Reproducibility study of "Competition of Mechanisms: Tracing How Language Models Handle Facts and Counterfactuals"
Authors: Tijs Wiegman
Leyla Perotti
Viktória Pravdová
Ori Brand
Maria Heuss
Venue: Accepted by TMLR ()

Abstract: This paper presents a reproducibility study of Ortu et al. (2024), investigating the competition of the factual recall and counterfactual in-context adaptation mechanisms in GPT-2. We extend experiments developed by the original authors with softmax-normalized logits as another metric for gauging the evolution of the scoring of tokens in the model. Our reproduced and extended experiments validate the original paper's main claims regarding the location of the competition of mechanisms in GPT-2, i.e. that the competition emerges predominantly in later layers, and is driven by the attention blocks corresponding to a subset of specialized attention heads. Additionally, we explore intervention strategies based on attention modification to increase factual accuracy. We find that boosting multiple attention heads involved in factual recall simultaneously can have a synergistic effect on factual accuracy, which is further enhanced by the suppression of copy heads. Finally, we rework how the competition of mechanisms is conceptualized and find that the specialized factual recall heads identified by Ortu et al. (2024) act as copy regulators, penalizing counterfactual in-context adaptation and rewarding the copying of factual information.

Published in Transactions on Machine Learning Research (7/2025)
Reproducibility study of "Competition of Mechanisms: Tracing How Language Mod- els Handle Facts and Counterfactuals"
Tijs Wiegman University of Amsterdam
tijs. wis..iiggman@ssudell.uuoo
Leyla Perotti University of Amsterdam
legll..errotii@sstdeed.......
Viktória Pravdová University of Amsterdam
Ori Brand University of Amsterdam
ort.branad@studdenl..uuuu
Maria Heuss University of Amsterdam
m. m.c.heess@uva..
Reviewed on OpenReview: https:
//
//tp://pppp openreview. net/ forum? id= VCG6j3t
Abstract
This paper presents a reproducibility study of Ortu el al. (2024), 1 investigating the compe- tition of the factual recall and counterfactual in-context adaptation mechanisms in GPT-2. We extend experiments developed by the original authors with softmax-normalized logits another metric for ganging the evolution of the scoring of tokens in the model. Our reproduced and extended experiments validate the original paper's s main claims regarding the location of the competition of mechanisms in GPT-2, i.e. that the competition emerges predominantly in later layors, and is driven by the attention blocks corresponding to a sub- set of specialized attention heads. Additionally, we explore intervention strategies based on attention modification increase factual accuracy. We find that boosting multiple atten- tion heads involved in factual recall simultaneously can have a synergistic effect on factual accuracy, which is further enhanced by the suppression of copy heads. Finally, we rework how the competition of mechanisms is conceptualized and find that the specialized factual recall heads identified by Ortu et al. (0044) act as copy regulators, penalizing counterfactual in-context adaplation and rewarding the copying of factual information.
1 Introduction
In recent years, transformer-baseee language models have shown impressive performance variety of natural language processing (NLP) tasks [Min et al. 2023). While great advances in the performance of these models have been made, understanding how they operate internally remains challenge. Mochanistic interpretability (Bereska and Gavves 2024), field of study which aims to explain the mechanisms in models by reverse engineering algorithms, provides some clues about the existence and nature of these mechanisms. One way in which this can be done is by identifying specific architectural components, also called mechanisms, within the model, such as attention heads or MLP layers, that play crucial roles in generating particular types of predictions (Elhage al. 2021

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Within the framework of mechanistic interpretability, previous work has laid out the foundation for con- sidering transformer-based language models as collection of mechanisms that jointly and systematically act on imputs (Olah et al. 20200
Olsson et al. 2022 inter alia). Other work has focused on identifying the exact mechanisms found in LLMs as well as their provenance and robustness across different modcls (Wang et al. 2023 Conmy el al. 2023 inter alia). This has enabled the discovery and characterization of single mochanisms such as the copy mechanism (Elhage et al. 2021] and the factual recall mechanism (Yu et al.
2023), which are both explored in this paper.
In this work, focus on selection of claims made in Ortu et al. (2024) that are related to the competition of mechanisms in GPT-2. The competition of mechanisms refers to the interaction between the factual knowledge recall mechanism and the in-context adaptation or copy mechanism. The factual recall mechanism allows models to access and utilize knowledge stored in their parameters during training, enabling them to make predictions based on previously learned facts. Mcanwhile, the in-context adaptation or copy mechanism facilitates dynamic updating of model behavior based on immediate context. Essentially, the copy mechanism allows the model to directly use new information introduced via prompting by "copying', even if the new information contradicts the model's stored knowledge. These mechanisms naturally compete when presented with contradictory information, creating a knowledge conflict (Xu et al. 2024) in which the model must determine whether to rely on its stored knowledge or adapt to new contextual information.
We attempt to reproduce the results presented by Ortu et al. (2024) and consider the original paper's claims with an alternative metric, softmax-nnormalized logits. Furthermore, we examine additional strategies for increasing model's factual accuracy, including separate and simultaneous boosting and suppression of mechanisms involved in factual recall and in-context adaptation. In doing this, we seek to shed some light on two main questions: can we use probabilities instead of logits to understand how the competition of mechanisms evolves in the model and how can we best characterize the attention heads involved in the factual recall and copy mechanisms,
Our contributions can be summarized as follows. First, we find that softmaxx-nommalizttion does not give significantly different results when probing the importance of different model components obtained using logits, providing evidence for the robustness of logit inspection. Second, we observe synergistic interactions between copy heads, as the effect of suppressing multiple copy heads at once is greater than the sum of suppressing the heads individually; to the best. of our knowledge, this has not been discussed in this way in the literature before. Third, we describe a regulational functionality of the copy mechanism mediated by attention heads that supports the copying of factual tokens and penalization of in-context adaptation to incorrect information as an alternative to a separate factual recall mechanism. Finally, we find evidence that the competition of mechanisms proposed by Ortu et al. (2024) easily perturbed by minor changes in the structure of the data provided to the model.
