Medical Interpretability and Knowledge Maps of Large Language Models
Authors: Razvan Marinescu
Victoria-Elisabeth Gruber
Diego Fajardo V.
Venue: ICLR 2026 Poster (2026)
Keywords: Large Language Models
Interpretability
Explainability
Medicine
Healthcare
Knowledge Maps
Primary area: interpretability and explainable AI
TL;DR: We do a broad study of medical interpretability for Large Language Models

Abstract: We present a systematic study of medical-domain interpretability in Large Language Models (LLMs). We study how the LLMs both represent and process medical knowledge through four different interpretability techniques: (1) UMAP projections of intermediate activations, (2) gradient-based saliency with respect to the model weights, (3) layer lesioning/removal and (4) activation patching. We present knowledge maps of five LLMs which show, at a coarse-resolution, where knowledge about patient's ages, medical symptoms, diseases and drugs is stored in the models. In particular for Llama3.3-70B, we find that most medical knowledge is processed in the first half of the model's layers. In addition, we find several interesting phenomena: (i) age is often encoded in a non-linear and sometimes discontinuous manner at intermediate layers in the models, (ii) the disease progression representation is non-monotonic and circular at certain layers of the model, (iii) in Llama, drugs cluster better by medical specialty rather than mechanism of action, especially for Llama and (iv) Gemma-27B and MedGemma-27B have activations that collapse at intermediate layers but recover by the final layers. These results can guide future research on fine-tuning, un-learning or de-biasing LLMs for medical tasks by suggesting at which layers in the model these techniques should be applied. We attached our source code to the paper for reproducibility.

Published as a conference paper at ICLR 2026
MEDICAL INTERPRETABILITY AND MAPS OF LARGE LANGUAGE MODELS
KNOWLEDGE
Razvan Marinescu
Victoria-Elisabeth Gruber
Diego Fajardo Lumos AI diego@the tetthhelmmssss. ai
Lumos AI
Lumos AI www.oriittthllmmss..... ai
razvan@ther wmseslumsss.. ai
ABSTRACT
We present systematic study of medicalldomaite interpretability in Large Lan- guage Models (LLMs). We study how the LLMs both represent and process med- ical knowledge through four different interpretability techniques: (1) UMAP pro- jections of intermediate activations, (2) gradient-based saliency with respect the model weights, (3) layer lesioning/removal and (4) activation patching. We present knowledge maps of five LLMs which show, at coarse-resolution, where knowledge about patient's ' ages, medical symptoms, diseases and drugs is stored in the models. In particular for 1lamaa.3-708 we find that most medical knowl- edge is processed in the first half of the model's layers. In addition, several interesting phenomena: (i) age is often encoded in a non-linear and some- times discontinuous manner intermediate layers in the models, (ii) the disease progression representation is non-monotonic and circular at certain layers the model, (iii) in Llama3.3-70B, drugs cluster better medical specialty rather than mechanism of action, especially for Llama3.3-70B and (iv) Gemma3-27B and MedGemma-274 have activations that collapse at intermediate layers but recover by the final layers. These results can guide future research on fine-tuning, un- learning or de-biasing L.LMs for medical tasks by suggesting which layers the model these techniques should be applied. Source code is available at hhttps::p //github. com/ TheLumos /edi cal http://..a..ttteeeettaaa.iaa.aea
find
INTRODUCTION
Large Language Models (LLMs) have achieved impressive performance on variety of tasks, in- cluding for coding, reasoning and in-context learning Radford al. (2019); Coignion et al.) (2024); Hao et al. (2024); Wang et al. (2023); Szymanski et al. (2025): Nguyen et al. (2024). However, the understanding of the underlying mechanisms of how such models represent and store knowledge is still scarce. This is particularly important for medical tasks, where insights about the LLMs' rep- resentations of patient demographics, diseases and drug treatments can help uncover hidden biases, thus making such insights crucial for building safe and trustworthy models.
Over the past few years, a variety of mechanistic interpretability studies have been done on LLMs. A line of research has posited that all features learned by LLMs linear, a claim known in the field as the linear representation hypothesis Bricken et al. (022);; Gurnee & Tegmark (2023); y: Heinzerling & Inui (2024). However, more recent work has shown that this hypothesis is not always true, and that LLMs can learn non-linear representations of features Engels et (2024). such as "days of the week" " or "years of a century", which have circular patterns. Other lines research have focused on understanding actual modules inside the LLM. such as the attention heads (AHs) Zheng et al. (2024). Thus, attention heads have been found that are specialized in associative memories Bietti et al. (003)) that identify particular structures e.g. the relation between the current token and the previous token OOsson et al. (2022); Nanda et al. (023) or duplicate words Wang et al. (0022).
that perform semantic processing related to the subject of the sentence Chughtai et al. | (2024). that
gather context Jin et al. (2024), that perform induction Edelman et al. (2024): Singh et al. (2024): Li
et al. (2024); Crosbie & Shutova (2024) well as other tasks related to latent reasoning and answer preparation, such as ensuring that the answers are correct Wiegreffe et al. (2024), coherent Guo et al. (2024) and faithful to the instructions Tanneru et al. (2024).

