Title: Measuring and Guiding Monosemanticity
Venue: NeurIPS 2025
Accepted: yes
Keywords: Interpretability

Monosemanticity

Sparse Autoencoder

LLM

Alignment
Abstract: There is growing interest in leveraging mechanistic interpretability and controllability to better understand and influence the internal dynamics of large language models (LLMs). However, current methods face fundamental challenges in reliably localizing and manipulating feature representations. Sparse Autoencoders (SAEs) have recently emerged as a promising direction for feature extraction at scale, yet they, too, are limited by incomplete feature isolation and unreliable monosemanticity. To systematically quantify these limitations, we introduce Feature Monosemanticity Score (FMS), a novel metric to quantify feature monosemanticity in latent representation. Building on these insights, we propose Guided Sparse Autoencoders (G-SAE), a method that conditions latent representations on labeled concepts during training. We demonstrate that reliable localization and disentanglement of target concepts within the latent space improve interpretability, detection of behavior, and control. Specifically, our evaluations on toxicity detection, writing style identification, and privacy attribute recognition show that G-SAE not only enhances monosemanticity but also enables more effective and fine-grained steering with less quality degradation. Our findings provide actionable guidelines for measuring and advancing mechanistic interpretability and control of LLMs.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper opens by introducing new metrics to measure two aspects of features in a language model: disentanglement, or how few features are necessary to encode a given concept, and monosemanticity, or how few concepts are encoded by a given feature. Ideally, the features output by a method should be both disentangled and monosemantic. It adds to a growing body of work in showing that SAEs are neither disentangled (enough) nor monosemantic (enough). The paper then introduces G-SAEs, a variant of SAEs that add a supervised term to the loss to target only the features that are known to be present in the text. Using the new metrics, the paper finds that G-SAEs are considerably more monosemantic and disentangled than SAEs.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\n- The study of SAEs is timely, if a bit overcrowded at the moment. I was initially surprised to read that monosemanticity has not been studied thoroughly for SAEs. While I believe that some prior works have indeed studied it to an extent, I think that this paper does a good job of adding to that literature.\n- The introduced metrics make sense to me, although they are a bit arbitrary (see Weaknesses below). The paper’s equations are clear without drowning in notation, and Figure 2 does a good job of conveying the design of G-SAEs. The paper is largely easy to read and understand, which is a big plus.\n- The evaluations support the claims of the paper, and the edge of G-SAEs well. \n- Although I might have missed some, I think the paper cites the most important papers that I know in this space. The authors have done a good job of surveying existing literature.\n\n**Weaknesses**\n\nI will structure this into two parts. First, the weaknesses of the content and contributions of the paper.\n\n- I found the formulations of disentanglement reasonable, but I have some disagreements with Equation 3, the formula for the overall FMS. Specifically,\n- I think one should take the harmonic and not the arithmetic mean of the local and global FMS scores to be robust to outliers.\n- In my view, both the local and global FMS scores measure entanglement (as the authors probably agree, given by the naming convention). On the other hand, I believe that if we wish to use a single number, it should also measure monosemanticity, or “does this feature measure a single concept?” To answer this question necessarily involves some sort of evaluation of how well the feature can recover other concepts—that is, I don’t think that a metric that only ever evaluates feature a on concept a (and never features a on concepts b) can capture monosemanticity.\n- G-SAEs, while performing well, seem to require supervision, which is a clear downside. Additionally, while I do not disagree with the metrics introduced here (or with the fact that one reason to introduce G-SAEs is to test these metrics), it would be nice to also evaluate with more conventional methods to show that the new metrics do in fact agree/improve upon already existing evaluations (for example, prompting an LM to evaluate how faithful an SAE is).\n- In Figure 4: while it is true that the gap between the mean activations of absent and present is higher for G-SAEs, there are also two downsides: (1) the spread of, e.g., the present activations is higher for G-SAEs, and (2) normal SAEs never activative for the “absent” case, but G-SAEs show a small but nonzero activation.\n\nOutside of these, I also have some suggestions for the presentation and flow of the paper. I have not taken these into consideration when providing a score below, and they are simply my opinion, but I still believe these changes will improve the experience of a reader.\n\n- I find the fonts and colors in Figures 1 and 3 a bit dull as well as hard to read. I also think that Figure 4(b) is a bit cluttered.\n- I feel that the title “Measuring and Guiding Monosemanticity” is a bit too broad to clearly convey what the paper is about. This had me expecting a bit less from the paper going in. I will admit that Section 3 onwards made me change my mind, and I had an enjoyable time reading the paper, but I think that the authors can make the title and Section 1 a bit more specific to make readers excited about their results.\n\nQUESTIONS:\n- Can you provide any extraneous evaluation of your new metric and/or the G-SAEs that does not depend upon each other? That is, some evaluation in terms of already accepted norms to provide more confidence in the soundness of these contributions.\n- Have you thought of how G-SAEs may be trained in an unsupervised fashion? I know that this is not the aim of the paper, and I don’t necessarily expect new results, but I would like to know if there are ways to extend G-SAEs to a general setting.\n- Your new metrics apply both to SAEs and to other forms of features that are used in interpretability, e.g., circuits. It would be interesting (and also support point 1 above) if you could use your metrics to compare, e.g., SAEs and transformer circuits in terms of how disentangled they are. My point is that your metrics may be more generally useful to compare not individual feature extractors but classes of feature extractors.