The explained variance and CE loss score are significantly lower for the 1M model, showing that the SAEs are under-trained. Average intrrinterpretabiliiyy scores are slightly lower for the earliest layers, but decline sharply with increasing layer. The trends in auto-interpretabiliiy and token distribution entropy with layer index are consistent with other SAEs.
21

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E UNCERTAINTY PLOTS FOR PYTHIA-70M
We computed uncertainty for our evaluation metrics on Pythia-70m 70m using five random seeds.
AUROOC (Pua))
AUDOC (Dotstie))
0.7
Exilaied Vaizzee
Couite Stullett
10
6.10
4.10
1.30
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3.00
Tokon Dianbaikk Eaburry
005
Trited
Raslonized ench
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Stop
Renntomized inc each
Figure 17: Uncertainty for Pythia- 70m metrics computed using five random seeds.
22

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F EFFECT OF SAE HYPERPARAMETERS FOR PYTHIA-160M 160M
Expansion Factor Trends
k Trends
Explained Variance (R9)
0.6
0.4
0.2
16
32
64
128
16
32
1.0
Cosine Similarity
0.7
0.6
16
32
64
16
32
6x 10*
L. Norn
4 101
3X10
2x 2X
1
Expansion Factor
Control
Randomized exel emh
Randomized incl emh
Step 0
Trained
Figure 18: Robustness of SAE performance to hyperparameter selection. Standard evaluation metrics remain stable across a wide range of expansion factors R (16 to 128) and sparsities & (16 to 32), with all initialization strategies maintaining their relative performance ordering. This stability suggests that moderate hyperparameter values (e.g., expansion factor R = 64, sparsity k 32) suffice.
23

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G EFFECT OF SAE HYPERPARAMETERS FOR PYTHIA-1B
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(ytia-t--dopppl (tm64 ke33), Rusdiniaddo unb 100M
Pythia 1B Metries ROC Comparisoe Hyperparamctr Variants (R, k) 100M tokens
32
12
14
Layer
Dunturrn
14.
NamE
(Taiaal)
1.0
1.0
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1.0
0.-
Figure 19: Evaluation metrics for SAEs trained on the Pythia- 1b model with different hyperparame- ters, including the main results from Figure SAEs with very small expansion factor R 2 and sparsity l = 4 are clearly distinguished from our default hyperparameters by the explained variance and CE loss score. Importantly, the antoiinterpretabiiity scores of these SAEs remain similar to those trained with default hyperparameters either trained or randomised models.
24

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H TOKEN DISTRIBUTION ENTROPY VS. AUTO-INTERPRETABILITY
Randomized
Randomized
Control
excl. embeddings
incl. embeddings
AUROC (Fuzz)
0.8
0.6
0.4
0.2
Step
Trained
AUROC (Fuzz)
0.8
0.6
0.4
0.2
0.2 0.4 0.6 0.8
0.2 0.4 0.6 0.8
Token distribution entropy Token distribution entropy
Figure 20: Scatter plots of the per-latent token distribution entropy against 'fuzzing' AUROC (auto- interpretability score) for SAEs trained on multiple layers of the Pythia-6.9b model. Each point corresponds to single latent, taken from the sample of latents used to compute the aggregate metrics displayed in Figures]
anddd 2
Figure 20 clearly distinguishes the negative control, randomized variants, and the trained variant:
Control: Latents have a consistently high entropy (i.e., max activating examples with activation patterns spread across many tokens) and low auto-interpeetability score (i.e., gen- erated explanations that fail to adequately explain these activation patterns). No correlation between the two variables is evident.
Randomized: For each of the randomization schemes described in Section 3 we see negative correlation between entropy and auto-interpretabblityy in general, the wider variety of tokens for which latent is activated, the less well the latent's activation patterns explained by its generated explanation.
Trained:
There is a weaker correlation between the two variables. Crucially, addition to the broad trend observed for the randomized variants, we also see latents with high entropy and auto-intent tittoonttrrretabliity... Some latents have activation patterns that are spread across multiple tokens, which are nevertheless consistent with the latent's generated explanation.
These results are consistent with the view that aggregate into-interpretabiliiy scores obscure the differences between SAEs based on trained and randomized models. While randomized models with consistent token embeddings can produce 'single-token' features, whose activation patterns are easy to explain, only Transformers trained on natural language produce more complex semantic features.
25

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I A TOY MODEL OF SUPERPOSITION
In Section 4 we trained SAEs on toy data designed to exhibit superposition (Sharkey et al. (022) and GloVe word vectors (Pennington al., 2014). this section, detail the data-generation procedure and training setup.
I.1 DATA GENERATION
First, we construct ground-truth features by sampling nt points on an nu.-dimensional hypersphere.
For each sample, we determine the feature coefficients by generating A E R Xn, where Aij (0,, 1), defining covariance matrix E AAT, sampling ã TR where oş NN E), projecting a onto the c.d.f. of (0, 1), decaying a; + a² where À R, normalizing a, mai /n, Σ, aj where E R, and performing independent Bernoulli trials with p oç. Finally, we multiply the trial outcomes by n, independent samples from a continuous uniform distribution U(..1)-
The parameter À determines how sharply the frequency of nonzero ground-truth feature coefficients decays with the feature index i. The parameter m is the expected value of the number nonzero feature coefficients for each sample.
Like Sharkey et al. (0022), we choose n, = 512, nd = 256, = 0.99, and m = 5. We include a Python implementation of this procedure in Figure 21
1.2
TRAINING
The SAEs described in Section comprise linear encoder with bias term, a ReLU activation function, and a linear decoder without bias term. We use orthogonal initialization for the decoder weights and normalize the decoder weight vectors before each training step.
The training loss is the mean squared error (MSE) between the input and decoded vectors, plus the mean I norm of the encoded vectors multiplied by coefficient, which we vary between 1 x 10-3 and 100.
For the toy data, we train for 100 epochs 10K data points with 10 random seeds. For the word vectors, we train for 100 epochs on 400K data points with random seed. both experiments, we reserve 10% of the data points
validation set, which we use compute evaluation metrics.
The MI.Ps described in Section 4 comprise two layers (i.e., one hidden layer) and a ReLU activation function. The input and output sizes are both equal to n4, and the hidden size is 4n We loosely based these choices on the feed-forward network components of transformer language models.
26

---

def senerate_sharkee num_samples: int, num_inputs: int,
num_features: int, avg_active_features: float,
lambda_decayy float, >> tuple[Tensor, Tensor]:
Args:
10
num_samples (int): The number of samples generate.
11
num_inputs (int): The number input dimensions.
12
num num_features (int): The number of ground truth features. (float): The average number of time.
eve_active_featu ground truth features active lambda_decay (float): The exponential decay factor for feature probabilities.
16
17
18
torch. randn (num (num_inputs num_features) features /= torch norm (features, dim=0, keepdim=True)
features
19
20
covariance torch randn (num_features, num_features)
covariance covariance @ covariance. correlated_normal MultivariateNormal torch zeros (oum_fattures] covarianee_marrkkcccviiii
25
26
27
samples []
28
for
in range aaaummmmmmseeeeee samples))
29
p HAANDARD_NORMAN cdf (correlated_ powpatteddddormaalooooe sample ())
30
p p p p (avg_active features (num_features p. mean ))))
(lambda_decall torch arange (num_features)
berchbbernoullipp clamp (8, 1))
coef * torch. rand (num_features)
35
sample coef @ features.
36
samples append (sample)
37
38
return torch. stack (sampless) features
Figure 21: A Python implementation of the data-generation procedure introduced by Sharkey et al. (2022) and used in Section
27

---

512 Latents
1024 Latents
Explained Variance
0.5
100
200
300
400
200
400
600
800
2048 Latents
4096 Latents
Explained Variance
0.5
0
200
400
600
800
1,000
1,200
500
1,000
1,500
L Norm
L Norm
Superposed inputs
Superposed outputs
Gaussian inputs
Gaussian outputs
Figure 22: Pareto frontiers of the explained variance against the L norm (sparsity) for toy datasets generated to exhibit superposition, Gaussian controls with the same mean and variance, and the corresponding outputs when these are passed randomly initialized two-layer MLP.
512 Latents
1024 Latents
Explained Variance
0.5
100
200
50
100
150
200
250
2048 Latents
4096 Latents
Explained Variance
0.5
0
50
100
150
200
50
100
150
200
T,A Norm
I,1 Norm
Superposed inputs
Superposed outputs Gaussian outputs
Gaussian inputs
Figure 23: Pareto frontiers of the explained variance against the Lt norm (sparsity) for toy datasets generated to exhibit superposition. Gaussian controls with the same mean and variance, and the corresponding outputs when these are passed randomly initialized two-layer MLP.
28

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512 Latents
1024 Latents
Explained Variance
0.5
20
40
60
80
20
60
80
2048 Latents
4096 Latents
1
Explained Variance
0.5
20
40
60
80
20
40
60
80
L Norm Over VL2 Norm
L' Norm Over /L² Norm
Superposed inputs
Superposed outputs
Gaussian inputs
Gaussian outputs
Figure 24: Pareto frontiers of explained variance against the I norm over the square root of the r norm (sparsity) for toy datasets generated to exhibit superposition, Gaussian controls with the same mean and variance, and the corresponding outputs when these are passed to randomly initialized two-layer MLP.
29

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512 Latents
1024 Latents
Explained Variance
0.5
1
0.8
0.6
0.4
0.8
0.6
0.4
2048 Latents
4096 Latents
Explained Variance
0.5
1
0.8
0.6
0.4
0.9
0.8
0.7
0.6
0.5
Hoyer Sparseness
Hoyer Sparseness
Superposed inputs
Superposed outputs
Gaussian inputs
Gaussian outputs
Figure 25: Pareto frontiers of explained variance against the Hoyer sparseness (sparsity) for toy datasets generated to exhibit superposition, Gaussian controls with the same mean and variance, and the corresponding outputs when these are passed a randomly initialized two-layer MLP.
30

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100 Latents
200 Latents
400 Latents
800 Latents
Explained Variance
0.5
50
100
0
50 100
150 200
100 200
300
0 200
400
600
L Norm
L Norm
L Norm
L" Norm
Explained Variance
0.5
50
100
0
50
100
100
0
20
L Norm
L Norm
L' Norm
L' Norm
Explained Variance
0.5
10
20
30
0
10
20
30
20
40
0
40
L Norm Over /L2 Norm
I Norm Over VI22 Norm
Explained Variance
0.5
0.8 0.6 0.4 0.2
1 0.8 0.6 0.4 0.2
0.8 0.6
0.4 0.2 1
0.8
0.6
0.4
Hoyer Sparseness
Hoyer Sparseness
Hoyer Sparseness
Hoyer Sparseness
GloVe inputs
GloVe outputs
Gaussian inputs
Gaussian outputs
Figure 26: Pareto frontiers of explained variance against sparsity measures for 50-dimensional GloVe word vectors, Gaussian controls with the same mean and variance, and the corresponding outputs when these are passed to randomly initialized two-layer MLP.
31

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200 Latents
400 Latents
800 Latents
1600 Latents
Explained Variance
0.5
100
150
200 0
100 200
300 400
200 400 600
500
1,000
L Norm
L Norm
L Norm
1 Norm
Explained Variance
0.5
100
150
200
0
100
200
100
200
0
50
100
150
200
L' Norm
L Norm
L' Norm
L' Norm
Explained Variance
0.5
20
40
0
20
40
60
20
40
60
0
20
40
60
L Norm Over /L² Norm
I Norm Over VI.2 Norm
Explained Variance
0.5
0.8 0.6 0.4 0.2 Hoyer Sparseness
1
0.6 0.4 0.2 Hoyer Sparseness
0.8 0.6 0.4 0.2
0.8 0.6 0.4
Hoyer Sparseness
Hoyer Sparseness
GloVe inputs
GloVe outputs
Gaussian inputs
Gaussian outputs
Figure 27:
Pareto frontiers of explained variance against sparsity measures for 100-dimensional GloVe word embeddings, Gaussian controls with the same mean and variance, and the corresponding outputs when these are passed to a randomly initialized two-layer MLP.
32