In this paper, we successfully reproduce the main experiments in Ortu et al. 2024 The scope of our reproduction is outlined in Section 2 We explain the methods adopted in the original paper and in this paper in Section 3 and present our results for the reproduced experiments and further investigation in Section | Finally, we discuss our results, reproduction experience, and conclude in Section 5 Our final codebase for our experiments has been made public.
2 Scope of reproducibility
We mcasure the competition between the two mechanisms based on the activations from each of the modcl's components. We say that a component, is highly activated if the magnitude of the difference between the activations of the component a factual token and counterfactual token is large.
The claims we discuss in this investigation are:
Claim 1: Both the individual mechanisms and competition take place in late, but not early layers.
Claim 2: The attention blocks play a larger role in the competition of mechanisms than the MLP blocks.
code: httpl/ttt.//gggtaa.aaa/a/eeee/ee Ratriiisctttttacccacaaoooooeeoit Reproccccoodcd
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Claim 3: A few specialized attention heads contribute the most to the competition.
Claim 4: All the highly activated heads attend to the same position: the attribute token.
Claim 5: The factual information flows by penalizing the counterfactual attribute rather than promoting the factual subject.
Claim 6: Modifying a few selected values in the attention map greatly increases the factual recall, jumping from 4% to 50% for GPT-2.
3 Methodology
3.1 Problem setup
Following the setup in Ortu al. (2024), we look into the next token completions of GPT-2 to compare the outcomes of the factual recall and in-context adaptation mechanisms. The competition of mechanisms is elicited by the prompt given to the model. The model contains encoded knowledge in its parametric memory, which can be retrieved by its factual recall mechanism when prompted. The model will also repeat information provided to its context, even if it is not factual. We call this its counterfactual mechanism. By first stating counterfactual statement, followed by an incomplete repetition of the statement (as is described in Section 3.4. both mechanisms are triggered. The model is then made to decide between two tokens, the token completing the statement factually, ffact, and the token repeating the counterfactual information, leufa: beginning the competition between the two mechanisms.
We which mechanism is preferred by comparing the model's scoring of tfact tcols in various locations inside the model. To compare how and where this preference is decided, we analyze how the scores of the target tokens evolve throughout the model's layers and components, utilizing multiple methods for explainability.
3.2 Explainability methods
In their paper, Ortu al. use two methods, logit inspection and attention mod- ification, to track the two competing mechanisms over the model's processing of the input. When reproducing their results, we also utilize these methods.
HL Bock
Anverion fince
Attendonn Hose
3.2.1 Logit inspection
To trace the competing mechanisms over various positions the model, the original paper tracks the logit values of the factual and counterfactual tokens, denoted fact and fcota respectively, using Logit Lens (Nostalgebraist, 2020).
LN
Logit Lens is method that can give insight into how the model's scoring of tokens changes throughout its layers. Logit Lens works by projecting model's intermediate representations from the embedding space into the vocabulary space, allowing a view into intermediate logits of vocabulary tokens.
LN
The model's architocture consists of an initial 'embodded" representation of the text, followed by an update added by each transformer block. In trans- former model, the final transformer block is followed by two layers: a layer- normalization layer and linear layer, with the latter represented by the 'un- embedding matrix'. This matrix transforms the hidden size internal embedding into vocabulary-sized logits, representing the model's score for each token in its vocabulary.
Figure 1: The internals of a transformer block, with markers indicating whore the altribution Logit Lens interventions take place.
Logit Lens applies these two output layers to intermediate activations posi- tioned in between transformer blocks to calculate intermediate logits.
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Logit inspection can be applied at other locations in the model too. The traditional application of Logit Lens is to apply it only after full transformer block, and this leads to a full set of intermediate logit scores. However, we can also use this to track the changes to the token scores in specific components. We do this by applying the Logit Lens to contribution activations, which are the activations before they are combined with the residual stream, resulting in so-called attribution logits. Figure [ iilusttates the locations where we take the activations to compute attribution logits. Applying Logit Lens after the attention (ATT) and multi-layer perception (MLP) blocks, indicated in Figure [ in orange and purple respectively, we can follow the influence of these blocks the token scores. When applied directly after attention head, indicated in yellow in Figure [ this shows the specific attention head's attribution to the token scores, allowing to identify precise influences.
A common implementation of Logit Lens is provided by the TransformerLens library (aada and Bloom 2022 which was used Ortu et al. (2024) and also in our reproduction and further experiments.
3.2.2 Attention modification
Attention modification a type model intervention exccuted by modifying the values of attention weights. In particular, when applied to select components, this can be used as a way to understand how information flows through the model by inftuencing model's output.
We follow the method outlined by Ortu et al. (2024). That is, we intervene on the attention matrix AM of the h-th head at the (-th attention layer. When modifying the attention values, we focus on specific entries in the matrix. Specifically, we focus on the positions (i.j) where j < i, which the attention value of the i-th input token attending to the earlier j-th input token. The modification is given by Equation |
Ahl bg
+ aAA
(1)
3.3 Model descriptions
Ortu et al. (2024) Lest their main claims on GPT-2 (Radford el al. 2019). GPT-2 is a decoder-only trans- former model with 124 million parameters. It was pre-trained on large corpus of English data, largely comprised of web pages. GPT-2 is a popular model for mechanistic interpretability as it is smaller in size, accessible, and was one of the earlier transformer-based language models available to the research commu- nity. In addition, the mechanisms of GPT-2 are more widely understood as a significant portion of the early work done in mechanistic interpretability was conducted on GPT-2. Ortu et al. (2024) provide supplemental results using Pythia-6.9B (Biderman et al. 2023 to test the generalizability of their claims.
We use the GPT-2 for the reproduction of the results Ortu al. (2024) and further experiments. Due to resource constraints, the ability to directly compare our results to Ortu et al. (2024), and the more widely available literature on the inner workings of GPT-2, we focus exclusively on this model.