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While most of interpretability studies have focused on general knowledge and tasks, few works have so far focused on understanding how LLMs represent and process medical knowledge. Some recent studies Chen et al. (2024); SSavage et al.) (2024) approached medical interpretability by asking the model to explain it's diagnosis decisions. He et al. has identified "modular circuits" a medical- domain LLM (MedI.lama-8B] which found certain circuits detect patient symptoms regardless of whether the model is doing diagnosis, treatment recommendation, or summarization. Wu et al. (2024) replaced the dense latent vectors of Llama Open with a sparse dictionary of medical features, where each dimension explicitly cooresponds to medical concept. Kraišnikovid ct al.] (2025) fine-tuned BERT on corpus of German pathology reports, and then projected the model's 's intermediate activations using t-SNE to visualize clusters of tumor pathologies. However, none of these studies systematically investigated LLM representations across a variety of medical knowledge areas, and in addition each study relied on a single explainability technique. In addition, each study only considered a single LLM. It is thus difficult to draw firm conclusions about the results and whether common representations might emerge across variety of LLMs.
In this work we present a systematic interpretability analysis of five open-source LLMs (Llama3.3- 70B, Gemma3-277 27B. MedGemma-2777 Qwen-32B and PPT-OSS--20B) for medical tasks. We study how the LLMs store knowledge about patient demographics such as age, as well as medical concepts such as symptoms, diseases, disease progression, drug treatments and drug dosages. We build LLM maps that visualize the layers where such knowledge is stored in the LLM. These maps are built by integrating four independent interpretability methods: (1) UMAP projections of interme- diate activations, (2) gradient- saliency with respect to the model weights, (3) layer lesioning (i.c. removing a layer and measuring the degradation in the response) and (4) activation patching (i.e. replacing the outputs of a single layer with the outputs obtained from a different prompt). We choose these techniques due to their simplicity as well as their signficant previous use in the litera- ture/Mllnnes et al. (2018a) 8a): Gana et al. (2024): Zhang et al. (2025); ; Dai et al. (2021). In addition, we find several interesting phenomena: (i) age is encoded in a non-linear manner at intermediate layers in the models, and sometimes shows discontinuities, such as between subjects younger than 18 and those older than 18, (ii) discase progression is circular and hence non non-monotonic at certain layers of the model, where late-stage embeddings come closer the early-stage embeddings (iii) 1lama3.3-70B internally has a drug representation that aligns better with medical specialty rather than mechanism of action, (iv) Gemma and MedGemma models have activations that collapse at intermediate layers. The prompts and the medical/drug classifications were verified by medical professional (Clinical Neuroscience PhD) on our team.
1.1 RELATED WORK
UMAP Embeddings: Zhang et al. (025) have used UMAP embeddings to visualize intermediate
activations in an LLM trained on single-cell RNA data. Bolukbasi et al. (202T); Reif et al. (2019) performed interpretability studies using UMAP on BERT. Yeh et al. (003) built Attentionviz, a
tool for visualizing intermediate embeddings in baaedd models generated using UMAP/t-
SNE/PCA.
Weight Saliency: Dai et al. (2021 used a gradient- saliency method to identify knowledge neurons in pretrained transformers. Fan et al. (2023) used gradient- weight saliency to un-learn the generation of harmful images. Frankle Carbin (0018) used weight sparsity, form of weight saliency, to achieve better generalization in neural network predictions.
Layer and Attention Head Lesioning: In neuroscience, lesioning studies, which involve removing a brain region and studying which cognitive, motor or sensory functions are lost, have successfully identified functions of specific brain regions. In AI interpretability. Logit-Lens nostalgebraist and Tuned-Lens Belrose et al. (2023) were used to interpret models such as GPT-Nco-2.7B by taking intermediate activations at a particular layer and computing the logit directly with it, thus removing all layers after it. Michel et al. (2019) studied BERT outputs after removed one attention head at a time, as well as all-but-one attention heads within a layer. |Voita et al. (2019) used layer-wise relevance propagation Binder et al. (016)) to study the importance of different attention heads before pruning, and they found that only a few specialized heads do the heavy lifting. Zhang et al. (2024) performed a layer ablation study on Llama3-70B and two smaller LLMs and found that only few layers were important, whereby removing them led to a collapse in the model's performance. Gromov et al. 2024) removed more and more layers in Llama2, Qwen, Mistral and Phi-2, followed
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by small amount of fine-tuning, and found that only by the time 50% of layers were removed did the models' performance significantly degrade.
Causal interventions and Activation Patching: Causal interventions such Activation patching Heimersheim & Nanda (2024); Zhang & Nandal (2023) and Rank-One Model Editing (ROME) Meng et al. (2022) have been used to identity circuits in LLMs and transformer- models, or even functions of specific activation heads [exx Fierro et al. (2024) used activation patching to localize the role of language during knowledge recall.
2 METHODS
In Fig. [w show an overview of our Medical LLM Interpretability study, outlining the process build LLM maps. We run the LLM through variety prompts related to medical knowledge: patient age, symptoms, diseases, drug treatments and drug doses. For each such knowledge area we run four different interpretability methods: (1) UMAP projections of intermediate-layer activations from which we extract quantitative metrics such Silhuette (cluster separation) scores, (2) gradient- based saliency model weights (3) layer lesioning. where we replace each layer with the identity function 7, and score the degradation in the altered response, and (4) activation patching, where we replace the outputs extract quantitative measures from each of these methods (top-right) and plot the LLM map sisting of the layer intervals where the highest quantitative measures are found. Since each method makes different assumptions and has different strengths and weaknesses, using all of them together helps give confidence that the layers identified are indeed the ones where the medical knowledge is stored in the LLM.
single layer with the outputs obtained from a different prompt. We finally
Prompt LLM on:
Run analyses
Extract layers with:
Highest increase in UMAP
Patient Age
UMAP of intermediate activations
Highest Saliency
Silhouette score
Silhoouatle score
Moot-altared resporse
through lesioning
Highest activation patching
effect
Symptoms
EMPT
Weight-based Saliency
LLM Map
2Loss
Age
Diseases
Symptoms
Layer Lesioning
Diseases
0-0000
Normal Altered response
Drug
Drug treatments
0
treatments
Drug dose
Activation Patching
Drug dose
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UMAP
Sallery
Adivation Patching
Figure 1: Overview of our Medical LLM Interpretability study, outlining the process to build LLM maps.
We use the Uniform Manifold Approximation and Projection (UMAP) McInnes et al. 2018b) project the intermediate activations of the LLM into 2D space for visualization. We use n me_neihhorrs = 15, min_dist = 0.1 and cosine distance metric to build the UMAP connectivity graph. To get a quantitative measure of clustering, we compute the Silhuette Shahapure & Nicholas 2020) (cluster separation) score for each layer, which is a measure of how well the activations cluster in the lower-dimensional space. The Silhuette score is defined as:
UMAP:
b(i) - a(i) max(a b(i))
s(i)
(1)
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where a(i) is the average distance between i and all other points in the same cluster, and b(t) ming n; Ez, 22,EC, EC, Ti) is the minimum average distance between i and points in the next closest cluster Cj. To get per-layer value, we average the Silhuette score across all prompts. We chose Silhouette because it directly measures ground-truth label separability without requiring running clustering algorithm such as K-means. For the Silhuette score, project using UMAP on D 30 dimensions (instead of D = 2 used for visualization) in order to retain more information about the original datapoints. We further compute confidence intervals for the Silhuette score using bootstrap resampling.