\n\nLIMITATIONS:\nRecently, SAEs have come under fire for being “bad” for various reasons: they don’t always find all the features that a model represents [1], may learn different features when trained with different seeds or perturbed weights [2], and are not very monosemantic [3]. The paper does acknowledge these shortcomings at various isolated places, and to be clear: I believe that this work is valuable despite the apparent shortcomings of SAEs. Even then, I believe the paper should centrally acknowledge that these are very real drawbacks of SAEs and related families of methods—possibly in a limitations section.\n\n[1] Leask et. al., “Sparse Autoencoders Do Not Find Canonical Units of Analysis,” ICLR 2025\n\n[2] Li et. al., “Interpretability Illusions with Sparse Autoencoders: Evaluating Robustness of Concept Representations,” arXiv preprint arXiv:2505.16004\n\n[3] Huang et. al., “RAVEL: Evaluating Interpretability Methods on Disentangling Language Model Representations,” ACL 2024", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-22"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces a new metric, the FMS, to systematically measure how well individual features in a model's latent space correspond to single, interpretable concepts. Building on this, the authors propose G-SAEs, which incorporate labeled concept supervision during training to improve monosemanticity, interpretability, and control in LLMs. Their method presumably enables more precise and reliable model steering which they demonstrate through improved performance in tasks like toxicity detection, writing style identification, and privacy attribute recognition.\n\nSTRENGTHS AND WEAKNESSES:\n##**[EDIT: Authors largely addressed the weaknesses mentioned below in their rebuttal.]**\n\n**Strengths:**\n- Overall, the paper presents a meaningful contribution to the interpretability field in my opinion, in particular due to formalizing notions of monosemanticity and providing a generalizable metric for quantifying monosemanticity that is intuitively understandable \n- The authors' results are meaningfully interpretable as they’ve also included statistical analysis compared to previous papers I’ve seen on the subject where it was harder to evaluate the actual signal of an evaluation\n- The writing in the paper is very clear and accessible\n\n**Weaknesses:**\n- My main concern with this paper is that it largely skipped over the initial step in the experimental setup where they state that “given a set of prompts annotated with concept-specific labels” (line 125) and potential impacts of decisions made in this step on the practicality, meaningfulness, and informativeness of their FMS measure and the corresponding G-SAE implementation.\n- In particular, many real-world concepts (like “toxicity,” “privacy,” or “style”) are inherently fuzzy, overlapping, or context-dependent in human language. The paper does not sufficiently address how concept granularity, semantic ambiguity, or inter-concept overlap affect the reliability of FMS or the performance of G-SAEs. In addition, while the authors assume access to labeled data, they do not discuss how label quality, annotation bias, or inconsistencies impact their metric or method. This is particularly relevant given that FMS scores and G-SAE performance are sensitive to these labels, as the latent supervision is directly tied to them.\n- Relatedly, I would have appreciated more evaluations outside of the ones in the paper that are primarily constrained to three domains (toxicity, Shakespearean style, privacy) to better understand for what concepts this approach works versus where it might fall short (e.g., more abstract, ambiguous concepts).\n- In addition, while the writing in general is very clear, I think it could be improved by adding one or two running examples to the writing up until Section 4.\n\nQUESTIONS:\n- What is the expected impact of other classifiers used? In the paper, the authors use binary tree classifiers and note that FMS is method-agnostic.\n- It would also great to better understand at what cost achieving monosemanticity comes in their proposed G-SAEs.\n- In general, a position on the points I raised under “weaknesses”.\n\nLIMITATIONS:\nYes, although they could say more about potential adversarial or ethical concerns raised through steering concepts (line 339).", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper (a) proposes three evaluation metrics for characterizing the degree to which a given sparse autoencoder model (SAE) truly generates representations that semantically isolate a given concept. The paper then (b) proposes a loss modification to the SAE architecture that combines reconstruction loss (standardized) with a term that encourages one neuron to optimally predict the concept(s) of interest. The paper then evaluates the proposed method in the context of several exercises, ranging from toxicity detection in text to privacy concerns. The authors also discuss how the proposed architecture (g-SAE) can improve concept detection and controllable generation by intervening on the single, known latent neuron associated with the concept of interest (no expensive concept-discovery needed).\n\nSTRENGTHS AND WEAKNESSES:\n--Strengths--\n\nThere is much to appreciate about this paper. The writing is exceptionally clear, the evaluation metrics are well-explained and useful, and the architecture change is well-motivated. The empirical evaluation is also solid. I find the simplicity of loss modification and the resulting ease of steering particularly attractive.\n\n--Weaknesses--\n\nThe paper could provide a clearer explanation of where there is a need for both local and global measures of disentanglement. This is explained somewhat in the paragraph beginning, \"Based on the notion above,...\" but could be strengthened. The reader may also not gain a clear empirical sense of how correlated the two measures are for various model/concept pairs. It would be helpful to present some of these correlations. It would also help connect the metrics part of the paper with the loss modification part if the paper explained how the loss modification is more directly targeting improvements in local disentanglement. This could be perceived as a limitation (the loss only directly targets local disentanglement, although could improve both in practice).\n\nQUESTIONS:\nWhat would a loss modification targeting global disentanglement look like?\n\nLIMITATIONS:\nSee discussion on local disentanglement in the modified loss.\n\nAn important limitation is that the original SAE work does not require labeled data for training. This g-SAE approach does. Thus, even though the g-SAE model would be expected to meet much better local disentanglement conditions than vanilla SAE for a given target concept, a given g-SAE cannot be applied to new scenarios. That said, if there is a strong scientific reason for measuring a given concept, the approach outlined here makes a lot of sense.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