3.4 Datasets
We use two datasets based on COUNTERFACT (Meng et al. 2022). The original COUNTERFACT dataset contains 219,180 tuples of factual relations. These consist of a subject, s, and a relation, r. For each factual statement (s, r, r,_,, there are two possible attributes: a correct attribute and an incorrect attribute. Ortu eet al.] introduce variant of this dataset, which we call CF-Juxtaposed for notational ease. This dataset was filtered to include only statements where the correct attribute consisted of only a single token, and was predicted the most likely next. token by GPT-2. Again, we denote the correct, factual attribute token fiact and the incorrect, counterfactual attribute token by tcola. A subset of 10,000 randomly chosen samples was produced containing entries where the model would predict the factual token for completion
To prompt the model into activating both its factual and counterfactual mechanism, the full prompt contains the statement with the incorrect attribute (s, r, tcola), followed by a repetition of the unfinished statement
PCF-Juxtapoeed Dataset: httpp://haaauutteccccceeettttsts

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(s,7,_). The two statements are then connected by prepending 'Redefine' to the prompt. The full prompt
template is given in Equation 2
('Redefine'', s, Ecofas S, r,T"_
(2)
An example given in Ortu al. (2024) uses the statement 'iPhone was developed by _y, consisting of the subject 8 'iPhone', with relation r "was developed by' with correct altribule lfat 'Apple' and incorrect attribute tools 'Google'. Resulting in the final prompt: 'Redefine: iPhone was developed by Google. iPhone was developed by We conduct experiments using CF-Juxtaposed for the reproduction and our further experiments from Section 4.6 to Section 4.8
3 The second dataset we use is the CounterFact- Tracing (CF- ( dataset developed by Neel Nande This version of the dataset contains statements in the form (s, rs) with a truthful completion and an incorrect completion obtained from the original COUNTERFACT dataset. The counterpart to the statement above in the style of CF-Tracing is "iPhone was developed by". We highlight that prompts in this dataset do not contain a repetition the statement with the incorrect attribute, and that the competition of mechanisms is driven by incorrect prediction within the model instead, not necessarily adapting to particular erroncous token as with CF-Juxtapseed. We use CF- Traciig in Section 4.9
3.5 The argument for normalizing logits
Many of the claims in this paper rely on comparing the magnitudes of logits. Specifically, the magnitudes of logits are compared across layers for Claim 2, across different layer blocks (ATT and MLP) for Claim 3, and across different allention heads for Claim 4.
Using intermediate and attribution logits to understand the contributions the components is a well-founded practice. When normalization layer correctly applied, activations are transformed into a zero-mean and unit variance Gaussian distribution for all inputs, meaning that the scale and center of the inputs are controlled. Assuming the normalized activations are projected using a frozen projection layer (wnembedding matrix), this forms systematic, linear transformation with consistent scale on all inputs. As a result, any differences in the logits are solely due to differences in the input data, not fluctuations in the learned model parameters or input scaling. This method is also attractive because linear transformations are easy and cheap to implement (Nanda 2022
Nevertheless, investigations of salience methods in neural models have shown that results using logits and probabilities can vary in their faithfulness, lending credence to the possibility that empirical results using probabilitics and logits may diverge. The model uses softmax function map logits to output probabilitios, which are then used in predicting the next token. The model is trained based these probabilities, not on logits, so any intermediate representation of a token's position in the model is most naturally expressed in probabilitics and not as logits.
Magnitudes can significantly change after applying the softmax normalization, depending on the logits of the whole vocabulary, For example, two tokens with similar logits can be squashed even closer together by normalization, alternatively they can be spread out further. The same is true for tokens with diverging logits, they can be separated further pulled closer together. The effect of normalization depends how the logits of the target tokens compare to those of the other tokens. Tn conclusion, unlike logits, probabilities more closely reflect the relative token importance (Bastings et al. 2022), which is what we are trying to measure. For this reason, using both metrics is required for a comprehensive analysis into any claims, as well as can provide insights into how good each metric
4 Results & analysis
We begin by reproducing results from Ortu et al. (2024) and analyzing the results. We build on top of the existing experiments with two new lines of exploration. First, we evaluate whether a similar analysis
SCF-Tracing Dataset: http:://wanggg....oooooooeeeeete

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of the competition of mechanisms can be made when activations are normalized with the softmax function. The second investigative line consists of experiments aimed at better understanding how to characterize the behaviors of attention heads involved in the competition of mechanisms. We use a similar setup as in the original paper for attention modification, but instead we cxamine how suppressing copy heads alters factual recall. We then analyze the interactions between the copy heads and factual recall heads and determine whether certain properties are intrinsic to factual recall heads. Finally, we look how models with attention modification behave in more general knowledge recall and model performance tasks.
Reproduced experiments
To reproduce the results from Ortu et al. (2024)- we use the supplementary code provided alongside the paper. After carefully examining the code, we find that the code strongly corresponds to the methods described in the paper. With some tweaks to get the code rumning, reproducing the original experiments consistently yielded the same results and plots. We follow the same reasoning the original paper for our analysis of the results, and present our replicated plots in appendix.
4.1 Intermediate logits per layer
To study in which layers of the model the competition of mechanisms takes place, the original paper uses logit inspection to see how the scores for the fcola and fratt change throughout the layers. Specifically, we examine the activations of the model as it predicts the last token position. To do this, intermediate logits are calculated as described in Section 3.2.1 by applying Logit Lens on the activations after every layer (i.e.
transformer block). We then take the average logit score over all inputs in the dataset.
Figure Appendix A shows an upward trend in the average logit scores across layers, as reported in the original paper (Figure 2b of Ortu et al. (2024)). This demonstrates that the relevance of these tokens increases in later but not early layers. We conclude this holds for the individual mechanisms and the competition between, supporting Claim 1.