For age-related prompts, which query patients with ages from 1 to 100, we instead compute local anisotropy, a measure of "ID-ness" of the datapoints. Taking the 2D UMAP-embeddings, the local anisotropy of a single embedding æ; is defined as A, = A where A 2 X2 are the largest two eigenvalues of the local covariance matrix computed from the k 20 nearest neighbors of each point. The final anisotropy value is averaged across all points.
Weight Saliency: We compute the gradient-based saliency the model weights for each layer by &log_ where L is the loss function and w are the model weights. To get a per-layer value, computing Fur
average the saliency across all weights in the attention heads and MLP at each layer. To obtain more reliable results, we average these values across multiple prompts, and also use these statistics to compute confidence intervals.
Layer Lesioning: We replace each layer in the LLM with the identity function I, and score the degradation in the altered response. For scoring the degradation, GPT-4o) and use a rubric where a score of 1/10 means no degradation compared to the original response and 10/10 means full degradation with gibberish responses.
an LLM-as-a-judge (here
Activation Patching: Activation patching is a technique that replaces select internal activations of neural network model. It runs multiple forward passes: clean run (cl), a corrupted run (*), and a patched run (pt). The patching effect is defined as the gap of the model performance between the corrupted and patched run, under an evaluation metric. For our study, we follow the best prac- tices from Heimersheim & Nanda ) Zhang & Nanda (2023). In particular, we only patch the attention heads and the MLP blocks at each layer. We compute the patching effect using the nor- malized logit difference: P LD (lddrr"")-..rirr
(rD (r.r') where LD(r, r') Logit(r) - Logit(r') is the logit difference between responses and r' (in our case, r and r' are single-token responses such as Angina and Pneumonia respectively). This normalized value typically lies in the range [0, 1], where signifies fully restored performance after patching and signifies patching has no effect. To obtain more reliable results, we average these values across multiple prompts (see Appendix sec. A and also use these statistics to compute confidence intervals.
LLM Maps: In order to plot LLM Maps showing where medical knowledge is stored inside the LLM, we select continuous layer intervals from each of the four analyses as follows:
For UMAP, for all prompt types except age, we select the layer interval showing the highest increase in Silhuette score after Gaussian smoothing (a = 1.0), using sliding window of 3 layers to find the maximum average rate of increase. For age-related prompts, we use local anisotropy instead of Silhuette score.
For saliency, layer lesioning and activation patching, we select intervals where values are above the 75th percentile threshold after Gaussian smoothing, requiring minimum 2-1ayer intervals and taking up intervals per analysis.
We finally plot the LLM map consisting of the layer intervals where the highest quantitative mea- sures are found. Since each method makes different assumptions and has different strengths and weaknesses, using all of them together helps give confidence that the layers identified are indeed the ones where the medical knowledge is stored in the LLM.
RESULTS
LLM Medical Maps: In Fig. we show the LLM map for Llama3.3-700 showing where medical knowledge about age, symptoms, diseases, drugs and drug dosages is stored in the LLM. We find
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that subject's age is leamne/prrocessed in layers 0-5, medical symptoms are learned in layers 0-9 as well as 15-40. Knowledge about diseases seems to be found in layers 0-5 or potentially 27-37, while knowledge about drugs is most likely learned in layers 15-45. Finally, knowledge about drug dosage likely learned the first half of the layers (0-40), although the results seem more inconclusive. For Drug dosage, could not use the UMAP results due the lack of clear quantitative measure that we could compute (see Appendix Fig. 22 for an example plot of UMAP results for drug dosage.)
Saliency-based
Lesioning bedeed
Patching-basse based
Age
Symptoms
Diseases
Drugs
Dosage
50 60 70
80
Layer
Figure 2: LLM Map for Llama3.3-70B showing where medical knowledge about age. symptoms, diseases, drugs and drug dosages stored. Each interval shown is estimated quantitatively from four analyses: (1) clutering structure in UMAP embeddings,
(2) high weight saliency values, (3) high degradation upon layer lesioning/ablation and (4) high patching effect in activation patching.
Age manifold is non-linear: In Fig. we show the UMAP analysis for Llama3.3-700 using prompts about subjects with different ages, broken down by gender. The age manifold gender- dependent, and further shows non-linearities throughout many intermediate layers, and sometimes altogether a complete change of direction of age progression. In the last plots we plot the true vs predicted age from a linear regression model at layer 37, where the largest discontinuity is found for "she" prompts. Since the discontinuity between ages 17 and 18, likely captures a separation between teenagers and adults that the model has learned. A similar age discontinuity between 17 and 18 years found for "he" and "someone" prompts (results not shown) the same layer.
Disease Progression representation is circular and non-monotonic: In Fig. 4 we show UMAP- projected intermediate layer activations for Llama3.3-70B using prompts describing the progression of four different diseases: Alzheimer's disease, Parkinson's, ss COVID-19 and COPD. The disease progression manifold shows multiple instances of "circular patterns" across all diseases analyzed. By cireular here mean that the prompts describing late disease stages location to the early-stage prompts. In the penultimate sub-plot we show the closest stages to the first stage (completely healthy with symptoms), ignoring stage 2, and we find that for many diseases, in particular Parkinson's and COPD, late stages (5-9) are closer to stage than earlier stages (e.g. 3-4), suggesting that the disease progression is non-monotonic. A similar result confirmed in the final subplot, where for Alzheimer's disease in particular stages 2-4 are closer to the end stage (death) than later stages in the representations at many intermediate layers.
back to closer
Drugs cluster both by mechanism and specialty: In Fig. 5 we show UMAP projected intermediate layer activations for Llama3.3-70B using prompts about different drugs, broken down by mechanism of action. In Fig. 6 we show similar results for drugs broken down by medical specialty. The L.M learns to represent drugs both by mechanism of action and by medical specialty. Quantitative comparisons with Silhouette Scores (Table []] confirm that across most models, drugs cluster more by medical specialty than mechanism of action.
For Gemma/MedGenmaa, activations collapse at intermediate layers: In Fig. 7 we show UMAP- projected intermediate layer activations for Gemma-27B. We notice that the activations collapse in UMAP space at certain intermediate layers (here layer 20 is shown), although they recover in later layers. Results with more intermediate layers are shown in the Appendix section D We found similar results for MedGemma-211 (see Appendix Fig. 19
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Figure 3: UMAP Analysis using subjects with different ages in Llama3.3-70B The age manifold shows non-linearities throughout many intermediate layers, well discontinuity between sub- jects younger than 17 and those 18 or older. Prompts used are shown at the bottom of the figure. The bottom-right plot confirms that the only discontinuity is at age 18.
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Figure 4: Disease Progression UMAP Llama3.3-70B. The model shows circular, non-monotonic disease progression, in particular for Alzheimer's disease (stages 2 and 4 closest to the final stage at many layers) and Parkinson's 's discase (stage 7 is closest to first stage at most layers). Details about each stage as it was prompted in the model shown in Appendix Table. 5