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400 Latents
800 Latents
1600 Latents
3200 Latents
Explained Variance
0.5
100 200
300
400 0
200 400 600 800
500
1,000
1,500 0
500 1,0001,500 ,500
L Norm
L Norm
Norm
1.0 Norm
Explained Variance
0.5
100
200
300
100 200 300 400
100 200
300
400 0 100 200
300
400
L' Norm
L Norm
L' Norm
L' Norm
Explained Variance
0.5
0
20
40
60
80
50
100
50
100
0
50
100
L Norm Over VIA Norm
L Norm Over VIA Norm
Explained Variance
0.5
0
0.8 0.6 0.4 0.2
0.8 0.6 0.4 0.2
0.8 0.6 0.4 0.2
0.8
0.6
0.4
Hoyer Sparseness
Hoyer Sparseness
Hoyer Sparseness
Hoyer Sparseness
GloVe inputs
GloVe outputs
Gaussian inputs
Gaussian outputs
Figure 28: Pareto frontiers of explained variance against sparsity measures for 200-dimensional GloVe word embeddings, Gaussian controls with the same mean and variance, and the corresponding outputs when these are passed to a randomly initialized two-layer MLP.
33

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600 Latents
1200 Latents
2400 Latents
4800 Latents
Explained Variance
0.5
0
200
400
600
500
1,000
0
1,000
2,000
1,000
2,000
L" Norm
7 Norm
1. Norm
7,0 Norm
Explained Variance
0.5
0
200
400
200
400
200 400
600
200
400
600
L' Norm
L Norm
L' Norm
L' Norm
Explained Variance
0.5
0
50
100
50
100
50
100
50
100
150
L Norm Over VIA Norm
L Norm Over VLA Norm
Explained Variance
0.5
0
0.6 0.4 0.2 Hoyer Spanseness
0.8 0.6 0.4 0.2
0.8
0.4 0.2
0.8
0.6
Hoyer Spanseness
Hoyer Sparseness
Hoyer Sparseness
GloVe inputs
GloVe outputs
Gaussian inputs
Gaussian outputs
Figure 29: Pareto frontiers of explained variance against sparsity measures for 300-dimensional GloVe word embeddings, Gaussian controls with the same mean and variance, and the corresponding outputs when these are passed to a randomly initialized two-layer MLP.
34

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J EXAMPLE FEATURES FOR PYTHIA-6.9B
VARIANT: TRAINED
FEATURE 180935 (LAYER 0)
The term "security" is predominantly used refer protection, safety, and measures to prevent harm, while "oz" is likely referring to ounces, possibly in context of measurement or quantification, although "oz" appears less frequently and often in different context.
Interpretation:
Top Examples:
Text: kendoftext- If for any reason you are unhappy with our service please contact us
1. directly so we can make it right for you.Jouraal of Cyber Security, Vol. Activation: 4.3750
Active tokens: Security
Text:
<endoftext-i< ;- Security Practitioner. Tremendously passing CompTIA Advanced
2. Security Practitioner (casp) cert has never been as easy as it
Activation: 4.3125
Active tokens: Security Security
Text: in trusted hands for your Cyber Security career or staffing needs. Call 0203 643 0248 to find out more. Technically proficient using
3. Activation: 4.3125
Active tokens: Security
FEATURE 93790 (LAYER 8)
Interpretation:
Nouns and phrases related economic concepts, development, and business, often referring to growth, progress, and improvement.
Top Examples:
Text: training requirements. See "Workforce" section for additional information. The Economic
1. Development Transportation Fund, commonly referred to as the "Road Fund," is an Activation: 21.3750
Active tokens: Development
Text: Montréal, The Williamsburg Economic Development Authority offers a 33% matching
2. grant up $7,500 for exterior improvements to existing businesses the City of
Activation: 20.2500
Active tokens: Development Authority
Text: Correction: In July 16 web story The Real Deal incorrectly stated that the Economic
3. Development Corporation was "circumventing" laws with its restructuring. In
Activation: 20.0000
Active tokens: Development
FRATURE 128309 (LAYER 12)
Various types of punctuation and grammatical elements that separate words or phrases, including hyphens, commas, ellipses, prepositions, and determiners, often indicating connec- tions, contrasts, or clarifications, and sometimes marking boundaries or transitions between clauses or ideas.
Interpretation:
Top Examples:
Text: Run it in JDK6, and it will print "[axons, bandrils, chumblies]". If you are having trouble switching from
Activation: 8,0000
Active tokens: in JD K
35

---

Text: Here, we introduce the coordinate systems for caree-dimensional space 0002. The study of 3-dimensional spaces lead us to the setting for our study
2. Activation: 7.8125
Active tokens:
Text: -pppt.hhee. expanduser""mmallvrrtoouus because this server doesn't it have X- Windows running.
If you are looking for simple and
3. Activation: 7.7188
Active tokens: path expand user
VARIANT: STEP 0
FEATURE 126848 (LAYER 12)
Nouns denoting people who train others, units or marks of measurement, and abbreviations or acronyms representing specific standards or technologies.
Interpretation:
Top Examples:
Text: What the various lessons member access at tennis club? Whether you beginner or advanced player, trainers help you to choose the right gaming
1.
Activation: 13.1250
Active tokens: trainers
Text: report include various simulation platforms and Serious Games. The report also analyzes some major allied products such as patient simulators and task trainers. The technologies
2. analyzed
Activation: 13.0625
Active tokens: trainers
Text: stylish spring in your step when you buy from our fantastic range of men's and women's
3. Asics trainers. We've ve got numerous styles from
Activation: 13.0000
Active tokens: trainers
FEATURE 2125 (LAYER 4)
Interpretation:
The word "papers" is often used in contexts referring to written documents, such as academic papers, court documents, printed materials, and is frequently mentioned in relation to tasks like writing, research, and education.
Top Examples:
Text: caustic solution. As an abrasive, alumina is coated into abrasive papers and Pakistan. Sierra leone. Taiwan. Turkey. Venezuela.
1. Activation: 5.7188
Active tokens: papers
Text: that can be associated with interaction with other individuals. For everybody who uncertain regardless of whether your papers is misstep no cost, buy inexpensive experienced
2. proofreading services
Activation: 5.6875
Active tokens: papers
Text: who RV, often traveling in groups, often alone. You just want have all the papers like
RC, licence and insurance coverage as effectively as PUC
3. Activation: 5.6562
Active tokens: papers
36

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FEATURE 9944 (LAYER 16)
Words or parts of words that are usually the beginning or end of a proper noun, surname, or word of foreign origin.
Interpretation:
Top Examples:
Text: astic gestures. Home Music World News Music the artform Where Do Music Festivals Go Now? Where Do Music Festivals Go Now? Are you ready
1. Activation: 10.4375
Active tokens:
Fest Fest
Text: client streams, and encouraging existing customers become more involved. Welcome
Fil Fest USA!! Do you love lumpia? Can you eat a handful of them
2. Activation: 10.3750
Active tokens: Fest
Text: Powers talking about his early inspirations. In response to San Diego Comic Fest 2012!:
Off to Comic Fest 2012. Should be interesting if nothing else!
3. Activation: 10.3750
Active tokens: Fest Fest
VARIANT: RANDOMIZED EXCLUDING EMBEDDINGS
FEATURE 151030 (LAYER 28)
Interpretation: Common nouns, proper nouns, or adjectives found in various contexts, including but not limited to geographical locations, people, organizations, time, and concepts, often possessing relevance to the surrounding text.
Top Examples:
Text: ion Patch Kills
Owner, Son,", Los Angeles
Times,
June
12, 1994,
1. http///ttiles..aatmss..oo
06-12
Activation: 58.0000
Active tokens: lat
Text: hard-headed coin of the realm their look. Firstly you've 'ue'e got to inventory on incident unique distinct blunt in retailer you superlativeness be
2. Activation: 56.5000
Active tokens: lat
Text: Jr's new film Dovlatov follows the life of the now celebrated writer Sergei Dovlatov over six days in 1971, as he struggles
3. Activation: 55,7500
Active tokens: lat lat
FEATURE 98924 (LAYER 12)
Interpretation: Adjectives describing size, or nouns representing concepts or objects that are being described in terms of their size.
Top Examples:
Text: The area to the right is for large dogs (small dogs also welcomed) however the area to the left is for small dogs only. Troup 69
1. Activation: 46.0000
Active tokens: small
Text: would not mind, but has to pretty less expensive. Can it use any windows aplication????
Or am really need cool,small
2. Activation: 45.5000
Active tokens: small
37

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Text: 3/4" - 1/2" rather than strictly 8 1/4") chose the "small" version, though should be 3. Activation: 44.7500
Active tokens: small
FEATURE 180589 (LAYER 24)
Interpretation: Nouns mostly referring to tasks, responsibilities jobs to be accomplished, often in a professional or organizational context, sometimes accompanied by proper nouns and a few instances with words having suffixes or prefixes.
Top Examples:
Text: completing tasks or captcha, users are awarded by GRSfractions. Why These Groestlcoin
1. Faucets provide rewards? Many people
Activation: 130.0000
Active tokens: tasks
Text: from Groestlcoin Faucets is In the exchange of completing tasks or captcha, users
2. awarded by Free GRS. To Earn
Activation: 129.0000
Active tokens: tasks
Text: and still contain small remnant circular genome, known mitochondrial DNA. Of the varied tasks undertaken by mitochondria, the most important the generation of the chemical
3. energy
Activation: 128.0000
Active tokens: tasks
VARIANT: RANDOMIZED INCLUDING EMBEDDINGS
FEATURE 39748 (LAYER 0)
Interpretation:
Words contain "Pul" are often used in the context of Pulitzer, a prestigious journalism award, while "Looking" typically precedes phrase expressing anticipation, expectation, or searching for something.
Top Examples:
Text: kendoftext-i-- to the 21 Century. Rhodes won the Pulitzer prize for The Making of the Atomic Bomb (01987) his first of four books chronicling the
1. Activation: 3.3750
Active tokens: Pul
Text:
<endoftext-2z James Coburn, movies we love, Pulp Consumption. Steve McQueen,
2. Western, Yul Brynner. Bookmark the permal
Activation: 3.3438
Active tokens: Pul
Crusher, Coal Mill and Coal Pulverizer for sale Coal crusher and coal mill is the major 3. mining equipment in sbm ceramic machinary
Text:
Activation: 3.2969
Active tokens: Pul
FEATURE 15633 (LAYER 20)
Interpretation: Nouns representing individuals entities possessing having authority something, often in a possessive or authoritative relationship with that thing.
Top Examples:
38

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Text: poetry. The Starkville/Missisiipi State University Symphony Orchestra kicks off 2012
1. with a Jan. 21 concert dedicated to parents of the performing musicians. The free
Activation: 80.0000
Active tokens: Stark
Text: Baptist. Kris Kirkwood, Stark Raving Solutions' lighting designer, says the architectural
2. system used, ETC's Paradigm architectural control, is
Activation: 77.5000
Active tokens: Stark
Text: so you can be as cool as Tony Stark. The Marvel Training Academy will taking place
3. throughout May, just check with your local shop guarantee your place
Activation: 77.5000
Active tokens: Stark
FEATURE 6069 (LAYER 4)
Proper nouns, nouns referring objects or places, and nouns with strong semantic connotations often related to religion or technology.
Interpretation:
Top Examples:
Text: in production include Bullfinch's 's Mythology: Age of Fable, The Story of Dr. Doolittle, and a collection Hans Christian Anderson fairy
1. Activation: 22. 1250
Active tokens: Christian
Text: reaction of the remaining flock remains the same: ostracism, shunning, even retaliation.
2. So yeah, Christian leaders won't 't make any big
Activation: 22.0000
Active tokens: Christian
of the best loved characters in the film. Walt Disney attempted as far back 1937 3. to adapt the Hans Christian Anderson fairy tale, The Snow Queen into
Text:
Activation: 22.0000
Active tokens: Christian
VARIANT: CONTROL.
FEATURE 290 (LAYER 4)
Interpretation: Function words and occasionally nouns or proper nouns that seem to be emphasized as part of a larger phrase or topic, often indicating transition or conjunction.
Top Examples:
Text: <endoftext-i- ance Chapters:
Words:
Fruits Basket
Rated: T
English
1. Romance//ngst- Chapters:
Activation: 5.0938
Active tokens:
Text: ococeus neoformans-reann reactive and total immunoglobulin profiles of human immunodefi- ciency virus-infected and uninfected Ugandans'. Clinical and Diagnostic Laboratory Immunol-
2. ogy, Vol 12
Activation: 5.0938
Active tokens: un
Text: and in fact any correspondence that the social club had in the run-up to the sit-in was from
3. the social club's 's own solicitors.
Activation: 5.0625
Active tokens: the
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FEATURE 176433 (LAYER 24)
Function words and common words including prepositions, articles, and verb forms that connect clauses or phrases, as well as nouns that represent various objects and concepts, often in specific contexts or idiomatic expressions.
Interpretation:
Top Examples:
Text: forgo insurance. Ultimately, that choice is up you. By understanding these aspects of the Republican tax plan, you can save big on your taxes in
1. Activation: 6.9062
Active tokens: taxes
ations Without fettine klusia wywiader hitch conselheiro amoroso online paul. In France, 2. Germany, Belgium, Luxem
Text:
Activation: 6.8438
Active tokens: wi
Text: K-ras oncogene and also via mutations in BRAF. Several allosteric activated
3. protein/extracelluaar signal-regulated kinase (ME
Activation: 6,5000
Active tokens:
FEATURE 203901 (LAYER 20)
Commonly emphasized tokens include determiners, prepositions, adverbs, and adjectives, often in the context of written spoken English, sometimes using colloquial expressions.
Interpretation:
Top Examples:
Text: was an avid reader and fantastic cook. Susan was a brave and courageous woman who battled MS for over 40 years. Even given the limitations of her
1. Activation: 9.1875
Active tokens: given
Text: says that he doesn't really consider Battlerite to even be in the same category, and that it
will be fine on its own. Well
2. Activation: 9.1250
Active tokens: to
Text: to see dime of the funds. The transaction occurred mere hours before the doomed
3. exchange stopped honoring withdrawals. Tsao sold nearly 20 Activation: 9.1250
Active tokens:
40