4.2 Attribution logits per block
To further analyze which model components are involved in the competition of mechanisms, the original paper investigates the attribution logits of each layer's ATT and MLP blocks. We apply Logit Lens after each block, described in Section 3.2.1 Again average the logits over all inputs and focus the model as it makes predictions for the last token position. We now analyze our results using the difference of the where the logits are computed per block.
attribution logits of afecrand foota, Acota -Log
Blocks with larger Accfll are assumed to affect the model's token scores more, implying more involvement in the competition of mechanisms. Just. as in Ortu et al. (2024), Figure 4b Appendix A (Figures 3a and 3b of Ortu al. (2024)) shows the difference Acota near zero carly layers for both blocks, then increases favor of loofas supporting Claim 1. Also, the difference larger in the ATT blocks, suggesting these blocks have more impact on the competition of mechanisms. We conclude these results also support Claim 2.
4.3 Attribution logits per attention head
In this experiment, we explore whether particular attention heads are more influential on the competition between fcota and (fect. For each layer and every attention head, we inspect the attribution logits using Logit Lens described in Section 3.2.1 averaging over inputs and focusing the last token position. We measure the importance of an attention head using Acofa; now taken as the difference of logit attributions within cach attention head.
Figure 4c in Appendix A as in the original paper (Figure 4a of Ortu et al. 2024 ), shows only a few specialized heads with higher magnitudes of Acota some positive and fewer negative. A large magnitude of Acota corresponds to greater involvement in the competition of mechanisms, and since this only occurs on a
4Code from Orta
2024 http::///tttttcccommmmeeeetttttt

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few specific heads, conclude these results support Claim 3. As these heads are all located in the later layers, this also supports Claim 1.
4.4 Attention head inspection
The original paper now more closely examines the attention heads that seem to be of consequence based on the previous experiment. Specifically we focus L9H6, L9H9, L10HO and L10H10 which favor feofa and L10H7 and L11H10 which lavor lfac.. We investigate the atlention scores between the last token position and the other token positions in the prompts.
Figure 4d in Appendix A shows that the attention scorcs are concentrated at the attribute position in all the aforementioned heads, in agreement with the original paper (Figure 4b Ortu al. (2024)). ) This supports Claim 4. Considering the heads favoring fact mostly attend the attribute position and scarcely to the subject positions, we conclude the factual information penalizes the counterfactual attribute rather than raising the factual subject, confirming Claim 5.
4.5 Attention modification
We replicated the grid search for a over {2, 10, 100) for the two most important factual heads L10H7 and L11H10, as seen in Table 3 in Appendix [ and confirmed a = 5 to be the lowest coefficient with a significant impact factual recall. Beyond a 5, the increase in factual recall is marginal. This supports the results of original paper, specifically Claim 6.
Further experiments
We introduce new experiments and findings. The primary goals of pursuing these experiments is to investigate whether probabilities provide similar level of prediction for analyzing the components most heavily involved in the competition of mechanisms, and how we can best describe the behaviors of these components, primarily attention heads, by intervening on the attention values of the models.
4.6 Logit normalization with softmax
Methods
Using logils may not be optimal and normalizing them could relieve this (see Section 3.5 For this experi- ment, we extend the experiments in Sections and 4.3 of this paper by applying softmax normaliza- tion (over the whole vocabulary). Mirroring the original paper, we define A = Softmax 5oftimaxLLogi((t..... (Logit((rota) Softmax (antmaaxLoogi((Aeee (fact)) the difference between the probabilities of laact and fcofa-
Results
We find that using the softmax does not change the main trends displayed in logits. The results of each experiment are shown in Figure 2 Figure 2a shows identical trends to Figure 2b in Ortu et al. (2024), as the probabilities grow the later layers and lenta dominates fact- Note that the data in this figure is shown on a log scale, meaning the divergence between the curves is much more pronounced. Additionally, the probability of foola gets to about 0.71, meaning its probability is significant. and dominates all other tokens. This suggests both mechanisms take place in the late, but not in the carly layers, supporting Claim 1.
Figure 2b shows the same trends as in Figures 3a and 3b seen in Ortu et al. (024)) as only the later layers seem to contribute and ATT more so than MLP. The magnitude difference between layers is even more significant, as is the magnitude difference between the two block types, ATT and MLP. Thus, this strongly supports Claim 2. Curiously, for layers 9 and 10 (very active layers), the difference of Acota between the attention blocks and MLP block is much more prominent than that observed with Arofa- This could be because ATT block is more relevant than MLP in the competition of mechanisms and using softmax shows this more prominently. Alternatively, could be that the attention block attribution logits have higher variance, which in combination with properties the softmax function results in bigger Acota-
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Figure 2c is very similar to what Ortu al (2024) presented in Figure 4a. The main difference is not the identity of the heads involved in each mechanism, but rather the strength of their contribution. We can see the heads where Arota is positive mostly have larger magnitude compared to the heads where Arofa is negative. The positive head L10HO is especially divergent from the original plot, as it is very dominant in this plot. In heads where Acota is negative, L1 1H3 is most dominant in this plot whilst
Probabiities in the Last Position
-
it was least so in the original plot. These differences indicate that the reported relative involvement of each head in the original paper could be limited. However, Claim 3 is strongly supported by these results, as we see the same set of highly active, specialized heads. Altogether, the experiments using draw the same conclusions about the involvement of model component in the competition of mechanisms using probabilities as those we do with logits.
Layer
(a) Intermediate probabilities per layer for bruct and Leots in the last position (log scale).
logits demonstrate that we can
Adtuntionn Blakk MiP
We continue without using softmax-normmaizztion..
1000
1001
4.7 Suppression of the copy mechanism via attention modification
(b) Difference in attribution proba- bilities Acota for different blocks in the last position.