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Quantitative interpretability metries: Across all 5 LLM and all medical areas, computed key interpretability metrics which we show along with the best layer where these are achieved (Table D. For Age, compute measure of linearity and the age predicted from a linear fit on the embeddings in the 2D UMAP space. For Symptoms, Diseases and Drugs, show the Sillhouette score, while for disease progression
the R of the linear fit between the true age
computed two proxy measures of circularity: (1) the mean MCSFS of closest stages to first stage (CSFS), excluding stage 2 and (2) the mean JCSLS of closest stages end stage (CSLS), excluding the penultimate stage. We excluded the second and penultimate stages due to it being affected by the granularity of the disease progression. For Dosages, we show the patching effect as obtained from activation patching. For all metrics, we show the scores obtained at the best layer. We also show the same metries averaged across layers (Table and the last layer (T
The results show that most models can achieve linear age manifolds at some intermediate layer, even though the final layer Gemma3-277 -7B and Qwen-32B only achieve an R of around 0.6 (Table 6 The best scores for Silhouette scores are achieved mainly by Llama3.3-70B and Qwen-32B. GPT- OSS-120B also achieves good scores variety of categories. MedGemma-27D7 and Gemma3-27B consistently achieve low scores in all categories except Disease Progression at the final layer.
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Figure 8: Selected interpretability metrics per layer for the five models. (first row) Age R measur- ing linearity of age manifold, age anisotropy (a measure of ID-ness), age gradient- based saliency and fraction of successful activation patches. (second row) medical symptoms metrics, (third row) drug dosage saliency and disease metrics and (fourth row) drug treatment metrics. Lines of some models terminate early due to having smaller number of layers (e.g. GPT OSS has only 36 layers).
We further plot key interpretability metrics across all layers for each model (Fig. 8 Llama and GPT show the highest R² across layers. Llama and Qwen show the highest UMAP Silhouette scores for symptoms and diseases, while the last layers of Llama show highest UMAP Silhouette scores for drugs. Highest saliencies are observed in the last layers of Gemma/Medgemna, first layer of Qwen, and layers 15-22 of GPT-OSS. Activation patching analyses show that patching succeeded
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(patching effect 0.5) more often in GPT and Gemma/Medgemmna than in the other models. Layer Lesioning analyses (Appendix Fig. 9 show that removing layers in Gemma/Medgemma seems to lead to severe degradation in the response, while the other models are most robust and thus have redundancy in their layers. Llama and GPT generally show the highest robustness to layer removal, for both early layers and late layers.
Table 1: Per-concept interpretability metrics across models. Values are best layer scores with the layer index. CSFS: Closest Stage to First Stage, CSLS: Closest Stage to Last Stage
Concept
Metric
Lamaa 70B
Gemma 27B
MedGemma 27R
Quen 32B
GPT-OSS 1208
Tayer
Tayer
Tager
Lager
Layer
Age
(lincar)
1,00 =:
2
1.00
Symptams
Silhouste
0.19 0.04
0.06
-0.08 00.05
0.044
Diseuses
Silhourte
0.16 0.05
-000 1.0.4
6]
-0.03 0.00
0.21 1: 0.84
60
0.10 0.05
32
Dicise Progression
CSPS
6:25 = 2.17
4,25 1.09
01
4.25 1.09
4.25 + 1.09
2
5.00 +: 1.5%
3
Disesse Progression
CSLS
5.50 e 2.06
74
5.75 1.30
54
5.75 1:30
52
6.75 0.43
44
6.75 a 0.43
29
Drugs
Silhouetee (mach))
0.07 = 0.08
75
0.03+ 1013+00.0
45
-0.01
0.00
44
0.83 0.0K
62
0.07 11,07
30
Drugs
Silhouette (spec.)
0,19 0.13
0.05 a
44
0:110
0.06 0,07
58
0.07 1 0,07
33
Dosages
Pazching biflett
1,71 1.49
7.33
11.04
23
Table 2: Per-concept interpretability metrics across models. Values mean + std across all layers.
Concept
Metric
Llama 70B
Gemma 27B
MedGemma 27B
Qwen 32B
GPT-OSS 120B
Age
R (lineur)
0.98 = 0.06
0.78
0.78 + 0,18
0.93 0.09
0.98 + 0.01
Symptoms
Silhouette f
0.08 0.08
-0.28 = 0.09
-0.30 # 0.08
0.10 0.09
-0.02 + 0.12
Discases
Silhouette 1
0.05 0.07
-0.21 0.11
-0,22
0.08
-0.06 0.09
Disease Progression
CSFS J.
6.96 a: 1.77
2.11
5.72
7.21 1.59
6,58 + 1.88
Discase Progression
CSLS f
5.08 = 2.00
4.68 2.10
4.66 2.07
5.23 1.98
5.65 1.47
Drugs
Silhouette (mech.)
-0.01 0.08
-0.10
-0.10
-0.08 a 0.06
-0.02 0.07
Drugs
Silbouette (spec.)
0.05 = 0.13
-0112
0.114
-0.09 + 0.07
-0.03
Dosages
Patching Effect
2.20 1.17
0.79 0.70
1,25 0.85
5.16
5.77
We also computed quantitative metrics on six other medical and life science LLMs: OpMnBioLLM- 70B, PMC-LLaMA-13B ClinicalCamel-700) PalmyraMed-70BB Meditron-70B, HuatuoGPT-700.
We show the metrics for the best layer Table averages across all layers in Supplementary Table
7 and the last layer metrics in Supplementary Table
Table 3: Per-concept interpretability metrics across medical LLMs Values are best layer scores (max across depth) with the layer index.
Concept
Matric
Elanad Op 70B
PAIC LLAMA 138
Clisical Camel 748
Polngra Med 70B
Meditron TUB
HostosGPP 7IB
Soom
Laper
Scon
Tayer
Soow
Sconn
Sonr
Loger
Scom:
Agc Sympioms
(inser)
1 1U00
16
I150 (100
5
35
1 a: (U)0)
0099 =s cuto
201
1.00 (LD0
Salbounte
IL94
51
1
[.152 0.25
Q.14
0014
Dacaz Progrssii
CSPS
5.50 2.50
4.25 à 1185
4.00.6.71
5.75 2.58
5.25 1.45
19
6.25
Docac Proprosion
CSLS
7,00 1.,00
34
6,30 0130
38
64
3:30
71
5.55 644
71
5.50
04
Siboratte (eads)
01:66
0.05
23
0.34:2.77
2.15
0113-007
51
0.11
Dosages
Pacdiig Effcet
3.22 1.30
70
21.35 46.35
0.2620.56
79
1.28
49
1.99 1,00
4 DISCUSSION
The knowledge map we derived for Llama3.3-77 3.BBB shows that most medical knowledge is stored in the first half of model layers, while the later half of the model likely stores other types of knowledge. However, GPT-OSS-122
and Qwen-32B show an oppoosite effect, most analyses point to layers in the second half of the model (see appendix sec. C). This suggests that methods trying to finetune these models for medical tasks, or un-learn (Fan et al. (2023)) certain medical biases, should focus on the layers we identified. In addition, our work helps suggest where causal interventions on the models should be focused, in order to change medical concepts.
The non-linearities and discontinuities of the age manifold suggest that age is not learned as a dis- entangled dimension in the LLM model. This will make age-based re-learning or de-biasing more difficult. In addition, the discontinuities noticed at age 18 suggest that Llama might consider the young subjects in group of their own, and might display further biases towards that group of young people in a variety of dimensions. Further investigations are needed to understand exactly what these biases could be.