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K COMPUTE DETAILS
We performed all experiments with single NVIDIA A100 80GB GPU in a private cluster. Table | lists the approximate duration of the final experiments for each model size and transformer variant. We estimate that the total cost of preliminary and failed experiments is roughly equal to the cost of the final experiments.
Model
Variants
Approx. time per variant (hours)
Total time (hours)
Pythia-6.9b
70
350
Pythia-lb
5
10
50
Pythia-410m
5
25
Pythia-160m
5
I
Pythia-70m
5
1
Overall time:
435
Table I: Approximate time required for our experiments.
L EXAMPLE FEATURE DASHBOARDS FOR PYTHIA-6.9B
Here we provide more detailed 'feature dashboards," including per-feature activation patterns, token distribution entropy, and auto-interpretability ('fuzz' ROC) scores. We include two randomly sampled features for the control, randomized, and trained variants described in Section trained on every fourth layer of Pythia-6.9b.
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L.1 TRAINED
Feature 10065 (Layer 0) Trained Entropy: 0.009 Fuzz
Interpretation: Prefixes or words starting with "Re" often indicating repetition, return, or renewal.
Ex 1
which JudgenKreep was censured, Brower says he has worked in diverse work places in the military and D A. office, also working with
Example: In Louros v KKreikas, 367 Supp. 2d 572 (S.D.N.Y. 2005), the
Ex 3
the Alh ra arches. Over the years, KKnaber has supplied the color separations, while printing services were provided by Century Graphics,
Ex 4
treated equal. What was the Statue of Liberty originally used for? Sh adows over Kragen Schatten über Kregen, 1996; English ebook edition
by Boston attorney A Kreiger, who represented AT&T Krieger explained the site choice was narrowed from 400 to three: 14 Sampson Ave
Activation (0:4.4)
Feature 10222 (Layer 0) Trained
Entropy: 0.050 Fuzz ROC: 0.970
Interpretation:
The token "inst" typically represents fragment word "install", "instill", "instigate", "instructions" or "instagram", and "intim" typically represents fragment of the word "intimidate" or "intimated",
indicating beginning related to fear or warning.
teaching, educating, influencing,
/Jndoffext| / sintim villa. Dua kamar tidur yang memil iii a ks langs ung Ex 1
ke kol am renang. Di set ap kamar
Ex 2
the non -Muslim im world more and more fold under their legal Antimidation as a result our pacifism, self rat complacency.
Ex 3
near Maneyal last October, they were ordered disband by force of 40 soldiers. The campaign of inttiiddtiion may have wo worked
Ex 4
least in part the attempt by the Railroad Commission of Arkansas to protect Arkansas shippers and build up Arkansas jobbing centers. In that case was intinmate
than half of those against people were assault cases, while nearly 45 per cent crimes of intimidation No person should have fear being violently attacked
Activation (0-8.0)
42

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Feature 100186 (Layer 4) Trained
Entropy: 0.096
Fuzz ROC: 0.993
Interpretation: The color "black' often describing modifying
product.
Ex 1
assessment of Robert Louis Stevenson's The Arrow 88 ) and one political (in 1938) Peace
address the question Europe Keep the
Ex 2
forum entitled 'The Next Ten Years: ist View Blackk America' at Macalester College on February 26, 1985. The event
Ex 3
collapse, the increasing racial inequity and highprofile police killings of una Bitak and Brown people, the persistence of global terrorism, à lar ges cale refugee crisis
Ex 4
pain were evident. On Sunday evenings when CBS covered the war in Vietnam 60 Minutes. Kent State. Martin Luther King assassinated. Black Panthers. The
Ex 5
with huge savings. Btackk jack badar bes known for casino de slmodovar del campo live music scene and entertainment gala casino millennium
Feature 102193 (Layer 4) Trained
Entropy: 0.233 Fuzz ROC: 0.841
Interpretation: Verbs
are part word including "assault" words with the "ass" or "aught" sound, and sometimes words related to violence intensity.
that has strong violent connotation,
Ex 1
met a similar fate the following year; more than 100 000 were slaughtered When the German women saw their men being defeated, they first slew their
2 </endofteet| >> been bolstered by the return of their Slaught il contingent. On back credible performance against Donegal Ulster, the
Ex 3
Kashmir. They are largely reared by tribe of nomadic people called the 'Changpa', ' At present, these goats rarely slaughte red
Ex 4
wearing the hat. also met Phelemon and his wife this year and they in photo in this update). They sl claghhtered chicken in
Ex 5
thrusts of his wings, heading for the incoming a army. He going to be sl rughtered as well, Nyx thought. He
12
Activation (0-14.2)
43

---

Feature 100186 (Layer 8) Trained Entropy: 0.097 Fuzz ROC: 0.797
Punctuation marks, specifically quotation marks, indicating the start of quotation or dialogue and often marking pause or transition in the narrative.
Interpretation:
Ex 1
ys s spanner was in her hand before she even thought about it. They rushed around the machine to find the
of the noise.
Ex 2
bring his death. Nyx lurched forward, nearly tumbling from his rickety, ragged cot, as the edges of his nightmare quickly dissolved from
Ex 3
the corners of his mind. me sat still, his hands grabbing the wooden bed rame tightly as he tried to catch his breath; tried to control his harmer
Ex 4 breathed a deep sigh as the shaking s stopped and the telescope resumed its slow, steady turn.
thud, she leapt down onto the workshop floor
Ex 5
whitewashed pine walls before I 'm jerked forward. Over the roar of the inferno I hear shouting in the da darkness, and we follow it, nearly
(0
Feature 10092 (Layer 8) Trained Entropy:
The second-persee person singular pronoun, typically context of direct address or instructional guidance, often implying the reader or user being given advice, options, or directions on how to proceed with particular action decision.
Interpretation:
Ex secure because we guarantee your privacy. Feel to get exclusive help, expert assistance and free painting quotes because are in trusted hands in Bussey
Ex 2
In addition, you?ll also get the choice of experiencing the Kuala Lumpur atmosphere night. You will
unforget
Ex 3 of work. Unless you mechanicallyy inclined individual, youu likely uncertain about the most cost -effectfee product. Fortunately, you
In addition, you can get full details of the rental including mul nultiple interior and photroorrpotogrreee detailed information from the prospective landlord.
of the unit and grounds and specific
Ex 5 in many ways! ! First, you will find yourself with whole lot of extra time to spend as you wish. addition, yyou will see your grades
44

---

Feature 100351 (Layer 12) Trained Entropy: 0.443
Fuzz ROC: 0.839
Interpretation: The token is often noun part file path, representing directory, filename, word that plays significant role in the context, including products, companies, concepts, and resources.
1
scripts/ / xf. script" ALpScript="scriptt zf. scrript /> </pre> where: *<b> name</ b> any
Ex 2
scripts/ df scrippt" smlScript="scriiig scrrpppt zipScript="scrippts sscript" interval= (="erval===0000 5000" /> </
Ex 3
4f php var / www htmb/ huake
188 D.
Use of undefined constant catid assumed 'catid
Ex 4
catid assumed 'atid' runtime/ Cache/
/n/
Ex 5
html/ y com sharev / d c960b e
Feature 100898 (Layer 12) Trained
Entropy: 0.164 Fuzz ROC: 0.967
Interpretation: Prepositions indicating relationship, possession, or origin, often non-English languages, particularly Spanish and Italian.
Ex 1
ash lheet Tamazight Dictionary: Tamazight English and English ight. EL libro comprendido como una unidad de ho
Tamaz
2
< </endofttet Iaas separado del entorno de bueno pues quer a saber si alcohol lampieza bosque verde)
Ex 3 uit, Victorian Farm film entier youtube. Enfin, j obtenu Lienidde confiance! Je viens de insc
Ex 4 <|endoftext watter relationship. Rsum La prsente tude comme objectif d examiner les rper cussions de la scheresse
Ex 5 </endfftext |> más nada.. 0 el conten ido de la carpeta HARBOUR 64 debo cop larla dent ro. de carpeta HARB
10
12
Activation (0-13.2)
45

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Feature 100102 (Layer 16) Trained
Entropy: 0.414 Fuzz ROC: 0.957
Numbers often used as identifiers, counters, or other reference values in formal documents. academic papers, and technical texts.
Interpretation:
Ex 1
damage towing service. Tourist Info rmation Office Maria Wör th Fund Seep romenade 5, 9082 Maria Wör
Ex 2
navigation systems Uster, Switzerland) or use the coordinates (47 360597
either enter the address (Hall en badweg 86 10
761 00845, whose registered office is located ROUBAIX CEDEX Publisher shall mean Arjo Solutions SAS
A. with registered office Ex 4 /or "carrier" refers to the company Moby S.p.A. in Largo Augusto ; 20122 Milano,
47814 Krefeld, Germany. Adolf Dembach St ras se 19 Tag Risskov Re j ser med p229 ; r229
Activation (0-18.9)
Feature 100493 (Layer 16) Trained
Entropy: 0.544 Fuzz ROC: 0.867
Interpretation: and prefixes related death, mourning, and memorials, indicating place, object, state.
words with suffixes
Suggestions for thematic issues and propased manuscripts are we welcomed, not using Word you should also funeral
pdf file of the entire article.
Ex 2 staff and resources are needed to service them. Funeral homes in Buras (LA) handle death every day and the funerat directors employed by them deal with
Ex no mistaking the add man out Washingtor funeral service for former President George H. W. Bush served as a rare reunion of the remaining members of
Ex 4 </endoftext|>, at the A. Bekavac Funerah Home Chapel, Clairton with the Rev. John MacLeod officiating.
Ex Funeral Burton Barber). There are only few hangers left of the old leaders. Now those age are also beginning to go Recently
10
15
20
35
40
Activation (0-44.5)
46

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Feature 100186 (Layer 20) Trained Entropy: 0.639 Fuzz ROC: 0.900
Interpretation: Initial or partial segments personal names, specifically sumname..
Ex 1 Lena Dunham is unquestionably one of Hollywood's 5 "it" girls, with an edge. The actress, di director, producer, and writer
Ex 2
you expect big things your dining experience. Brad Pitt and Ange lina Mobie custody battle now has a trial date. The
Ex 3
then over two and half years in Japanese POW camps Angelina Jobie directs. What: Clint Eastwood directs "American Sni
Ex 4
wwill fit into their roles. Viewers were especially excited to see Olivia Helena Bonham Carter as Princess Margaret
Colmmnn as Queen Elizabeth And
Ex 5
was being probed over an incident involving one of his children with wife Angelina dolie. Jolie announced earlier this week that she has filed for divorce
41.2)
Feature 34941 (Layer 20) Trained
Entropy: 0.434
0.769
Interpretation: Prepositions often used with nouns, typically Indicating location or relation, such as "In'', "on", "within", "outside", and "beyond".
Ex are larger than there is major diffreence is in the carbon price required to
Cubed model withiin same ballpark. Where
Ex Binary options auto traders pro signals review cheap salary countries and are. long as stays boyyswiithig the price points that were set, the trade ends
Ex whole base for all the old contract, which has been demonstrated clusively. This is good to ensure graduates obtain the maximum amount of propased work fallswithin the classroom
Ex </endo |e keep the speed limit, sir. $10 50 plus shipping for DNA75 board. Damn that cheap! Thank you ^^
Ex 5
and completeness. Nothing be added taken away from that Tendul kar flick that would not diminish the shot.. wiithin its own terms, it cannot be
Activation (0-52.2)
47