Methods
We extend the line of reasoning introduced Section 4.5 and attempt to increase factual recall through the suppression of the copy mechanism by modifying the attention heads contributing most to this mechanism. In doing so, hope to learn more about the importance of these attention heads in the competition of mechanisms. We do this in a similar fashion as in Section 4.5 this time selecting the heads responsible for the counter- factual adaptation mechanism. We perform a grid search over the values a E (1, 0.5, 0.2,0.1,00 0.1,0} 0) on the four-combination of relevant heads (L7H10 L9H6 L9H9 and L10H0) determine the best a for further use. Based heuristic understanding and the results of an exploratory grid search (sce Table 5 in Appendix C we choose a value of 0 suppress the relevant atlention heads by total ablation, as it should produce the most pronounced suppression effect of any E Rzo-
Read
Layer
(c) Difference in attribution proba- bilities Acsta for different attention heads in the last positions.
We investigate the impact of modifying individual attention heads relevant to factual recall, as well as all subsets of these heads. We separately boost attention heads contributing to the factual recall mechanism and suppress attention heads relevant to in-context adaptation.
Figure 2: Results of same exper- iments with logit normalization using softmax.
Results
While the individual boosting of the heads L10H7 and L11H10 each yielded significant improvement (23.92% and 18.64% respectively), boosting them together yielded even better results at 50.14%, suggesting a powerful synergistic effect when these specific attention heads are modified together.
We identify that the heads that contribute most to counterfactual adaptation are L7H10, L9H9, L9H6 and L10HO, in agreement with the original paper. We investigate the impact of ablation (modification with 0) of different subsets of these heads on overall factual recall. The results in terms of factual recall % for different subsets can be scen in Table The ablation results reveal an intriguing pattern of head interactions. While individual heads show modest. improvements when ablated alone (with L10HO being the exception at 12.74%), their combined effect is again far greater than the sum of their parts. This is particularly evident with L9H6, and to much lesser extent with L9H9, which barely improves over bascline when ablated individually (4.19%) or in pairs or triplets, yet contributes significantly to the four-head configuration that achieves 38.78% factual recall. This suggests these heads operate synergistically rather than independently,
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with their full potential only realized when working in concert. This is especially notable in the performance jump from the best, three-head combination (28.19%) to all four heads (38.78%).
4.8 Simultaneous factual boosting and copy suppression
%fr
4.13
Methods
5.85
4.19
We now turn to characterizing attention heads involved in the competition of mechanisms by observing the interaction between them. By doing this, we can further understand how the competition of mechanisms works and how the factual rocall mechanism can be boosted. We select the best subsets involved in each mechanism based on the results of Sections 1.5 and 4.7 and evaluate the factuality of responses when boosting and suppressing simultancously.
6.13
12.74
6.21
10.48
4
16.58
6.28
Results
18.17
18.68
Through simultaneous modification of multiple attention heads, we discovered that boosting L10H7 and L11H10 while supprcssing L7H10, L9H9, L9H6, and L10HO yields factual recall rate of 78.81,, as seen in Table 2 This, how- ever, is only 1.2% higher than the 77.63% that we get when suppressing only L7H10, L9H9 and L10HO while boosting the same heads. Once more, this shows the interplay between attention heads and reinforces our carlier obser- vation about L9H6's unique behavior. While L9H6 plays crucial role in the complete suppression ensemble, its contribution becomes less significant when combined with boosting interventions, suggesting that the boosted heads may compensating for its function through alternative pathways by which in- formation is propagated within the model. This observation provides further evidence of the complex interdependencies between attention heads and their ability to adapt and compensate for modifications in the network.
4
11.06
4
+
25.08
+
4
28.19
+
4
26.08
38.78 4
Table 1:
Factual recall scores for combinations of suppressed heads:
L7H10 L9H6 L9H9
(Horizontal lines separate number of heads selected. suppression).
L10HO
+ denotes
4.9 How many mechanisms?
Methods
7fr
In the CF-Juxtaposed dataset, we frequently have the case where the factual token occurs as substring of the subject, that is, sometimes both the factual and counterfactual tokens are present in the prompt given to the model. An example from the dataset is the prompt 'Redefine: NBC Nightly News premieres on MTV. NBC Nightly News premieres on', where the factual the factual token, "NBC", appears in the prompt part of the subject and the counterfactual token, 'MTV", appears the attribute. In fact, over 60% of the prompts in the data from Ortu et al. (2024) are constructed like this. This casts some doubt as to whether the results in Ortu et al. (2024) are the consequence of what they suggest is a factual recall mechanism or the action of the copy mechanism.
4.13
28.19
26.08
38.78
18.64
1
23.92
1
50.14
4
77.63
4
1
74.74
4
1
78.81
Table 2: Factual recall scores
We aim to shed more light on which mechanism is responsible for the increased correctness in prediction by examining whether the properties of attention heads identified relevant to the factual recall mech- anism generalize beyond the structure of CF-Juxtaposed. We perform similar experiment to Scction 4.8 on the CF- CF-Tracing datasct. Recall that CF-Tracing does not have counterfactual statements prepended to its prompts, as described in Section 3.4 We use the attention head com- binations that yielded the highest factual recall rates in Section 4.8 that is, we boost L10H7 and L11H10
simultaneously boosting and suppressing attention heads:
for
L7H10, L9H6
L9H9
L10HO
L11H10.
L10H7
(L and respectively denote
suppression and boosting).
and suppress L7H10, L9H9, L9H6 and L10HO. Essentially, we ask whether we can still observe the effects of factual recall mechanism when using a different dataset.

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In addition, to control for the repetition of factual token in the subject string, split the CF- dataset into two disjoint subsets based on whether the subject contains the factual token. We end up with a subset of of 2006 prompts in which the factual token appears in the subject, and subset of 19913 samples free of the factual token. We call the aforementioned subsets the copyable and filtered sets, respectively. If an independent factual recall mechanism exists, we expect to see that the number of correct predictions increases on both subsets of CF- Fe-Traaiig when intervening on the attention heads listed above.
Results
30
copyable
In the copyable set, where the factual token is already present in the prompt as part of the subject, we find that boosting the attention heads involved in factual recall increases the accuracy of the model in pre- dicting the correct token from 21.1% to 24.4%. We don't observe any meaningful change in accuracy in the filtered set when boosting factual heads.