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The circularity of the disease progression manifold is somewhat suprising. Previous research by Engels et al. (2024) has found other types of internal representations that are circular, such as the days of the week of years in century. While resentation for medical tasks, we hypothesize that a useful representations for disease progression would have three desiderata: (1) it would contain common healthy state across all diseases, (2) optionally a common death state across all diseases and (3) for certain diseases, the disease pro- gression trajectories would converge key mid-points; for example, in Alzheimer's disease, it is hypothesized that both amyloid as well as tau pathology eventually lead to neurodegeneration and brain atrophy, thus the state of reuuo-degenerttion could be converging point for both trajectories, eventually leading to advanced cognitive impairment and dementia. While Llama did satisfy (1) and (2) according to our prompts, for (3) we did not find such converging points in our study. However, due to the difficulty of creating suitable prompts, we only did this for limited number of diseases, so further work needs to be done in order to validate this.
not certain about the benefits of such rep-
The collapse of intermediate layer activations for Gemma/MedGemmaa models warrant further in- vestigation. Even though the activations do recover, we believe there is a possibility that representa- tional power is being wasted in those layers where representations collapse. Further investigations are needed to understand the nature of the collapse, whether it is caused by the model's s architecture, the training regime, or the data used for training, and the implications of such collapse.
4.1 ACTIONABLE RECOMMENDATIONS
Removing age discontinuities and enforcing linear manifold for ages: If one wants to de-bias the age in order to remove the discontinuity at 18 years or to make the age manifold entirely linear, one can fine-tune only those exact layers where age is processed and use an age regularizer in the loss function that maintains linearity, such as fage = * (1 Rages,, UMAP(prompt( ) where R is the linear regression coefficient which will be 1 if the manifold of age embeddings is entirely linear.
To prevent the collapse of Gemma/MedGemmaa the Google team can add during training a Uniformity loss on a hypersphere Wang & Isola (2020) as follows: Luuit log Eizy eex((<a||< 2- A large will incur large penalty for small distances between embeddings z; and zj. thus preventing the collapse into 3-4 blobs as seen in the
Preventing collapse of Gemma/Med
UMAP Fig
Enforcing monotonicity in disease progression: During training, Al labs can add a regularizer that enforces monotonic disease progression, such as:
9-1 mmax (0, d(h,, h1) - h1)))
R ammontooic
which enforces the hidden-state activations ht at disease stage to move further away from f1,, the activation for stage (healthy), where d(-,-) is chosen distance function. These regularizerscan be made even more complex by anchoring not just at h1, but at other intermediate stages as well.
With lack of ground-trutt data, is difficult validate the results we derived. In order to alleviate this, we ran four different analyses (UMAP, saliency, layer lesioning and activation patching) on the same model and thus allow the reader to see the consistency of the results across different methods. Since the methods operate with entirely different assumptions, where they are in agreement, we can be significantly more confident in the results we derived. Even when they are not in agreement, some conclusions saliency and activation patching indicate signal the first half of the model layers (even though they disagree the exact layers), so we can at least draw some broader conclusions even in this case. In addition, as opposed to other interpretability studies, we note that we cannot use human experts to provide ground-truth data due to the lack of understanding of the model's internal representations.
Limitations:
still be drawn. For example in the case of drug dosage, both
ACKNOWLEDGMENTS
This study was supported by Lumos AI. Computational credits were provided by Google Cloud.
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APPENDIX
Notes on LLM usage: We used LLMs for the following:
For brainstroming ideas, such on how to implement the hooks for activation patching and lesioning
For generating portions of our code, such as specific functions, and for debugging
For auto-completions during the writing of the latex file, mainly for latex code generation for tables and figures
For generating some of the prompts used in the analyses, which we then manually reviewed and edited
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A PROMPTS USED
Table 4: Prompt templates used across all analyses. Blue words automatically substituted with variety of values in order to test multiple ages, symptoms, diseases, drugs and drug dosages. Green words denote the expected answers with respect to which we compute gradients (saliency) or logit differences (activation patching).
Analysis
Prompt Templates
Age
UMAP
He is {20} years cld.
Salieney
Answer with a single number, nothing else. At what age somcone logally considered an adult? {18}
Lesioning
How long people live around the world?
Activation
Clean:A (75)-year-cld patient has {memory problems] The most
Patching
likely cause -- [Dementia)
Corrupt:A (s)-year-old patient has {memory problems). The most likely cause is - (
Symptoms
UMAP
À patient has (headache).
Saliency
Answer with a single word. What is the main symptom present in (Pneumonia]2 (headache)
Lesioning
A patient has lupus. What symptems are associated with this disease?
Activation
Clean:For (migraine), a common symptom is fheadache) Corrupt: fepilepsy), common symptom is - (seizures)
Patching
Diseases
UMAP