---

Feature 105563 (Layer 24) Trained
Entropy: 0.738
Fuzz ROC: 0.698
The token that appears to be often relevant is the suffix or part word which usually at the end of word, indicating some sort categorization, action, descriptive feature associated with the preceding word.
Interpretation:
Ex 1
money and mission teams all over the country. Some of the places we have served include: Washington Soup Chicago Projects, Dare to Care
Ex
crore having facilities for processing milk and Ikiproducts besides packaging vegetables and fruits. The current Winter session of Parliament is set for record rmanermance in transaction
Ex ori pccademmyy and Daycare, Little Kickers, Fourth Trafalgar Scouts, community yoga and volleyball, You can tune parameter vision a algorithm
Ex 4 Ltd,
that manages 'The Bridge' project, Oxley Emerald (Cambodia); (Cambodia); and Oxley $
ley Gem
grease duct, chilled water and heating water piping. The Plumbing system consisted domes tic hot water steam heat exchangers domestic water pump sk
5
10
20
25
30
Activation (0-37.5)
Feature 107293 (Layer 24) Trained Entropy: 0.448
Fuzz ROC: 0.795
Interpretation: Transitional phrases words often used to preface statement, provide warning, express sentiment, typically introducing or connecting ideas in text.
it Also, note that I am bui luing this Web API 2 service on top of the Microsoft Owin framework. could have built directly
Ex 2 recognize authentiations issued by itself. Next we assign the UserManager Factory to lambda expression that returns new, properly configured User Manager. [Note:: :
we
Ex 3
your own (depending what interests most). Also, note that it is mostly walking tour and you will be on your feet for the better
Ex 4
a very tedious pracess. For more information, contact the embassy in your home country. PS: Mote that this code can be used whenever you want, as
Ex 5
account (as it would have been ovenrritten). Noke that the MD signature will have to be calculated prior lola it be sent within the request
66.0)
48

---

Feature 120367 (Layer 28) Trained
Entropy: 0.423 Fuzz ROC: 0.713
Interpretation: Short sequences of characters that appear to be fragments of words, often function words, articles, or prefixes and suffixes.
Ex 1
brands on thene appropriate product presentations. Large brand partners have included Target, GE Lexus, Yahoo, Intel, Pronter Gamble, The
Ex 2
Now watching sold, view Details, part of Pronter Gamble invertisinnprrmmttioo Pronter Gamble., also known as
Ex 3
Cuts Advertising Allowances Leading To Diminished Brand Strength".... .ow P&G Is Forced To Cut Brands! Promter
Ex 4
However you want to look at it this is what happened. For generations Procter Gamble demanded brand dominance with all its products. Year
Ex 5
an American multirational consumer goods company headquartered downtown Cincinnati, Ohio, United States, founded by William Pronter and James Gamble,
11.9)
Feature 134070 (Layer 28) Trained Entropy:
Interpretation: Nouns words that represent general concepts, objects, categories, often related broad topics like technology, documents, or services, and sometimes appear in formal or technical contexts.
Ex
and measure properties of fffuctions with finite relaxed energy are studied. Concerning the total mean and Gauss curvature, the classical counterex ample by Schwarz- Peano
>acceptZinbbjeetss<bb determines whether ZipObjects are to be accepted. D>acceptffi@@bjeetts/// determines whether FileObjects
Ex 3
propartion of complaints resolved without dispute, with less than 6 percent of complaintirespoeee disputed. The CFPB should make the Consumer Complaint Database user-friendly
a single image inside such link that doesn' have. some kind Ex
of Fallback solution formtinks where title is present, Opera seems
Ex space for everything-sart spaces for, e. safe space for a disadvant grou cannot also be safe space for no holds bar
Activation
49

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L.2 CONTROL
Feature 10065 (Layer 0) Control Entropy: 0.556
Fuzz ROC: 0.503
Interpretation: A suffix or word, usually not the first word in the sentence or phrase, that is often an article, preposition, or suffix, sometimes a noun or verb, that has some importance for the behavior, often fixed expression or a grammatical function.
Ex
think #100 r.00dassot Looks pretty impressive. Not shab by especiallyconsidering I probably only had around 500 Instagram followers at this
Ex </endo ft two to play for the title, namely Daniel Hu and Andrew Simons Game 2017's best three title Match between Daniel
Ex 3
for visiting, and have to feed those compulsions! Ugh I feel you, also have book buying disorder.. I also have a signing
rriticum gulgare (Wheatgerm) oil, Simmond ia chinensis (Jojoba) seed oil, Cocos
Ex 5 with other people we trust those people aren t going to be filling their heads with garbage. personally, and I sure you can relate, am
Activation (0-5.7)
Feature 10222 (Layer 0) Control Entropy: 0.562 Fuzz ROC: 0.497
Interpretation: Various tokens including articles, conjunctions, prepositions, nouns, and adjectives, aften functioning as common words or phrases in sentences, with no specific part of speech or grammatical function standing out prominent pattern.
Ex </edofftet] }> the end of the war r and even made an excursion into Mary capture Union ficees... have anti- immigration hate speech many times
Ex 2 <|endoftext |> would do well to cansider the Roland FP- 30. Its combination on superior sound quality, quiet action, portability, Bluetooth page turn ing feature,
better conditions". Summarizing his views, Romano thinks that if we want understand the politician, the strategist, and the man Vladimir Putin, we
Ex 4
quiet bliss that assured everything was in its right place. As the star studded congregationgatthere in Pico De Loro for the big event,
Ex use all your options lakland City Indiana Spas and Salons Directory Oa land City has wide spanning number of theatre offerings within reach
Activation (0-5.0)
50

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Feature 10058 (Layer 4) Entropy: 0.566 Fuzz ROC: 0.464
Control
Interpretation: Various tokens including nouns, adjectives, adverbs, and pronouns that function as essential components sentences, aften signifying objects, actions, or relationships preceding or following punctuation marks.
entities, and sometimes
Ex 1
support preparing this plaque my supervisor! They worked together ensure that I recieved it in a timely manner! My supervisonwiit truly treasure this plaque
Ex 2
pig Petunia! David joined us in 2014 as anvolnneer. His past experience as school administrator and his energetic, friendly nature have helped many of
Ex 3
for preparatory phases bodybuilding. Boldenone provides hard and remely refined muscularity with desired vascularity well. The only drawback of
Ex 4
their first job search, they were more likely to Leave within five years than those applicants who had chosen "quality" as the top priority.
Ex 5
Town web site, be aware that electronic data can altered subsequent original distribution. Data also quickly become out of date. It recommended that careful attention
Activation (0-5.2)
Feature 10805 (Layer 4)
Control
Entropy: 0.609 Fuzz ROC: 0.565
Tokens often functioning prepositions, articles, conjunctions, or other functional words that serve to connect, modify, or provide context to the surrounding words or phrases.
Interpratation:
Ex 1
it if Saw 2nd preview this best play I've Complex characters deal with complex situations which are proxies for real world
years!
117b 01528/. 12. "Dassault Lève Le Voile Sur LenMissile Jericho [D assault lifts
Ex 3 an McGeeney was inspirational leader, player and manager. He was different than Micko in his approach, but Like Mick
Ex ftext] |> poets was as thrilling as the dialogue between Pearl London and the other nineteen poets. The otherppatts included Maxine Kumin, Robert Hass, Mur
Ex
0 ml min at total pressure of 8.6 bar and temperature of 425 K. More publica tons about STM on metal surfaces in the
Activation (0-5.3)
51

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Feature 100102 (Layer 8) Control
Entropy: 0.580
Fuzz ROC: 0.532
Prepositions and articles, occasionally conjunctions, that play functional role in binding phrases or sentences together.
Interpretation:
Ex 1
Posted on January 19, 2012 January 16, 2012 Categories backyard birds Tags bluebirds@ Comments on Bluebirds hanging out with Frosty !
Ex 2
enterinn the details from the statement for your policy your tax return and using tax claim code F. This section applies if you are covered as dependent
Ex 3
and can support photo and extended description. Categories are aitomtiicallycreatee as a result of these selections, and churches are assigned to соmmon categories basedmon location
Ex 4 review of the policy. Caught the middle are school districts Like Portland Public Schools. The state board approved four broad exemptions state instruction time requirements
Ex 5
the capability and know how to screen thousands resumes qualify hundreds of candidates to find the right fit for your position. Through our detailed Led screening process,
Feature 100351 (Layer 8) Control Entropy:
Interpretation: Prepositions, common function words, serve as linkers, often idiomatic expressions, sentences or phrases that provide additional information.
other parts speech provide context
Ex 1 forbidden to kill vicunas, they captured them al Live in massive hun ts and then sheared them. Vicunas travel in several different types of
Ex 2
garment, leather, toy, computer embroidery. handcraft, advertisement, decoration, building upholster, package materials, digitalprinting. paper products
Ex 3 take responsibility for el eliminating violence in the wo korkp lace. Because of the widespread of stress and violence in workplace, organizational leaders hold legal and social rosions ibilities to reduce emp employee stress
Ex 4
health (an es estimated 305 members) invited to participate. Response frequencies analysed in SPSS. Openmended comments subjected to thematic analysis. Results: Eight
Ex 5
flexibility, it enhances your ability to tackle problems from multiple perspectives, and pushes one s critical thinking skills on daily basis. Growing an international
0.0
0.5
1.0
1.5
2.5 Activation (0-4.5)
2.0
3.0
3.5
4.0
52

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Feature 100186 (Layer 12) Control Entropy: 0.519 Fuzz ROC: 0,483
Various types of punctuation and function wards, often indicating the end of sentence or pause the text, and sometimes preceding or following a quotation mark.
Interpretation:
Ex 1
suggest you keep pat just for making candles (not for cooking). Four home designers share their sources of inspiration. Hint Ghance outside!
Ex 2 its Offices and Manufacturing Facilities in Birmingham since its inception over years ago. It very proud of brummie heritage and now
Ex uspecting visitor to your site, making email look Like it came from When the person clicks on the link, the script will navigate to your
Ex 4 Forge Wivenhoe Counter Stool, 26 in Gunmetal (Set of by Best Choice Products is Get rid of your old bed and invest
</endoftext |> protect her and her son, i doubt if she is common or not, Chanakya says she saved Samrat life she is common
Activation (0-5.1)
Feature 100493 (Layer 12) Control
Entropy:
0.525
Interpretation: Articles, prepositions, common words, and punctuation marks, often denoting transition connection between phrases sentences.
Ex 1
do live in Austin, Texas after all and we all know winters here are a del ights, but continue to sport chunky w neutral knits
Ex 2 direction mistakes that the nation may suffer. We fully believe that bring about economic improvements for the people and for nation, the notion that "governments
Ex 3 </endoftext(> let of old assumptians. worn out predjudices and lingering fears? Why it always "not me, not now?" Why we insist that
Ex 4 more industrial setting. Niceminternal appearance decoration. Good overall feeling. Room and bathroom good Breakfast fine. Good f free wifi internet access throughout hotel Very close to
Ex 5 just quotation but bes poke comp sempreeeeiieerpppooo outlining how the operation will run, and trating the specialist equipment we will use. Everything detailed, from
Activation (0-8.0)
53