25
filtered
% factual recall
15
10-
Suppression of attention heads involved in the copy mechanism yielded more interesting results. Even in the presence of the correct token, the suppression of copy heads causes a reduction of the model's accuracy from 21.1% to 12.1%, and to 15.4% when the suppression is combined with factual boosting, as seen in Figure 3 In the filtered set, suppression also reduces factual recall from 4.1% 3.4%. Simultancously boosting doesn't correct this behavior and observe reduction to 3.5%.
5.
unmodified
suppressed
boasted
suppressed boosted
Figure 3: Percentage of factual re- call on the CF- CF-Tracing dataset.
Based the results of this experiment, we believe that the charac- terization of the attention heads by Ortu et al. (2024) is flawed and occluded by the structure and inherent repetition of the factual token. tion heads Ortu et al.] identify as factual rocall heads come into action only when copying takes place, as seen for the copyable set in Figure much smaller effect than in the setting using CF-Juxtaposed. We reason that these attention heads may act copy regulators, penalizing the copying of counterfactual information, developed through Claim 5 by Ortu et al. (2024) and our experiments. Likewise, these attention heads encourage the copying of facts, remonstrated by the counteracting of copy suppression in Figure 3
Instead, we observe that the atten-
addition, the effects of intervening on these attention heads has a
Nevertheless, seeing as these attention heads do not universally correspond to factual correctness, we cast doubt as to whether Ortu et al. (2024) demonstrates that a competition of mechanisms exists in practice. In particular, we argue that the copy mechanism in GPT-2 explains the behaviors observed in Ortu et al. 2024h and in our experiments using CF--uutaposed.
4.10 Surprisal under attention modification
Methods
A natural question that emerges when modifying the attention patterns of a model is whether the behavior of the model on other tasks changes as well. To compare the performance of the model after attention modification, we measure the perplexity of the baseline and attention modified models on the test split of attention-modiffed
WikiText-2 with a batch size of 32 and stride of 512. We make the same attention modifications as Section 4.9
Results
We determine that the performance of the model does not degrade when modifying the attention heads. We obtain perplexity value of 29.9 over the WikiText-2 test set for both the unmodified and modified models. We reason this result is the way it is because very few entries the attention head matrices are modified and also due to the specialization of the attention heads in each of the mechanisms. Nevertheless, more work, potentially of more qualitative nature, is required better understand how the performance of the model changes when intervening on its attention values.
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5 Discussion
5.1 Further experiments
Softmax normalization
Applying the softmax normalization in Section 4.6 the experiments of Figures Za and 2b resulted in near identical trends. This suggests comparing the logits directly is a valid approach for drawing conclusions as was done by Ortu et al. 2024). In Figure 2c however, there a difference in which heads are most active,
as discussed in Section 4.6
Figure 4a orruu al. (2024) suggests that L11H10 is the allention head most involved in the factual recall mechanism, whereas the Sermmax-normaliee plot (Figure 2ch indicates that L11H3 contributes somewhat more the competition. In our informal experimentation, L11H10 had bigger impact on the factuality than L11H3 when boosting the heads involved in factual recall. This indicates that the softmax-normaline might not have the same predictive power as logits in determining the degree to which attention heads are involved in the competition mechanism. Nevertheless, this of L10HO the adaptation mechanism more accurately than plain logits. More work is needed to better understand the practical advantages and disadvantages of each metric.
logits
logits predicted the strength
There some interesting comparisons to be made between heads of the model. For example, L11H10 and L10H7 have been identified as 'negative mover heads", related to copy suppression (Wang et al. 2023 McDougall et al. 2024). which is in agreement with our conclusions. L11H3, however, has been linked to semantic understanding (Lee et al. 2024h, which we did not explicitly confirm or reject.
results and prior literature regarding the
Attention modification
The compounding effect factual recall from applying suppression to both L10H7 and L11H10 simultanc- ously, as mentioned in Section 4.7 hints al synergistic effects. We hypothesize that this is related to the fact that the heads are positioned in different layers, meaning that as the activations propagate, the processing of the first head impacts the inputs the second head. Thus the processing the second hoad affected by the first head, resulting in behavior separate from the behavior of the heads individually. is possible that during training, these combined heads are largely responsible for promoting the counterfactual mechanism over the factual one.
5.2 Reproducibility experience
Some issues that we faced when reproducing the results from Ortu et al. (0024) were understanding parts of the code. We had first debug some of the code order to run their original experiments. In particular, we found the code for experiments using the Logit Lens be lacking in documentation and challenging navigate.
In addition, when adapting the logit altribution experiments for using softmax-normmlized altributions ran into issues of scaling up. Instead of calculating the scores for just the target tokens, we now had to calculate the scores for the entire vocabulary to compute the normalized logits, increasing computation and memory demands. We resolved this issue by running the experiment on a computer cluster and by reducing the batch size
5.3 Conclusion
Outcomes
This study successfully reproduces the experiments of Ortu et al. (2024) and extends the analysis with additional experiments. Our results confirm and strengthen some key claims from the original work, but reveal nuanced picture when exploring new data.
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Within the framework introduced by Ortu et al. (2024) we are able to show that the competition of mech- anisms can also be analyzed using softmax normalized logits and that the results mostly agree with the analysis done using logits. On the task formulated in this framework, boosting multiple factual heads pro- duces a greater effect on the factual recall suggesting synergistic interactions between factual recall heads. In addition, combining factual boosting and counterfactual suppression siguificantly increases factual recall, achieving a factual recall of up 78.78%.
Beyond the framework, find that boosting factual recall heads doesn't improve factual recall in prompts from the COUNTERFACT dataset unless the factual token is present somewhere in the prompt. When the factual token is found in the prompt, factual recall heads align the copying behavior with the model's stored knowledge. These findings characterize the factual recall heads not as promoters of factuality, but more specifically as regulators of factual information within the scope of copying. At the same time, suppressing copy heads significantly reduces factual recall, even the presence of a factual token. The reduction in factual recall occurs even when the factual recall heads are boosted, indicating that the copy mechanism is considerably stronger than the copy regulation performed by the factual recall heads. We also find that attention modification doesn't cause the model's performance, measured by perplexity WikiText-2, to degrade.