À patient was diagnosed with (COPD).
Saliency
A patient has fever, productive cough, and chest crackles exam. The most likely diagnosis is (Eneumonia)
A 30-year-ola female patient presents with chronic cough, shortness of breath, and wheezing. what is the most likely diagnosis?
Lesioning
Activation Patching
Clean:A patient has {chest pain and shertness of breath). The most likely ciagnesis is (Angina)
Corrupt:
patient has (chest pain and coughing). The most likely diagnosis is [Pneumonia]
Drugs
UMAP
A patient takes (Atervastatin).
Answer with a single word only, describing a drug. patient suffers from high LDL, cholesterol and has atherosclerotic disease. What drug should the patient take? (Atorvastatin)
Saliency
Lesioning
A patient takes Metformin. What are the side effects?
Activation
Clean:The patient has (ADHD). A common medication is [Adderall}
Patching
Corrupt:The The patient has (depression). common medication is (Sertraline)
Drug Dosage
UMAP
A patient took [5 mg of (Amlodipine].
Saliency
A patient has taken {5} mg of (Amiodiping) will the pat be alive or dead? (alive), (dead)
Lesioning
What is a safe dose of Amlodipine and what is a lethal dose?
Clean:What will happen to the patient? Answer in exactly one English word: stable or dead. patient took (5]ng (Amlodipine). The patient will be (stable)
Activation
Patching
Corrupt:Same the clean prompt, except the dose changed to (100)ng, and expected answer changed to (dead)
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Table 5: Disease progression prompts used for UMAP analysis. Each disease has 9 stages from healthy to death, showing realistic progression patterns.
Disease
Progression Prompts
Alzheimer's Disease
Stage 1
Someone is healthy with symptoma.
Stage
Someone occasionally worries about minor forget fulnss.. ness.
Stage
Someone frequently misplaces items and struggles with recent
memory.
Stage
Someone forgets recent conversations and repeats questions,
Someone st surggle significantl with language and daily tasks.
Stage 5
Someone regularly becomes confused and has trouble recognizing family.
Stage 6
Someone has severe cognitive decline and is unable to communicate clearly.
Stage 7
Someone is bedridden, minimally responsive, after severe cognitive decline.
Stage 8
Stage
Someone just died from severe cognitive decline.
COVID-19
Stage
Someone is healthy with no symptoms, Someone has mild fatigue. Someone has mild fatigue, slight fever, dry cough. Someone has persistent cough, fever, mild shortness of breath.
Stage 2
Stage 3
Stage 4
Stage 5
Someone has worsening respiratory distress, oxygen saturation 908.
Someone has been receiving suppl emental oxygen and is the ICU.
Stage 6
Stage 7
Someone was sedated, intubated and mechan ca ly vent i llated Someone experienced malti-organ failure and declining vital signs.
Stage 8
Someone just died from multi-organ failure and declining vital signs.
Stage 9
COPD
Stage
Someone is healthy with no symptoma, Someone experiences shorcness of breath with strenoous exercise.
Stage 2
Someone experiences shorcness breath when climbing stairs, mild cough.
Stage 3
Stage 4
Someone is breathless during everyday tasks, chronic cough. Someone has regular flare-ups, struggles with daily tasks. Someone requires frequent hospitalizations due to breathing difficulty.
Stage 5
Stage 6
Stage 7
Someone needs supplemental oxygen at home, has severe breathlessness.
Someone experi ences chronic respi failure, continuous oxygen dependence.
Stage 8
Someone just died from chronic respiraton failure, cont inuous oxygen dependence.
Stage
Parkinson's
Disease
Stage
Someone is healthy with no symptoms. Someone has mild tremors in one hand.
Stage 2
Stage
Someone experiences stiffness and slowness of movement.
Stage 4
Someone has mild difficulty with balance and coordination Someone has moderate difficulty with balance and coordination.
Stage
Stage Stage 7
Someone has severe tremors and muscle rigidity. Someone is unable to walk without assistance. Someone is bedridden with severe motor impairment requires full-time care.
Stage 8
Someone just died from severe motor impairment and complications.
Stage
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B ADDITIONAL PERFORMANCE METRICS
Table 6: Per-concept interpretability metrics across models. Values are scores at the last layer (representations right before output).
Concept
Metric
Llama 70B
Gemma 27B
MedGemma 27B
Qwen 32B
GPT-OSS 120B
Age
R² (lineur)
0.98 0.02
0.63 + 0.07
0.90 + 0,06
0.59 0.37
01.8 + 0.00
Symptoms
Silhouette f
-0.14 0.05
-0.13 0.07
-0.16 0.07
-0.01 1: 0.04
0.01 0.05
Diseases
Silhouette 1
-0.03 0.04
-0.01 0.04
-0.03 0.04
40.01 0.05
-0.04 0.03
Discase Progression
CSFS +
7.50 1.12
5.00 + 1.22
5.00 + 1.22
6.25 e: 1.48
7.25 +: 2.05
Progression
CSI.S f
Drugs
Silhouette (mechh.
0.06 = 0,08
-0.03 0.09
-0.01 0.08
0.00
0.03 1 0.07
Drugs
Silhouctte (spec.)
0.16
-0.03 0.11
-0.02 0.100
-0.01 0.07
0.07
Dosages
Patching Effect
2.64
0.67 0.82
0.98 0.63
5.77 9.005
2.40
significant degradation
Lesioning Degradation
Symptoms Degradation
Diseases Lesloning Degradation
Degradetion Score
Dogrodation Scora
Degradatin
degradation
Layer
Layer
Layer
significant degradation
Drugs Lesioning Degradation
Dosage Lesioning Degradation
Models
Degradation Scare
Degradation Score
L'ma 708
Gemma 278
MedGemma
Owen 328
GPT-OSS 1208
60
60
20
degradetin
Layer
Figure Lesioning metrics per layer.
C MAIN LLM MAPS FOR ALL OTHER MODELS
Main
UWP soed
Achvolin Facheptaned
Figure 10: LLM Medical Map for GPT-OSS-120B
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UMAP bases
Saltoncy bases
Lesining based
Actirot on Parching bases
Age
Symptoms
Diseases
Drugs
Dosage
Layer
Figure 11: LLM Medical Map for Qwen-32B
UNAPbbased
Salincybbsed
Activat Patching
Ago
Symptoms
Diseases
Dosage
Layer
Figure 12: LLM Medical Map for Gemma3-27B
Petching
Age
Symptoms
Diseases
Drugs
30
20
30
40
50
Layer
Figure 13: LLM Medical Map for MedGemma-277
18