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Feature 10092 (Layer 16) Control Entropy: 0.583
Fuzz ROC: 0.472
Interpretation: Tokens that often function words punctuation, or the beginnings of new clauses sentences, and sometimes brand names or proper nouns.
Ex 1
manipulated and trae events including, detonating a nuclear device prompting the volcano beneath Yellowstone National Park to erupt killing 150 million people before similar set of eru
Ex 2
then! Wednesday, October 24 The Mastermind Intensive Carlton Landing, OK The first event was last month, and
Ex 3
between hours: It the biggest cha Challenge Mariah Carey. She needs to eat compote of apples or raw vegetables les something light. The gym sessions
Ex 4
great. Love your photos, I think you have the cleverest squirrels around you. Beautiful birds. Your work ALWAYS so amazing and
Ex 5
that off peak and per cent is during the peak. These trains are very rvercrowded but kknow of no plans yet agreed to increase their
Feature 101533 (Layer 16) Control Entropy: 0.549 Fuzz ROC: 0.485
Interpretation: Various tokens that appear be nouns common function words a variely sentence structures, often punctuation marks.
Ex 1 < endof text >> Honda Motor Co. First announced on July 1972 as coupe 2 doors following by hatchback doonss at September on the same year. Honda
victory of 23 years. Five PGA Championships and four U. S. Open titles are also included in the list. He has set many records and earned
Ex 3
groom ente red until the very end of evening, the atmosper and smiling faces were all wonderful. Your music achieved everything we hoped and planned for. You looked great
Ex 4
phonew 14. Please note the festival cannot waive entry fees for anyone. This he helps cover the cost that the festival must bear, and we know you can
ope its new editorial brand focused on health. Although housed on HuffPo, The Scope niche pursuit that decidedly fferent from HuffPo
Activation (0-5.3)
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Feature 112177 Entropy: 0.449 Fuzz ROC: 0.393
(Layer 20) Control
Interpretation: A variety parts of speech, including prepositions, articles, adjectives, nouns, and verb forms often function words transition words, activated different contexts.
Ex Mendoftext pi The surveys was risrespected Representing the are twentieth BEAD 2010 which is the paper disorders as per UBC 1997). The ideas Like
Ex <lendoftext of struggle against Nazis made perfect political sense. Indeed, with became something like second founding myth of the Soviet Union the Great Fatherland War
<|endoftext[> model firmm Mechano when I was 12 53 years ago The be smallish to
limiting factor here the rotor size which
Ex 4 </endoftext Tae Course Of Any Day A Major Part Of Child Advocacy Is As king The Tough Questions.. As Many Times And In As Many Ways
Ex <lendof text | The oversized balcony offers an amazing view ocean. Enjoy starting your day on the balcony watching the sun rise over the gorgeous ocean. Un
Activatian (0-5.3)
Feature 132678 (Layer 20) Control Entropy: 0.523 Fuzz ROC: 0.543
Interpretation: diverse set of takens, including nouns, adjectives, adverbs, prepositions, and other parts of speech, often forming phrases appearing specific contexts.
Ex 1
United States).. Sa Saqui Sannes, Pierre de and Apvrille, Ludovic. Making Modeling Ass umptions an Explicit Part of
with Templeton. These images are as raw and unforgiving as they are luminous and moving. Largely comprised of images of people "living their lives
Ex3 3 cervical biopsy, hysterectomy, and others. What will the market growth rate, Overview and Analysis by Type of Global Gynecology Surgical Instruments Market 20
Ex Moses, this varietal of cannabis is for peoplemwha don t want to be under the influence, and it is available in oral doses in Israel.
Ex 5
armed defense and the flirtation with violence, beyond dividing the mo movement, went nowhere. Left holding the bag most tragically were those
Activation (0-5.8)
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Feature 100351 (Layer 24) Control Entropy: 0.606 Fuzz ROC: 0.426
Function words and nouns that part of common prepositional phrases or clauses, often marking relationships between objects, locations, or actions.
Interpretation:
Ex 1
her ta ed samples! No simple white. No creamy ec ru Her tatting shouted with joy and cavorted cornflawer blue, hyac
Ex 2
Worlds, which released wowld Wednesday, also highlights modern society's inability to learn from its 29 century mistakes Lucas was fascinated by
Ex 3
Thursday morning! After NewnStudent Convocation concludes, Welcome Week events are only for students. If you plan to stay at a local hotel during Move
Ex 4
can be completed in - days They have the potential to become huge nuis ances anywhere that food is processed or stored (homes, restaurants,
Ex 5
search warrant before pulling data from vehicle's "black box, reinforcing that today constantly evolving computerized cars shouldd have the same privacy protection as smartphones.
Feature 10064 (Layer 24) Control
Entropy:
Interpretation: Common function words in the context of descriptive or instructional text, and sometimes preceding or following quotation or a specific topic.
representing various objects, concepts, actions, often
Ex 1
it huge help to have this integration instead of having to trawl for ratings/reviews an unrelated social media site ("Hey,
Ex 2
tho c 'eet un comble. HAHA love that logic. Tell your vegetarian friends its ok to eat thisi MEAT,
Ex 3
Install 5 Gigs of Music!!! I just got the interface module from Blitz safe Model# BMW/ ALP V. w/ Aux
Ex 4
of tomorrow and for our shared digital future. As the our technology expands rapidly, too much educational efforts and outreach. In this light. 3
Ex 5
the age window, college age South Koreans must choose whether they will suspend their undergradual work or their past past-an graduation academic and professions careers to serve
Activation (0-5.2)
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Feature 10064 (Layer 28) Control
Entropy: 0.458
Fuzz ROC: 0.506
Interpretation: Tokens of various parts of speech, often function words, punctuation, short words that provide grammatical structure or semantic nuance.
1
< //nnofeeet |> tale worthy of a Shakespearean tragedy. In 2008, his story told stage play co- -oroouddedd by Bunuba Films and the Black
Ex 2
<|endoffeet in Human Resources Management: Assessment of Human Resource Functions. ustairability:
Press. Werbach, A., 2009. Strategy for
Ex 3 <|endoftext |> The whole trip is going to be rej rejuvenating, and I'm super happy that Bassnectar is playing on the last night.
Ex 4 </endoftext |> and leading consul to the professions to learn and to exchange share knowledge on how to build the business of professional services firm in the
and how
Ex 5 </endoftext ow the the Lounge gives you head- toe therapy and 18 total pulsating jets comp lete your adventure in relaxation. Length
Feature 107353 (Layer 28) Control
Entropy:
Interpretation: Punctuation marks, function words, and determiners provide grammatical structure clarify meaning sentences.
1 </endoftext aal for "An Energy Absorbing Rearview Mirror Assembly" and
the U.S. S. Pat. No 5, 327 288
Ex 2 </endoftext | it comes limiting secondhand smoke. "San Francisco's way out of date," " she said. "That's why it 's critical we get this
Ex prndofotex//vviollaet her probation. She d go to jail strung out, sober up for a week or so then go back to using.
Ex 4 </endoftext Iaaaas assembled magnificent crew of clinicians concentrate numerous features res of forensic psychiatry and esychotherapy to offer their stories and theories in this bold topic. The
Ex </endoftext IRRS and Ke ogoghs. The more you save now the bigger your nest egg will be. Take retirement job Working during retirement might feel
Activation (0-5.8)
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L.3 RANDOMIZED EXCLUDING EMBEDDINGS
Feature 10063 (Layer 0) RRandmiizdd excl emb Entropy:
Common words and phrases used in everyday English that often represent a person's state, feelings, actions ar passessions, such as "enjoy", "self", "remain", "precise", "prices", "talent", and "despair", often used descriptive and conversational contexts.
Interpretation:
1
<| endoftext] Leweary barman responded. Alttgether bad,' the host concluded. you will, but there's something noce nice hidden men avoid wine
Ex 2
ushers guest into the interior places. calm themweary spirit or send a subtle message. Indirect lighting, especially
create the sparkle,
Ex 3
from a sound sleep to take care of the carriage horses. I slipped into the house quietly as could, instructing housekeeper to not disturb
Ex 4
You must conscious that cellar can actually weaky and horrible. For that reason, choose vivid timeless colors for the blind window in the basement
Ex 5
in Test. Jadeja was finally undone mus ter a cel celebration as he knocked the stumps over and Koh
Lyon who could barely
Activation (0-4.6)
Feature 11080 (Layer 0) Randomized excl emb
Entropy: 0.107 Fuzz ROC: 0.932
Interpretation: Nouns referring to information, substance, or materials contained within something, often digital textual context.
Ex <Jendoftext| >> Wouldn 't the following INCA -descriptor complement the CEFR descriptors inccontents and form, you think? Vul pe, Thomas
Ex </endoftext|>> the iontents of the tank. In addition, prolonged contact with taanucontentss, for example in the case of viscous or caustic liquids, can degrade
appears with a certain probability and level up quickly. i Various items collectioniconent 1. The Dark Stone Altar Collect the magic powers of the weapon growth
Ex 4 < </endoftext|>> those non nondfind con. tw websites and webpages, and is not responsible for thei recontent or their use. By linking to a non
Ex
copy thntenttens. Right click on "Soldiers the Universe" on your desktop and click "Open file location". Lastly right click and
Activation (0-7.9)
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Feature 10064 (Layer 4) Randomized excl emb
Entropy: 0.347 Fuzz ROC: 0.620
Interpretation: A sequence of characters that often prefix suffix, typically 2-5 characters long, and is aften part of a word, especially one that is commonly abbreviated or truncated.
Ex 1
behavior analysis principles. Oscan PhD dissertation focuses on youth with Fetal Alcohol Spectrum Disorder transitioning from children to adult services.
O Osean supports
Ex 2
to burn scarred and nearby areas. New Mexico First organized and facilitated the conference: see http:// amfirst...... org/events//frr and
agreement was signed on in January by IUF general secretary Sue Longley Meliá Gabriel Escanrer, as part the process initiated with the
Ex 4
The romantics think the riots were a positive force should visit the riot riot-scamred neighborhoods in North Philly and tell me what they find there.
Ex 5
actor has come forward to say he wants to star in it. Oscan Isaac, who is known for portraying the pilot Poe Dameron in the
10
15
Activation (D-23.0)
Feature 10201 (Layer 4) Randomized excl emb Entropy: 0.071 Fuzz ROC: 0.948
Interpretation: Tokens representing either the concept of imported goods or a transitional word "u" often used colloquial contexts, particularly informal writing or web-based communication,
Ex 1
which has consistently importted more from Quanti fying the impact of the UK leaving the EU on Northern Ireland trade is problemate
than
exported to
Ex 2
region has eexpp more to the EU over the past ten years than it has Lipprrted from it (Figure 3). This contrasts to the UK as whole,
Ex 3
found embedded in an imported document. If it can, display is handled by operating system. This the ideal situation. Unfortunately, there two reasons
Ex 4
al lowing access from several computers and collaboaation with other users. PDF files authors, title, and journal
iiporteed into Mende ley desktop and metadata such
Ex 5
Import LDAP using the ALM Octane Settings LDAP imparted to the space or to the workspace. These instructions describe haw
0.0
2.5
5.0
7.5
10.0
12.5
15.0
17.5
20.0
Activation 20.0)
59

---

Feature 10064 (Layer 8) Randomized excl emb
Entropy: 0.120 Fuzz ROC: 0.833
Interpretation:
The term 'spectrum' and its variations often represent a range or scope of something, light, sound, electromagnetic frequencies,
frequently used scientific or technical contexts, sometimes used more broadly to describe a range or scope of something, including a wide range of possibilities broad category of things.
Ex 1
ents a healthy slice of everything good that is happening in traditional now, across sparkling spectrrun sound. With Louder Than War saying of last year
Ex 2
random, noise- like signal. The reason being that the transmit tted signal frequency response must have flat noise- like spectrun in order to use the allotted
Ex 3
mercury Lamp spectrun with the versatility lity of broadband lamp, the Sc ope Lite 200 is ideal for multitude of applications ranging from FLuorescencemicroseee to cosmetic dent
Ex 4 generation broaduspectrun antibiotics to tackle the global problem of rontinirrobialleiitttae has raised $9 million (approx. 08062 Cr.) from Japanese venture capital firm University
Ex 5
radio, as simple as a crystal set or as complex as a aspectrun ana Lyser Don 't be shy? We would all really like to see what projects
46.0)
Feature 11002 (Layer 8) Randomized excl emb
Entropy:
Interpretation: Nouns representing various concepts, often denoting objects, activities, fields of expertise, and sometimes indicating sense of technology ar development.
Ex <|endoftext |>henburg about Integra system that Sanician built on top of Otto for the city. Gothenburg itself is city in which more
Ex 2
"Spurred by the growing body of scientific research showing the broad harm reantmotinoide pose to bees and other pollinators, earlier this year
canon in McCovey Cove, with all of the other loyal Giants fans who can t make it "in" to the game.
Ex human made pesticides, neomisotinoids. or neonics for short. And feel good about what I doing here at Cathedral Drive Farm
Ex kayak, canoe, , or Bel lyak. Lace up those running shoes, trail shoes, climbing shoes, hiking shoes. Load the car
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7.5
10.0
125
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Activation (0-22.4)
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Feature 10064 (Layer 12)
Randomized excl emb
Entropy: 0.030 Fuzz ROC: 0.917
Interpretation: References to physical body parts objects held in the hand, often implying direct human action or involvement.
Ex 1
.450 in the medium term. On the other song decline below Rs 2, 200 will strengthen the stock short- term downt
Ex 2