comments: SUMMARY:
This paper proposes a novel metric, the Feature Monosemanticity Score (FMS), to measure the monosemanticity of features learned by sparse autoencoders (SAEs) in large language models (LLMs). The authors further introduce a new architecture called Guided Sparse Autoencoders (G-SAE), which incorporates supervision via a conditioning loss to improve monosemanticity and concept disentanglement. Empirical results demonstrate superior monosemanticity, detection, and steering capabilities compared to baseline SAE and state-of-the-art steering methods across tasks such as toxicity reduction, style modulation, and privacy preservation.

STRENGTHS AND WEAKNESSES:
**Strengths** 

 1. The proposed FMS metriccaptures not only the concept representation capacity of individual features but also their local and global disentanglement.

 2. G-SAE is a simple yet effective extension of SAEs that injects supervision during training, ensuring targeted features are better aligned with desired concepts.

**Weakness**

1.  For any kind of explainability a user study demonstrates the utility of the explanation.  For G-SAE a user study will enhance its contribution.

2. The motivation for using monosemanticity is not completely convincing. It would be nice to have one use case demonstrating the utility of monosemanticity for end user.

3. To apply G-SAE you would need a dataset where concepts are annotated. This kind of dataset may not always be available. This limits the use of the proposed approach.

QUESTIONS:
Did you try any weighted loss function for the different components of loss function in G-SAE?

Please also check the weakness enlisted above.

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-04