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Published as a conference paper at ICLR 2026
D FULL PLOTS FOR ALL KEY RESULTS IN MAIN PAPER
Age Layer
100
Layer
Layer
Layer
Layer
Layer
Layer
Layer
Layer
Layer
Layer
Layer 36
Layer
Layer
Layer
Layer
Layer
Layer 50
Layer 52
Layer 54
Layer 56
Layer 58
Layer
Layer
Layer
Layer
Layer
Layer
Layer 76
Layer 78
He ots. he?
Hew
did, they?
Figure 14: UMAP Analysis for Llama3.3-700 using prompts about subjects with different ages. The age manifold shows non-lincarities throughout many intermediate layers (e.g. layer 8). Prompts used are shown at the bottom of the figure.
19

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Published as a conference paper at ICLR 2026
tary
UMAA
UHAFE
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UMARE
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LMA
UMAFL
Layer
UMAAS
UMAri
B
LMAA
15
-2
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-27
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unde
LMA'
UPAP
Figure 15: UMAP embeddings for discase progression for Llama3 3.00BB..-.BB......
20

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Published as a conference paper at ICLR 2026
Liver
Layer 30
Laver 32
Laver 34
Layer 36
Layer
Layer
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Layer 42
Layer
Layer 65
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Layer 23
Layer 25
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Figure 16: Drug UMAP embeddings by mechanism action 1.amma33-700
21

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Published as a conference paper at ICLR 2026
Laver
4g
Layer
Laver 32
Laver 34
Layer 36
Laver
Layer
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UMAA-1
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Rheumatology
Endocrinalogy
infectious
Oncology
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Figure 17: Drug UMAP embeddings by medical specialty in 1lama3.3-70B
22

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Published as a conference paper at ICLR 2026
Lawer
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Layer
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5.0
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Figure 18: Intermediate layer activations collapse in UMAP space for Gemma.
23

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Published as a conference paper at ICLR 2026
Layer
Layat?3
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12
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Figure 19: Intermediate layer activations collapse in UMAP space for MedGemma.
24

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Published as a conference paper at ICLR 2026
E
OTHER UMAP RESULTS ON LLAMA3.3. LLAMA3.3-70B 3370BB
Layet
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Figure 20: Symptom UMAP embeddings for Llama3.3-70B coloured by symptom groups.
25

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Published as a conference paper at ICLR 2026
Layer
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Figure 21: Disease UMAP embeddings for Llama3.3-70B coloured by medical specialties.
26

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Published as a conference paper at ICLR 2026
recommended dese
deadly dose
LLM prodiction Al
LLM indiitton: Dead
1500
10750
20000 mg
Layer
23
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-1.
15
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Layer 56
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Layer 70
14
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50
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Figure 22: UMAP Analysis for Llama3.3-700 using prompts about different drug dosages for Potas- sium Chloride. We ran 50 prompts with different drug dosages between the recommended dose (1,500mg) and deadly dose of 20,000mg. We also analyzed the Llama's response to each dose, and classified it as alive dead. Two groups are forming early on in the internal representation of the LLM, but these are not aligned with alive/dead groups. We don't 't understand this behaviour, and further research needed look into this.
27

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Published as a conference paper at ICLR 2026
F LLAMA3. LL-MA
Leaer2
Levent
Lager
Liver
Liyer
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Figure 23: UMAP Analysis for Llama3.3-700 using prompts about different symptoms.
28

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Published as a conference paper at ICLR 2026
Layer
Layerr
Layerd
Layer
Layer
Laver
Layer
Lpy
20
13
Laaer 30
Layux 24
Lasor 25
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p-cortad
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Figure 24: UMAP Analysis for Llama3.3-70B using prompts about different diseases.
29

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Published as a conference paper at ICLR 2026
G
RESULTS ON OTHER MEDICAL AND LIFE SCIENCE LLMS
Table Per-concept interpretability metrics across models, Values are mean + std across layers.
Concept
Meerie
Llama3 Ope TUB
PMC LIMMA 138
Ciisical Coneel 708
Palmyya Med 708
Meditren 708
HoanuoGPP of 700
Ago
(izzar)
40112
+00-0
Symptoms
Disase Progession
1.91
Diecse Progrzedio
CSLS
536 2.00
5.16 1:35
5,61 1.57
4.56 1.66
546 2 1.57
5.15 2.01
Drugs
Sihooete (meedh
3000 = DU06
063.+000 +.05
0,04 0007
0.04 0:00
= 0.17
RJ5 = IL15
Druss
Siltoattc opeci
0.08 2.000
0.5 10.07
0.77 a
0.10
0.07 0.111
a.13 R.I1
Desges
Paiching Eiflect
5
-343 1245
12:06 25.75
00.67
1.09 2M
1,70
Table 8: Per-concept interpretability metrics across models. Values are scores the last layer (representations right before output).
Concrpt
Openiooo
Palmyys
Jinear
0.8 0.00
+.007
0:58
= 0.12
0,98
Symptems
Silbautti
-0.07 = 0.05
0:8.4
-0:12 0.05
-0.10 0.04
-802 0.004
-0.11 2.04
Disoses
Sitoouna
-0.0 = -0.00
0.00 a 0.88
-0.05 + 0.04
-000 0.04
4001 +1.04
-110 BU0
Diocso Progeadoon
1:17
144
775
7.80 1,87
7:50
Disese Progession
Silhoutte
0.0000.00
0.15
0.11=00:1
Deags
Paiching Ellect
3/22 1.30
3.35 1.3.1
11,89 24.77
0.36 0.56
1.20
1.78 2 0.94
30