 TL remains low level. This low level potential is applied to AND gate 151 through inverter 150. On the o other handd as result of
Ex 3
ezers,
steady scott haand et specifications is web based software that supports improves efficiency in the auditing accreditation systems scott
Ex 4
28 30 100 100 100 Each individual designed glue- filled sy
ringes, cast fabricated part shand scott assembles using twe
Ex 5
the other hand the price of the base version of the JAWA Forty Two starts at 600 1. Lakh As for the
36.2)
Feature 10092 (Layer 12)
Randomized excl emb
Entropy: 0.025 Fuzz ROC: 0.926
Interpretation: The token appears part various words, often found suffix position, across multiple examples from different contexts.
Ex 1
you' re like us of course and completely forget that it was half-terie Un d Taunted, we headed off to visit Tangmere Mill Military Aviation Museum.
from wearable devices such as the much vaunted Google Glasses Lord Mayor Councillor Garath Keating, Chief Executive Roger wilson entrepreneur
Ex 3 Days on end John would spend kicking that ball up against side of some old ladies house until he was headh unted by scouts and accepted trial
Ex 4
unny mystery books for kids. Adults will love them too! Spy Pets 2: Ha unted Drive- In is hilarious adventure for children ages 6-
Ex 5 all miss him terribly as we uphold his memory and sacrifice and continue un daunted by the task hand. Our thoughts and prayers are now with his family,
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Activation (0-68.5)
61

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Feature 10058 (Layer 16)
Randomized excl emb
Entropy: 0.001 Fuzz ROC: 0.969
Interpretation: The token "record" often refers to document or collection of data, achievement, or keeping track information, setting breaking standards,
accomplishment, frequently used the or preserving history.
Ex 1
level. A substantialprrpor tion of publications should be lead/ cor corresponding author. Sus tastained record of playing leading role in successful bids competitive,
sustained and substantia lureond of publications high high-quality, peer reviewed journals,
for submission to the UK Research Excellence Framework rk at the highesttinerrmationnl quality
international
Ex 3 each processing hundreds of thousands of barrels of crude day, and India' Reliance Industries running one refinery at a recocnd 1. million bpd
ain a conciseurecond inside the civilization you desire to critique along with the puts you will need to re Investigation. A whole lot of trainees think it is hard to
Ex 5
Co Dirrccor of Rutgers Queer Newark Oral History Project, to learn how his o organization' s digital initiative, "Free to All," is helpingimecord the
20
Activation (0.64.5)
Feature 10064 (Layer 16)
Randomized excl emb
Entropy: 0.151 Fuzz ROC: 0.665
Interpretation: Various nouns different types, including objects, concepts, and words that convey specific meaning terminalogy, often related medicine, commerce, everyday life.
Ex 1
Teales wishes, is open to the public from dawn until dusk year round. Edwin Way Teale at work in his bliind along Hampton Broak in
Ex 2
eng et al. Ho et al. et al. Yang et al. Zhang et al. fai led to tombiind study particiaassss and personnel
Ex 3
student comments have been enabled. Because of the nature of bliind marking the s tudents cannot see the final grade until all of the students' names have been revealed
bblind. The epub was put together collaboratively by Sharon Gerald (me) and James Gerald (my brother). love good PDF. This
Ex 5 totally These alarming numbers may be attributed lack of facilities in rural areas, inability to af ford quality treatment, and lack awareness blindness visual
Activation (0-98.5)
62

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Feature 10379 (Layer 20)
Randomized excl emb
Entropy: 0.092 Fuzz ROC: 0.910
Interpretation: The tokens "rise" and "lock" appear frequently, often as parts words or phrases, with "rise" often indicating an upward movement, "lock" often referring confinement security mechanisms.
Ex 1
href=""ttps:// rule in BTC</a>
org/ node 11949 PCRR thwart BPF
v with faster loading, FPS frame trimes. For loading time numbers and FPS other games, you check Overlord video
Ex 3
was originally published on href="https: //www amtimes org/">Assam Times</ az. Michhel Jackson' s *Great Beer Guide
Ex primitiveillustratiins and arresting borders add inneasurably to the sense of place. This is winner for storytimes.. Written Lynn Moroney
Ex 5
also see 5 cent increase in FPS once the anti am tech was removed. Frame timess show a 16 ms minimum and
Activation (0-40.2)
Feature 10636 (Layer 20) Randomized excl emb Entropy: 0.102 Fuzz ROC: 0.916
Interpretation: Words phrases related sight or vision, including word "eye", often used literally or figuratively, and also words related to "pres" which
associated with titles positions authority.
Ex 1
't have. Warcraft have 20% miss chance. Fhance.Fami ly Tree Friday: Preserving the integrity of original records..includdan the mistakes! As primary
Ex
79 www. pcmc co nz Botox is a riptionn Medicine containing 100 units of clostridium botulinum Type A toxin complex for
Ex 3
Presumably they will have specific targets this year rather than sit back and let the market come to them like last year, no? ANSW: Obviously ly
Ex 4
available from The Preserve, this unparalleled property includes 10 acre building envelope, full-time equestrian rights, the ability to build a
Ex 5
citizens must associated with the adoption and scrutiny public policies with exceptions, stressed Alex Bodry With regard to the Five Presidents
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60
63

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Feature 10201 (Layer 24) Randomized excl emb
Entropy: 0.107 Fuzz ROC: 0.926
Interpretation:
The tokens "looks" or "looks like" often appear in sentences describing appearance or how path route, sometimes related hiking
something seems, or outdoor activities.
Ex 1
she Books her natural af hair while announcing exciting news. We are happy for her that she decided to chop it all off,
Ex 2
to engage the vast majority of teachers (most haven 't even heard of it) and now nowks to be resorting to crony ism. This will not
a form despot that thatsl Like freedom the electorate, but in fully designed structure keep people from being
reality could too concerned about loaking
Ex 4
Shed category andktooks convincingly like old boozer rather than tiny hideout. Garry spent small fortune on equipping his miniature
one So yes, Creating Infectious Action is a course about leadership, where leading Looks lot like cultivating garden. Man, great
Activation (0-85.5)
Feature 10805 (Layer 24) Randomized excl emb Entropy: 0.001
Interpretation:
"ir" typically appearing the middle towards the end often foreign origin proper nouns, sometimes indicating connection to place or geography, or part of a surname..
MA IrMa Graceful Inheritance" the debut full- length studio album US power/ heavy metal act Hemm Apparent.
Ex 2
metaphor for attempting something that is really Cevera ago was kindly given a clump Iris Sibámica in a bucket,
Several years
Ex </endoftext |> Tank West Tank West Tank Whimley Basin Tank Number One Wham ley Basin Tank Number Two Whiskey Reservoir
Ex 4
es Tank
Fantasia Tank Fat Tank Fence Pit Tank
Fence Tank
Fence
Tank Fenceline Tank Fan Pit Tank
Ex 5
He im Apparent formed 1983 released 1984 before being signed by French independent label Black Dragon Records for the release of Grace ful In
20
30
50
Activation (0-59.5)
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Feature 10201 (Layer 28)
Randomized excl emb
Entropy: -0.000 Fuzz ROC: 1.000
Interpretation: The ampersand symbol (&) used as conjunction to connect words, names, or phrases, often titles, names companies, or lists items.
Ex
<|endoftext pot grounds buinging &c still have the [gap] arrange the grounds for a coal trade & transshipment at the Inlet) since
other VMware VCP6 5 DCV certification exams in the first attempt. Why Buy VMwa --20 Ex
Ex 3
& Martin Sefton & Ping Zhang, 2005. " "En 'Engggeennnt and the Balance of Power: an Experimental Study," Discussion Papers 2005 08
Ex 1 Transportation Drivers Movers Delivery Driver (part or full time) with Door Make up to $18. 0/hour Delivery Driver
Ex 5
studio one day!! Oh my... tango mirror, chest ottoman, black white, elegant with wh whimsy. Oh yes
Activation (0-37.0)
Feature 10636 (Layer 28)
Randomized excl emb
Entropy: 0. 0.122 Fuzz ROC: 0.897
Interpretation: Titles or nouns referring to medical professionals or maternal figures, typically in formal or professional contexts.
Ex 1
1965. Colman and Bonham Carter also bear striking resemb lance their real life counterparts, pictured here with Queen Elizabeth, the Queen Mother, in
Ex 2 There's a clue in the title with this one Sauce Tomat, the fourth of the FrenchiMather sauces, is made from you guessed
man had just been released from custody and was headed on a New Orleans- bound bus surprise his for Mother? Day. He appears to still be
Ex 4
Signs expertise, can help illuminate those ideas screaming color Mothers Day on May 12th (Sun.) To
Ex 5 the weekend for will possibly be not having to cook on Mothhr'ss 's Day individuals. Architects cannot be apolitical we have a duty to
20
80
100
Activation (0-105.0)
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L.4 RANDOMIZED INCLUDING EMBEDDINGS
Feature 10063 (Layer 0) Randomized incl emb Entropy: 0.171
Interpretation: Nouns, organizations and product names, often used in formal language.
proper nouns
part names, companies, locations,
Ex 1 <I /eedoftext > visit our shop in Kansas City 64110. Fue injection is system for admitting fuel into an engine. Since 1990, fuel injectors have
</eedoftext > 2008, "A Novel Gas Turbine Cycle With Hydrogen Fueled Chemical Looping Combustion, Int. Hydro
<|endoftext |>, Ron Suet said. Lewis is cousin the Suebz Ex 3
family and always looked to Esther. She developed into
Ex 4 <|endoftext >> scale- row wwwssiiiiattionssyeeeeo systeitttiinssstsemmmmm of the Karner Blue (Ly (Lycaeides melissa samuetis), ) the butterfly he is perhaps most famous for studying,
</endofttet guess ing that it might take a time of 6 months at least. Prem ium Version: Due disc brake Electronic Fuel Injected (EFI)
Activation (0
Feature 10252 (Layer 0) Randomized incl emb Entropy: 0.120 Fuzz ROC: 0.840
Interpretation: Tokens that are often nouns, with many being related to service pravision, viewing, or containers, often denoting objects or concepts that provide or hold something.
Ex 1 <//endoftext another. The provide
service called a server; the recipient of a service is called the client. An entity is server or
Ex 2 </endoftext ectopic pregnancies are detected around to 8 weeks of pregnancy, The key to early diagnosis involves communication betweneeewern between you and your healthcares thaathcareapoviccn reprovide about any symptams you may
3 </endoftext Your health plan may require you or your medical prrvier to get prior authorization or pre- creeccriifiiation before you receive some services. Services that often require
Ex 4
a digit code unique winamp skin maker torrent bugs Draw provider, you il have to race against each other for up to
Ex 5 <|endoftext| hear from them via email than week after purchase. Themprovider informed us that they were not able to do the service. (I lieee that
Activation (0-4.7)
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Feature 10064 (Layer 4) Randomized incl emb
Entropy: 0.041
Fuzz ROC: 0.863
Interpretation:
The token "ack" is often part of ward, usually in the middle or at the end, mostly in common
Ex [enddffeet| When was young man of 19 in the USAF, I would report to duty every night right smack dab in the middle of
Ex of bad
then held on steady climb, ola BRUNS, Investment Trusts Marketing Manager, PM
thrilled."
Ex
in Anything Goes Hysterium in A Funny Thing Happened on the way to the Forum, Nicely Nimely in Guys Doll
Ex 4
Cackling Stump', Each has its magical character and will bring delight laughter and the thrill of mortal peril. Translated from the original run
Garden Iad of Kauai, fireworks and firecraceers will be booming off tve midnight. Pilgrims beginning to arrive here
10
15
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25
Activation (0-25.8)
Feature 10092 (Layer 4) Randomized incl emb Entropy: 0.072 Fuzz ROC: 0.978
Interpretation:
Technical terms related to audio, media, and technology, often including words "audio" and 'laid" used in contexts of electronics, software, and devices.
Ex
clothing range from taid back sweatshirts hoodies, versatile shirts, joggers and of course True Religion jeans. The iconic horseshoe logo for
Ex 2
the Echo device. Speakers connected the Echo Dot via the 3. 5 mm audio jack will work with multi room Amazon has made the
Ex 3
the feature today, it will likely come to other connected speakers soon. Multi- room audio should already available on Amazon Echo, Echo Dot, and
Ex 4
new multi- room audio feature available for speakermanufacturen integrate into their Alexa enable led products. While Amazon' s three Echo products are the only ones to support
Ex 5
apart from their ginormous exterior size, is that they all to have aftermarketiauddi systems controlled by near microscopic buttons. It takes
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20
25
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Feature 10064 (Layer 8) Randomized incl emb
Entropy: 0.060 Fuzz ROC: 0.766
Interpretation: Tokens that represent quantities, measurements, percentages, often used to specify amount or proportion.
Ex 1
(NO ) 2 precursor is used as opposed to the Mn (Ac) precursor. Our DR EEn and XRD results show that the Mn (NO
Ex 2
session FIFTY! 1410 hrs IST: Tea in Dharamsala Another good session for India asthey lose only one wicket Puj
Ex 3
found in the GIFT Certificate link below. The categories of ENZYME PEEL CHEMICAL PEELS, MI CR ERM
Ex 4
ABRASION, LIGHT THERAPY, COLLAGEN INDUCTION THERAPY and LIFT FIRM are all advanced
Ex 5
5984 for NEFRY and And brokers and at your chances be used Binary options formula chart software. Options trading and commodi binary practice account south
32.82
Feature 10379 (Layer 8) Randomized incl emb Entropy: 0.115
ROC: 0.979
Interpretation:
Text features 4-digit numbers, typically representing the 2009, often used to specify time period, date, or year of an event, publication, or product, as well as nouns like "governor" and "achieved"
with varying importance levels, and the suffix "-ceiver" sometimes appearing technological context.
Worst Case Pattern f= 65MHz f= 85MHz 58 75 mA 70 87 mA IRCCS Re ceiver Power Down Supply Current PD
Ex 2 Conversion Number of IF Circuits in the Receiver. Coordinated universal time An international time and date system derived from the 0 degree mer idian
this point they repeat back to zero again and repeat the cycle. effect cyclical assignment. The Receiver Channel Port Card arbitrates and alloc
Ex 4
transaction unit, wherein said at least ane of personal number and a credit card number is transmitted in a secure signal, and said transceiver further adapted
connectivity (cell phone, WiFi, Internet) and storage (hard drive, flash memory). The field of comnunications spans signals processing and error rontrol coding for transgeiver
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Feature 10058 (Layer 12)
Randomized incl emb
Entropy: 0.112 Fuzz ROC: 0.902
Interpretation: Words or ward parts representing body parts (lip), chemical or biological terms (nucle, lex), or suffix (uter, osto) indicating relationship function.
Ex
NPA), those facing chargers are mostly activists cha challenging Duter te authority. In late February, the Duterte te regime released a list of almost
Ex
is, but fiscal record and rhetoric 't line up. happened to the money after the New Jersey governor killed new commuter rail
Ex Mars to digging tunnels under Los Angeles to lay the infrastructure for -pgh-speed public eraasportationsystee that would once and for all solve the comnuter nightmare
Ex 4 stable handling of mountain bike, the Raleigh sly is for you. This women bike makes a great city bike or commuter bike.
el Gatchalian is the cont roversial priest who was caught on video praying for Pres.. Duten illness during his homily. The al alleged gift
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Activation (0-50.5)
Feature 10201 (Layer 12) Randomized incl emb Entropy: 0.152 Fuzz ROC: 0.915
Interpretation: A set of nouns including core, literature, meals, and fog, which seem to represent central or fundamental aspects, academic written works, food, and atmospheric conditions respectively, often appearing in contexts where they are being focused on or emphasized.
Ex 1
and literature is also passed down through generations, thus shaping the cul ture of that community and taking years to form. country's history has a major role in
Ex 2
and Islamic Forces in iniaatestin; General Union of Palestinian Workers; Palestinian General Federation of Trade Unions; ; Pales tinian Non Government al Organizations' Network (PNGO
college campuses over Israel's treatment of Palestinians and the United States complicity with it. campus groups such Students for Justice in Patestine Jewish Voice
Ex 4
and review of all scientificlhieeeatii available well my I have prepared extens guidelines for you to follow during the 7 Stage Fat On The Move
Ex 5
choice to read at, below, and above their level based what they are interested in. while think it easiest to apply this to touiiterauure,
10
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25
Activation (0-30.8)
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Feature 10058 (Layer 16) Randomized incl emb
Entropy: 0.042 Fuzz ROC: 0.894
Interpretation: Significant words mainly include "point", often used relation to a specífic moment, location, idea, and sometimes "pack", "haul", "activation", "sketch", which have specific meanings various contexts, including physical systems, units measurement, or visual representations.
Ex 1
ppint, RHRP conducts PDHRAs for Army Carps of Engineers and Army Install ations Command civilian loployees who have returned from deployment. How do
Ex star point least. It amounts a simple Wrath Khan fight you happen to play it After get grasp of working with
Ex
oj is at this I' d'd insert a very, very frowny, frowny, frowny face But our modern era, saturated
Ex 4
was not really practical restrict usage way, Hence the pricing on our universal inte ffaces (interfaces that worked with all the cars avai Wailable that ppintt
Ex olves did one better, shutting down their visiting opponent. Best pick: I had little more faith Atlantic's ability score picking the
Activation (0-37.2)
Feature 10201 (Layer 16) Randomized incl emb Entropy: 0.047
ROC: 0.946
Interpretation: Polite expressions gratitude, commonly used in informal written communication.
1
OS. Une fois que vous avez installé. Parcourez et tél échargez des apps la catégor Jeux
Ex 2
Situer le design thinking parmi les rut approches iinovvttion (lean startup, agilité). Découvrir la démar
Ex 3
open air dance floor. has Marilyn Monroe tattooed thick thigh. couple dance together voluptuously. eleg
Ex 4
policy to www.rrormnaaluutainnnbbi has a "couple of subtit les to the course. One is appreciation, and one
He further states that the class
WeatMiss MAL wttttssttttttiitttittttttta ang Adam Banks
Activation 54.0)
70