Limitations
One of the main limitations the generalizability of our results. Our findings are restricted to one model, GPT-2, and following a similar methodology with another model may yield different results. Another limitation is the dataset we use in Sections
4.7 to 4.8 Over 60% of entries contain the factual token as
part of the prompt, described in Section 4.9 This makes some of our observations about the competition of mechanisms less precise, the copy mechanism may serve both counterfactual in-context adaptation and the copying of factual information that aligns with knowledge stored in the model.
Additionally, this work heavily relics on two mechanistic interpretability techniques, namcly Logit Lens and allention modification. This can be problematic as these methods may not fully capture model behavior and do not provide a clear causal interpretation of the model's prediction. This investigation's 's ability to analyze how components interact and how is somewhat narrow, and could have been strengthened by accompanying it with newer methods different types of explainability techniques. Furthermore, the datasset limits the investigation to predictions comprised of one token on a rather artificial prompting structure.
Future work
A potential future investigation could adopt more recent mechanistic interpretability techniques like circuit analysis (Olah et al. 22200] Elhage et al. p. In light of Section i.9 in particular, it would also be interesting to see if the competition of mechanisms could be reimagined in terms of circuits and whether the roles of the factual recall and copy heads we identify make it so that these heads are part of single circuit multiple competing circuits.
Other mechanistic interpretability methods can be applied to investigate the competition of mechanisms. For example, the Logit Lens technique is being further developed and newer, more advanced tools exist, like Tuned Lens Belrose et al. 2023 and Logit Prisms (Nguyen 2024].
Our experiments are based on the task of single token prediction as a clearcut way of examining the compe- tition of mechanisms, however, this competition is feature of other types of tasks well. Further work to investigate how our analysis of the competition of mechanisms generalizes to other tasks such multi-token prediction or text summarization could be an interesting extension, depending on the capabilities of the model considered.
Additional future work follows from what was proposed in the original paper. The competition of mechanisms could be examined on larger models. The original paper performed additional tests on Pythia-6.9B, but we were not able to reproduce these due to resource constraints. Another possible route of future work could consider the design of the prompt beyond its current structure. Finally, more investigation is needed to understand how the synergistic interaction works, where it originates, what its exact purpose and behavior is, and if more such interactions exist in the model.
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2023, 1123.emmlp-ame manlp-main.. 615/
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A Reproduced figures and tables
Attantion Blet va MUP Blec
Aesto
Logits in the Last Position
615
000
-2.9
45
Layer
(b) Recreations Figures 3a and 3b merged.
Layer
(a) Recreation of Figure 2b.
(c) Recreation of Figure 4a.
203m
055
Layer 10 lead
Lone boy Huddo
Lagar leads
Toloon Fosttonn
(d) Recreation of Figure 4b.
Figure 4: Recreated figures from Ortu et al. 2024
factual recall%
0
0.67
1
4.13
2
32.18
5
50.14
10
51.56
100
52.29
Table 3: Percentage of factual recall with boosting factor a.
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B
Baselines for factual boosting
Methods
Factual Recall by Layer
All positions altered
Attribute position altered
To establish baselines for the efficacy of attention modifica- tion as a strategy for increasing factual recall, we examine how boosting individual attention heads across the model affects the percentage of factual responses produced by the model. In per- forming this experiment, seek to answer if factual recall by boosting random attention heads or if this ca- pability is intrinsic exclusively to the identified factual recall heads. We chose 100 random attention heads (scod=347) from various layers the model, excluding the allention heads we identified as highly involved in the competition of mechanisms. In addition, boost random pairs of attention heads located in different locations within the model, following Section 4.5
Factual %
Jo a U
increase
10311.2
101112
Layer
Layer
Figure 5: Box plot of factual recall af- ter modifying randomly selected attention heads per layer, with 5 to 10 heads altered per layer.
and using boosting factor of a = 5. For both individual heads (Figure () and pairs of attention heads (Table 4 we examine interventions of the attention patterns in the attribute position and all token positions attended by the last token position.
Results
Figure 5 shows that random boosting leads fac- tual accuracy between 1% and 6%. We see less vari- ability in factual accuracy when modifying all to- ken positions across all model layers compared with modifying only the autribute position. In addition, when only modifying the attribute we see that the range of accuracies later layers is larger than in the earlier layers. This is another indication that the competition occurs in the later layers of the model and not in the early layers, supporting Claim 1.
Distance between head pairs
All (Mean %
Attribute
(Mean %frr
Same Layer (d = 0)
4.49
6.68
Consecutive (d 1)
4.02
3.53
<< d d> 5
4.53
4.07
4.24
5.42
Table 4: Random boosting of attention head pairs (pairwise comparisons).
We also note that boosting random head pairs does not increase factual recall, regardless of the distance between the heads a pair and whether the modification was applied to all positions or only the attribute position. When boosting head pairs at the the attribute position, we find that the mean factual recall ranges between 4% and 4.6%. Random modifications of head pairs at all token positions result in mean factual accuracies between 3.5% and 7%, depending the distance between the heads seen in Table 4 Once more, observe greater variability in factual accuracy when modifying all token positions. Additionally, while boosting at the attribute position observe higher mean factual recall for some distance values, there is no clear trend based on distance.
Importantly, both trials demonstrate that random boosting of single heads or head pairs does not cause the factual accuracy to deviate siguificantly from that the unmodified model. This implies that the attention heads observed in Section 4.4 are indeed specialized to the factual recall process and further validates Claim 3.
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C Supplemental experiments
factual recall%
4.13
0.5
21.38
0.2
34.22
0.1
36.7
0
38.78
Table 5: Percentage of factual recall with suppressing factor a.