Reviews:
[{"review_id": "gBPWB4t2vg", "reviewer": "Reviewer_QKiR", "summary": "This paper presents a systematic study of medical interpretability in large language models (LLMs), introducing “medical knowledge maps” across five open-source models. It combines four complementary interpretability methods—UMAP projections, gradient-based saliency, layer lesioning, and activation patching—to locate where medical knowledge (age, symptoms, diseases, drugs, dosages) is stored within model layers. The work is ambitious and methodologically comprehensive, providing interesting insights into layer-wise organization and representational phenomena such as non-linear age encoding and circular disease progression.", "questions": "* How stable are the LLM maps across different prompt templates or random seeds?\n* Did you consider potential confounding due to tokenization or vocabulary frequency in medical terms?\n* Could the observed circular disease progression simply arise from UMAP distortions rather than true representational topology?\n* Have you tested whether the identified “knowledge layers” align with known reasoning behaviors (e.g., zero-shot diagnosis tasks)?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* The study systematically applies four complementary interpretability methods, yielding a triangulated view of where medical knowledge resides in LLM layers.\n* The work addresses a relatively underexplored area by focusing interpretability specifically on medical-domain knowledge and tasks.\n* The empirical scope spans five models and multiple medical subdomains, improving the breadth and potential generalizability of the findings.\n* The paper presents clear visualizations and “LLM maps,” making the layer-wise organization of knowledge intuitive to interpret.\n* The insights have practical implications by potentially guiding fine-tuning, unlearning, and bias-mitigation strategies for medical LLMs.", "weaknesses": "* The evaluation lacks strong external validation and limited use of ground truth, leaving some claims insufficiently anchored beyond internal metrics.\n* The interpretation of non-linear and circular manifolds remains conceptually ambiguous, risking over-interpretation of representational geometry.\n* The statistical analysis does not thoroughly quantify robustness across seeds, prompt perturbations, or hyperparameter choices, which weakens rigor.\n* The connection to broader interpretability theory and causal abstraction frameworks is not fully developed, reducing theoretical grounding.\n* The reproducibility story in the main text omits detailed settings and implementation specifics (e.g., UMAP and patching parameters), which may hinder independent replication.", "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": "xIJTn17Qf2", "reviewer": "Reviewer_Denk", "summary": "This paper proposes CPA (Causal Prototype Alignment), a framework aiming to enhance the medical interpretability of large language models by aligning learned prototype representations with nodes in a medical causal graph. The method consists of three components:\n(1) a prototype-based representation module that learns latent medical concepts via a prototype bank,\n(2) a causal alignment module that regularizes the proximity between prototypes and causal nodes extracted from medical knowledge graphs (e.g., UMLS), and\n(3) a counterfactual consistency module to enforce robustness under irrelevant feature perturbations.\nExperiments on clinical datasets (e.g., MIMIC-III) show improvements in classification performance (AUC/F1) over baselines such as ProtoPNet and CausalBERT. However, the empirical evidence for interpretability and causal consistency remains weak.", "questions": "1. What ensures that learned prototypes correspond to meaningful medical concepts rather than latent clusters?\n\n2. How was the causal graph constructed and validated for correctness?\n\n3. Can you provide a quantitative metric or human study to support claims of enhanced interpretability?", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. Addresses an important and timely problem — interpretability of medical LLMs.\n\n2. The framework combines multiple paradigms (prototype learning, causal graphs, counterfactual consistency), showing awareness of interpretability literature.\n\n3. Implementation appears systematic, and empirical results show consistent performance gains on standard classification metrics.", "weaknesses": "1. The paper suffers from conceptual ambiguity. The definitions of the prototype, causal node, and alignment mechanism are unclear, and the core idea is only described at a high level without being formally defined or mathematically grounded.\n\n2. The work shows weak causal grounding. The so-called causal graph is loosely constructed from UMLS relations and textual templates, but it lacks genuine causal semantics, and no causal inference or intervention analysis is performed.\n\n3. The paper presents misaligned evaluation. Most experimental results emphasize predictive performance metrics such as AUC and F1, rather than interpretability, and there are no objective metrics or human studies that convincingly demonstrate improvements in interpretability.\n\n4. The paper provides a poor explanation of figures. The numerous visualizations are largely descriptive rather than analytical, and they do not clearly show how interpretability or causal reasoning emerges from the proposed model.\n\n5. There are serious reproducibility concerns. The paper omits essential implementation details, including how the causal graph is built, how prototypes are aligned with causal nodes, and how the model’s hyperparameters are selected or tuned.", "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": "tEwxP8PVur", "reviewer": "Reviewer_W4B3", "summary": "This paper presents an interpretability analysis of five popular LLMs, focusing on the medical knowledge encoded within their layers. The authors employ four interpretability methods: gradient-based saliency, UMAP projections, activation patching, and layer lesioning. They examine how medical knowledge is localized across patient demographics, diseases, and drug treatments.", "questions": "Please refer to the weaknesses.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper explores an interesting and typically underrepresented domain within interpretability research.\n- It employs multiple complementary approaches, offering a broad and consolidated set of metrics and analysis.", "weaknesses": "**[W1]** While the paper presents a substantial number of results and metrics, it falls short in translating these insights into actionable recommendations. Even in the discussion section, the authors primarily offer intuition behind their findings but acknowledge that further analysis is required. To enhance the practical relevance of this work, authors can provide a clearer argumentation or implementations regarding how their analysis could inform model development.\n\n**[W2]** The paper does not specify key statistics or details about the data used to probe the models and conduct the mechanistic analysis (apart from the prompt templates listed in the Appendix). Given that interpretability results can be highly data-dependent, this omission makes it difficult to assess the generalizability of the findings.", "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": "kS7cei2pVd", "reviewer": "Reviewer_bgnx", "summary": "This paper has focused on the medical-domain interpretability in large language models. The authors have found that existing works have only explored limited medical knowledge areas with a single explainability technique. To fill the gap in this field, this paper has explored four interpretability methods on five various LLMs. Through extensive experiments, the authors have found several conclusions.", "questions": "All my questions have been included in the weakness section.", "limitations": "", "rating": 4, "confidence": 2, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "+ S1. This paper is well-organized and well-written, making it easy to follow.\n+ S2. Extensive experiments have been conducted.\n+ S3. The code is released, making it easy to reproduce.", "weaknesses": "- W1. The motivation for the specific exploration in medical areas is insufficient. It is still unclear why the interpretability techniques cannot be well adopted in the medical area.\n- W2. The authors have claimed that they focused on the medical domain, but only one medical-specific LLM is experimented with in this paper, i.e., MedGemma-27B. In my view, more related LLMs, such as Huatuo-GPT, should be considered in this paper, instead of general-purpose LLMs.\n- W3. Though the authors have argued that previous works only consider one of the medical knowledge areas. However, this paper also only considers them independently, while ignoring the relationships between them. Thus, it seems that this paper has only conducted more experiments but has not addressed this issue basically.\n- W4. Why are the four interpretability methods, i.e., UMAP, gradient-based saliency, layer lesioning, and activation patching, selected in this paper? What's the selection criterion?", "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)