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Feature 10058 (Layer 20) Randomized incl emb Entropy: 0.097 Fuzz ROC: 0.896
Interrogative words, mainly "where", often initiating question, and sometimes words indicating significance, modification or intensity, such as "severe", "adjust", or "candle".
Interpretation:
Ex 1
82 off expired. # will give you Funny landle Scents Persona Lised Scents, This special educational pricing on certain products,
Ex 2
time clock. Candice, we passed your feedback to the Neo support team: great to know that appreciate their approach. Love your suggestions for
Ex 3
Europe. Home Baby Clothes Fall/Winter Baby Clothes > Boys Baby CL othes > Funtasia Too boys clothesm red and white checked
Ex 4
Tank And Flushes. Yankee Candle Orders Are In! You must pick up your candles this time, as there is no place store
Ex 5 being kinds crazy. On Friday we had pizza at our house. Candice, Aleksey, Katiya, Cody and Kathy came over
Feature 10064 (Layer 20) Randomized incl emb
Entropy: 0.070 Fuzz ROC: 0.841
Interpretation:
Tokens are part technical or specialized terminology, often referring to electronic components systems, sometimes representing sequence of instructions process.
Ex 1
positions inccircut with thermal-conducte contacted with convex platform. This thermal dissipation mode acquires cooperative work of structure and PCB board
insulating mat used to be
the circuit 103 is latched by latch LT1 for a determined time period, for example, one field period. The output from latchacircuit LT1
Ex 3 Reducing the thrust fluctuation is key and difficult point magnetic circuit design. Thrust fluctuations occur due to: primary current and back - EMF there
Ex 4
cirucir 123 shown in FIG. 12. Referring first to FIGS. 1A and 1B showing an embodiment of the present invention, Ca denotes the
Ex 5
is to assemble components ontorcircuit board through thermal conductive tape with the other end connected ted with heat sink. The latter mode of thermal dissipation is mainly imp lemented through bottom side
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40
Activation (0-44.0)
71

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Feature 10092 (Layer 24)
Randomized incl emb Entropy: -0.000 Fuzz ROC: 0.960
The word "vegetables" appears consistently across various examples, signifying its importance as common noun in text, often associated with food, health, and nutrition.
Interpretation:
Ex 1
thumbs up from me! So glad you enj enjoyed it Georgia! You can use any still speed when they
vegetabites you Like. Speed vegetables
Ex 2
get more fruits, wegetables, and whole grains. Growing your herbs and produce will cut down your grocery bills and the amount of pesticides
Ex 3 vegetables, masters of their craft will prepare real masterpiece. Therefore, about the "Jo- Joo reviews you can hear mostly pleasant. As elsewhere,
Ex 4 local honey, pickled vegetables, and gourmet j ams. Some of the prepared foods to go include tomato pies, hummus, pasta, tuna
Ex 5 weight AT ALL then you should be eating five servings vegetabbee per day Usually, diets inc Ludewegettabbes AND fruit, but I recormend five serv ings of
Feature 10201 (Layer 24) Randomized incl emb Fuzz
Entropy:
Interpretation: Adverbs "almost" and "encouraging" and sometimes "mystery" that generally indicate degree or extent of something, often used In formal and Informal writing, particularly in descriptive sentences phrases convey nuanced information
1 almost exactly the same words as our mothers while in conversation. It is great when the person you talking are none the
your mother; they
Ex 2
America is being carried
identi fiable people and parties. could be stopped and even reversednaimost at once. Polls consistently show that the American people favor much
Ex 3 </endoftext | when I came home. The squuree, triangles and tracing papers waited for me. stared at the Bachelor's s pad plan for ratmostt an hour
Ex 4 name?
Exotic", Her energy just bubbled forth, the complete opposite of the icier Solus. He 's triple threat in ingatmost
Ex 5
to check sizing and how it all comes together. It 's's that exact stomach churning shade pink that small girls are armost guaranteed love. The
Activation (0-55.2)
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Feature 10201 (Layer 28)
Randomized incl emb
Entropy: 0.071 Fuzz ROC: 0.937
Interpretation: The token often represents organization, group location where people gather, sometimes specifically for children.
Ex 1
said Etheh Phelas an Along with 20 years of experience in the real estate industry, she also involved number of businesses. 2012,
Ex 2
Ed Deira. the Danes and restored. venerated at Charl bury, Oxon, England (Roeder). ways
Ex 3
850, 000 members, thriving eChapter and over 200 operating Local Chapters m pleased to welcome Ethek into this exceptional group of
Ex 4
Nat National Association of Professional Women ( (NAPW) ) honors Ethek Phel an as a 2017 2018 inductee into VIP Woman the Year Circle. She
Ex 5
Thank you organizing this... It's an amazing form get help for the victims. Am participating www. theauck ye lephant wordpress
Feature 10379 (Layer 28) Randomized incl emb Entropy: 0.156
Interpretation: Varlous Including nouns, technical terms, and common words, emphasized, often representing entities, concepts, or objects that are central to the context or sentence structure.
Ex
on an afte rnoon shopping trip to a mall the Buffalo, New York, suburb of Cheektowaga, Rebecca vanishes, seemingly abducted, Ör
Ex 2 Home Park, known
as Pinewoods. Pinewoods is predominantly Mexican- immigrant community located right outside downtown Athens. Con ducted as
Ex disappearance. Former Scotland Yard detective Colin Sutton says
believes the little girl was abducttdd in targeted kidnap. He told The Mirror that
arem and abductted Bara Fathi Qar awwi The soldiers also invaded Bal town, east of Tulkare
Ex 5
design reviews and risk management reviews. Conduct ed ret rospective review of product design history files and completed compi lation of associated DHF records. Completed rospett gation,
Activation (0-102.5)
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L.5
STEP 0
Feature 10065 (Layer 0) Step Entropy: 0.107 Fuzz 0.821
concept something being below, beneath, foundational something else, often referring underfying structures, causes, principles.
Interpretation:
Ex 1 <I |endoftext |> Kar la muttierez, C Violeta (ww. cas avsalllttauullm com) typifies Tulum barefoot
<|eedoftext Juary or Condolence page for Silvia Gutierrez Moya Fairy tale bedrooms for adults bedroom furniture adultsf airy. Bedroom fairyt
Ex 3 Your Rain Gutters perform essential purpose for your house and should be Looked after by the best Gutter cleaning company can identify. At Clean Pro Gutter
Ex 4 New roof Re roof, Leak repair, Chimney Pointing, Ventilation system installation, Flat roofs, Gutiters Gutter protection
</endofteet Hoot Roderick Paulate, and two of the current Showbiz rentral hosts Raymond Gut rez andJennylyn Mercado. The new show
Activation (0 0.74.)
Feature 10222 (Layer 0) Step 0
Entropy: 0.061 Fuzz ROC: 0.842
Interpretation: organization, or system.
established guidelines, regulations, standards governing particular activity,
read this article for review of the rutes or to learn them better. Check out the full article below for this eemely helpful knowledge. Judge of will is an
Ex 2
directors according to the rules and regulations of the SEC and Nasda Mr Stark has been des designated as the chairpersons the Compensation Committee. Our
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Activatian (0-3.0)
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Feature 10058 (Layer 4) Entropy: 0.111 Fuzz ROC: 0.835
Step 0
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Ex
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Ex 5
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Feature 10201 (Layer 4) Step Entropy:
0.650
Interpretation: Abbreviated truncated words, often representing organization names, technical common words, typically in formal or technical context.
Ex 1
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Ex 5
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Activation (0-5.5)
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Feature 10064 (Layer 8) Step Entropy: 0.001
Fuzz ROC: 0.937
Interpretation: The dollar sign, indicating unit currency, primarily used in monetary values prices.
1
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Ex 2
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Feature 10092 (Layer 8) Step Entropy: 0.115 Fuzz ROC: 0.655
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for related individuals.
Ex 1 white storks, eagles, black kites, haaks, golden jackals and common king fishers. A truly rare species can be found too:
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Ex 5 was basso. Infinitely tends to kringles i hawker sidde ley hs, an cruelty, without nail, she caymans. Pillows
Activation (0-9.1)
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Feature 10058 (Layer 12) Step Entropy: 0.056
Fuzz ROC: 0.794
Interpretation: Names words ending with the suffix "ier* often indicating noun referring to person, place, or object, sometimes surname or proper noun.
Ex 1
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Feature 10201 (Layer 16) Step 0
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Ex 1
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Feature 10058 (Layer 20) Step 0
Entropy: 0.105 Fuzz ROC: 0.797
Interpretation: Nouns referring to either prepared food item or person's surname, often in the context of recipe, restaurant, or event.
Ex 1
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12
Activation (0-12.1)
Feature 10092 (Layer 20) Entropy: 0.080
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Fuzz ROC: 0.696
The verb "apply" in various contexts, often referring to submitting application, put into practice or use, often for job, scholarship, or in technical sense.
interpretation:
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Ex 5
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Activation (0-10.4)
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Feature 10064 (Layer 24) Step 0
Entropy: 0.065
Fuzz ROC: 0.893
Interpretation: The prefix "App" often appears as part of compound words, usually representing applications apprenticeships.
Ex 1
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16
Activation (0-16.5)
Feature 10379 (Layer 24) Step 0 Entropy: 0.125
Fuzz ROC: 0.923
Common past tense verb forms with suffixe,, typically "ived", "ordered", and "honor', often found formal or written contexts, such as articles, documents, and official reports.
Interpretation:
Ex 1
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Activation (0-8.3)
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Feature 10058 (Layer 28) Step
Entropy: -0.000 Fuzz ROC: 0.971
Interpretation: The token "old" indicating age, usually suffix to adjective of time in descriptive phrase.
Ex 1
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Ex 2
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Ex 3
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Ex 4
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Ex 5
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Feature 10201 (Layer 28) Step
Entropy: 0.108 Fuzz ROC: 0.876
Interpretation: Nouns representing objects, places, concepts, aften including "locations" or "functions", that convey specific roles purposes.
Ex 1
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system of the present invention or may provide multiple display functions such described U.S. patent pending application entitled MODULAR REAR VIEW MIRROR
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Activation (0-10.0)
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Reviews:
[{"review_id": "GiiLIdd8Js", "reviewer": "Reviewer_7Tpb", "summary": "This paper investigates how SAE quality metrics, particularly a few automatic interpretability scoring methods, are effected by SAEs being trained on randomly initialised transformers. They show that similar auto-interpretability results are found for SAEs trained on random and trained transformers. They also provide a toy model of superposition in random networks.", "questions": "What would help improve this work?\n- Significance: I think the most interesting / significant thing about this paper is the token distribution entropy result. More detailed analysis of features which have high token distribution entropy and the kinds of properties which they capture may speak to the kinds of non-trivial features which are more difficult to automatically interpret or annotate. The current implications of the paper are somewhat but not very interesting. If the paper were to propose different SAE architectures (eg: inclusion of Matrioshka SAEs might help), this may provide actionable insights.\n- Presentation: It is odd to focus on the validity of auto-interpretability metrics and yet not conduct more analysis of feature activation patterns / kinds of features. The obvious implication of features on random networks being interpretable is that there may be interpretability illusions associated with SAE feature analysis (results we'd see on random models) - but the paper doesn't really go into this except some feature max activating examples in the appendix where we can't see the feature activation patterns.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "Originality: The paper provides the most in-depth study of the relationship between auto-interpretability scoring methods and whether / how networks were randomly initialised that I am aware of. The use of a networks randomly initialised except for the embeddings space was a good choice and adds to the interestingness of the results since much of the interpretability of SAE features may come from association with and between tokens. \nQuality: The paper is comprehensive - comparing trained modles, randomly initialized models, randomly initialized except-for-embeddings models, step-0  models and \"control\" (which breaks association between tokens) models. They provide and analyse a toy model set up as well providing further insights there. \nSignificance: Token distribution entropy appears to capture something unique about trained models - and relates to previous observations in OpenAI's SAE paper (figure 22, showing 25% latents fire on a small set of tokens, and 75% fire on a wider variety or tokens). I think there is some significance in showing this property might distinguish SAEs trained on random / non-random networks. However, clearly some features should be fairly token aligned so in terms of scoring the \"interpretability\" of a given feature this metric may not be particularly useful.", "weaknesses": "- Significance: While feature dashboards and auto-interpretability explanations are commonly used with SAEs, to my knowledge auto-interpretability scoring methods aren't generally considered to be very useful or meaningful (moreover, many SAE quality metrics are not particularly useful in practice). So while the result that these metrics may not be capturing something meaningful seems well supported - the significance is not particularly clear.\n\n- Soundness: The paper discusses auto-interpretability scoring in the aggregate for the most part - providing no examples of how interpretability scores associated with feature dashboards / max-activating examples. The results are less compelling because we don't see specific examples from each of the SAE-model combinations and the interpretability scoring metrics.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}, {"review_id": "L4d6agZ9qz", "reviewer": "Reviewer_pFem", "summary": "The authors train SAEs on LLMs and on LLMs with randomized weights, apply EleutherAI's automated interpretability pipeline which uses an LLM to find monosemantic explanations for every feature given activating examples, and showed that both score similarly high.\n\nImportantly, this does NOT mean that SAEs fail to learn real, interesting computational features. Rather, the authors show that SAEs trained on random weights can still recover some simple structure and score highly. The paper's main message is simple: Don't use existing auto-interp scores as proof for SAE quality but they also go into great depth to explain why SAEs trained on random weights can still be sparse and contain apparently monosemantic features.\n\nI think the importance of the paper might be a bit limited because auto-interp scores are not usually used to benchmark SAEs; they are used to check the quality of the generated explanation. They never made sense to be used as SAE metrics as they don't measure the quality of an SAE feature.", "questions": "Do you think that auto-interp fails to distinguish random vs learned because (a) auto-interp works on real SAEs but the random SAE's features are simple (low entropy) and thus easy to guess so they get the same score or (b) real SAEs learn complex features that auto-interp methods fails to recover and they are thus as bad as random SAE's. (If b, I'd like to see more evidence for this)\n\nThroughout the paper, you assume that auto-interp scores are used as an SAE quality metric. However, auto-interp scores measure how well the explanation is that the LLM explainer found, and not how great the SAE feature is it measures. Are you saying that despite this, folk are using auto-interp scores as an SAE quality metric? Could you pinpoint to papers that use this metric like that?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "- the title: the title perfectly describes the paper's main finding\n- the methods are elegant and sound\n- the randomization, control, and training configuration (dataset, n tokens, buffer size, k, expansion, etc) is well-chosen.\n- entropy is a great metric to quantify the hypothesis that random SAE's features activate for token identities\n- the authors go into great depth and effort to present a hypothesis, experiments, and evidence that shows why SAEs trained on random weights may still exert, preserve, or amplify superposition.", "weaknesses": "The primary reason why I think this paper is great but not exceptional is a possible limitation in its usefulness (that I'm happy to discuss during the rebuttal period). There could be two reasons for the papers main results: (1) SAEs or auto-interp methods are sus and we should not trust them, and (2), SAEs trained on random weights learn trivial features that are easy to guess and cause the high auto-interp scores. For example, SAEs in layer 0 (or even later) might learn 1 feature for every token (there should be roughly as many features as vocab size, and the embedding likely contains most of the variance and inference can be cut off reasonably well), making it very easy to find a high-scoring feature explanation. Real SAEs might learn features like \"I'm active at \"the\" so I want to predict a noun and I want to predict the object of the previous sentence\" which would be a great, generalizing feature but very hard to guess for the LLM explainer from a small feature dashboard. In fact, the paper argues that the second case is likely true, as they show that the entropy of real SAE features is much greater, especially in later layers where we expect and typically see abstract features.\nIf (2) was the reason for the paper's results, the take-away boils down to \"having great auto-interp scores doesn't mean you have a great model or SAE\". However, although auto-interp is important and used downstream for exploration or circuit tracing, I'm not aware that researchers use this metric to evaluate the quality of their SAE features, limiting the applicability of this paper. If I'm wrong here, could the authors link to papers where auto-interp scores have been used to benchmark SAE features?\n\nA stronger conclusion would be that because random-model SAEs achieve high auto-interp scores, SAEs may not discover meaningful, real features. But I'm not sure if this is true (probably not?).\n\n\nMinor:\n- Why was zero-ablation chosen instead of mean-ablation?\n- I read this paper thinking the authors' goal is to show that SAEs don't learn meaningful features. Although the paper never posits this conclusion and is precise in the abstract, I think that naturally, people reading and skimming the paper will assume that this is the central take away even if this wasn't explicitly written in the paper. It would be great if it could be made more clear what the author's interpretation of the results is and what it is not.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}, {"review_id": "lP8XwvfoP0", "reviewer": "Reviewer_P4Ex", "summary": "This paper investigates automated interpretability metrics for SAEs (“fuzzing” and “detection”, explained variance, cosine similarity, L1, cross-entropy loss) trained on activations from randomly initialized (untrained) LLMs, and evaluates whether these SAEs produce similar scores compared with properly trained transformers and SAEs. The authors find that these commonly used automated interpretability metrics for SAEs are similar for conventionally trained transformers and randomly initialized transformers. The authors conclude that these metrics are therefore insufficient proxies for interpretability, and recommend token distribution entropy as a more consistent means of quantifying the fidelity and “abstractness” of SAE-extracted features.\n\n**Verdict**: This paper is clearly written and has broad relevance for the mechanistic interpretability community. While the paper represents a contribution to the mechanistic interpretability field, particularly in the scope of SAEs, the weaknesses listed below should be addressed in order to give more weight and proper context to the results.", "questions": "- Q1: Why does token distribution entropy only reasonably separate trained from random transformer models >1.0B parameters?\n- Q2: How do “improperly trained” SAEs (for properly trained transformers) perform on these automated interpretability metrics?\n- Q3: How do the results in Section 4 relate to the automated interpretability metrics explored in the previous sections?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- S1: Since SAEs are very widely used tools for interpreting the internals of transformer models, the subject of investigation is of interest to the mechanistic interpretability community as a whole. \n- S2: The paper is largely well-written, with clear explanations and motivations for each experimental design choice. The Related Work section is extensive.\n- S3: A wide range of model sizes are tested (using the Pythia suite); the results and conclusions are therefore more likely to be generalizable.", "weaknesses": "Major:\n- W1: Based on Figure 2, token distribution entropy seems to only separate trained versus randomized transformers in models >1.0B parameters. The authors don’t appear to comment on this, but greater discussion of the specific results of this token distribution entropy metric would be appreciated.\n- W2: Somewhat lost in this paper is the original purpose of automated interpretability metrics for SAEs: to find out if the SAE is properly trained, and to monitor its training. It would be a useful comparison to train an SAE on a normal transformer, but perhaps with clearly poor hyperparameter choices. In particular looking at “fuzzing” and “detection” auto-interp methods on this SAE would be interesting. \n- W3: Section 4 feels only loosely connected to the previous results, and the conclusions drawn are limited. It is too focused on the trained versus random transformers, which is less interesting than the main subject of the paper. As in W2, it would be useful to tie these results back into the original purpose of the SAE interpretability metrics.\n\nMinor:\n- W4: “Latents” is a term used frequently throughout the paper to refer to activations in the SAE latent space but is never properly defined. A short phrase defining this term at the beginning of the paper would help readability.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)