17


Reviews:
[{"review_id": "yRa2yIvp1t", "reviewer": "Reviewer_CthS", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "**Strengths**\n\n- The paper presents several novel and valuable contributions beyond the original work of Ortu et al. The analysis using softmax probabilities instead of LogitLens is particularly compelling, as it strengthens the credibility of Ortu et al.’s claims through an alternative and arguably more interpretable lens. Moreover, the experiments involving the suppression of attention heads most associated with the copy mechanism, along with well-designed baselines for boosting heads relevant to the factual mechanism, significantly enhance the robustness of the original claims through careful ablation studies.\n\n- Section 4.10 offers an especially important and insightful finding: nearly 60% of the prompts in the dataset used by Ortu et al. include the factual token as part of the subject itself. This raises critical concerns about potential confounding effects, as the performance of the attention modification technique differs substantially between the filtered and copyable subsets. This observation has major implications for interpreting the results of the original study.\n\n- The paper is well-written and provides a clear, detailed overview of both the problem and the methodology proposed by Ortu et al. The experimental section is thoughtfully organized and makes direct comparisons to the original results, which facilitates a transparent and coherent evaluation of the reproduced findings. The novel methodologies and experiments are well-motivated, and the authors offer meaningful discussions on the implications of their empirical observations.\n\n\n**Weaknesses**\n\n- A minor criticism is that some findings could benefit from deeper explanation. For instance, in Section 4.6, the authors state that \"the softmax-normalized result suggests that attention blocks are even more important in the competition mechanism than what was presented by the original paper.\" However, the rationale behind this observation is unclear. Why would using softmax probabilities instead of logits emphasize the role of attention blocks more strongly? A more detailed discussion of this phenomenon would greatly enhance the reader’s understanding.\n\n- The description of the experimental setup in Section 4.10 is somewhat confusing. The authors write, \"we perform the prediction of tokens on a variant of the CounterFact dataset, which does not have counterfactual statements prepended to its prompts.\" If taken literally, this would imply that the model only sees prompts such as \"iPhone was created by _\", without the counterfactual context. In such cases, it’s unclear why or how a competition of mechanisms would arise at all. This may be a typo, and perhaps the authors intended to say that the factual token is not present in the subject of the counterfactual statement, while the counterfactual prompt remains prepended. Clarifying this would help avoid misinterpretation.\n\n- In the original paper, Ortu et al. focus on instances where the counterfactual token is selected (which reportedly occurs in 96% of cases). It would be helpful to know whether the authors of the current study adopt the same strategy. Additionally, if they did not analyze cases where the factual token is chosen, a discussion explaining this decision would be valuable. Would the original claims—such as the competition between mechanisms occurring earlier in the network and being more prominent in attention layers—still hold in those scenarios?\n\n- For the experiments in Sections 4.7 to 4.10, it is not clear whether the authors are using the logit-based inspection method or the softmax-normalized approach. This detail is important and currently easy to miss. It would be helpful to clearly state the methodology used in these sections.\n\n- Finally, it would be a useful addition if the authors could summarize the tweaks or adjustments they made to get the original codebase from Ortu et al. running. Even a brief mention or a link to a GitHub file documenting the changes would be greatly appreciated by researchers attempting to build on this work.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The authors present a thorough reproducibility study of the paper \"Competition of Mechanisms: Tracing How Language Models Handle Facts and Counterfactuals\" by Ortu et al. They successfully replicate most of the original findings and extend the analysis with experiments that employ alternative methodologies. Notably, they demonstrate that the core claims of Ortu et al. remain valid when comparing the model’s preference for factual versus counterfactual tokens using softmax probabilities instead of logits. Additionally, the authors contribute novel insights into the attention modification approach proposed by Ortu et al. by investigating the impact of suppressing attention heads associated with the copy (counterfactual) mechanism. They further analyze the effects of suppressing or boosting the copy and recall heads both independently and in combination. A particularly interesting finding concerns a potential issue in the original dataset: the subject string often contains the factual token, which may have influenced the observed outcomes. By isolating a subset of the data where the factual token is not present in the subject, the authors show that the attention modification technique no longer improves factual accuracy, suggesting that the original results may, in part, be driven by this artifact.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "Please refer to the weakness section above.", "broader_impact_concerns": "I don't think there are any major ethical implications of the work that would require adding a broader impact statement.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "561SV8FSBc", "reviewer": "Reviewer_u2Nt", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "1. Following the guideline for TMLR (\"A proper reproducibility report that systematically studies the robustness or generalizability of a published method and lays out actionable lessons for its audience could satisfy this criterion.\"), I think a reproducibility paper should discuss whether the published work is generalizable and what are actionable lessons. In this work, the authors mostly downloaded the code, and inspected their implementation and reproduced their results. Authors use the same model and dataset. It seems to me that it is a rather trivial reproducibility paper. While it is interesting to examine whether normalizing logits to probabilities it a valid concern, it would be interesting to discuss for other papers and methods, whether using logits versus softmax probabilities would or would not change experiment outcomes and conclusions. \n\n2. In the original paper, authors experiment with different alpha values for supression. However, authors then use alpha=0 and claim \"we perform suppression by ablation as it should produce the most pronounced suppression effect of any alpha > 0\". Even though the intuition makes sense, the methodology is not thorough.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "1. The authors successfully reproduced findings from Ortu et al. (2024)’s\n\n2. They discuss concern of using logits or softmax probabilities as an alternative metric to better reflect token salience during competition. The conclusion is that using normalized probabilities leads to the same conclusion as the original paper. \n\n3. Additionally, they experiment with intervention strategies to boost factual accuracy by boosting attention heads involved in factual recall. In contrast, random attention modification does not significantly affect factual recall, confirming that the competition is driven by the attention blocks corresponding to a subset of specialized attention heads.", "claims_and_evidence_yn": "No", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "1. Related to weakness 1, I think the reproducibility study would be interesting if the work can show the generalizability of published method (different models or datasets), or a broader set of analysis on whether using logits or softmax matter in different MI methods.", "broader_impact_concerns": "No concerns on the ethical implications of the work.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "QhskOGvo8P", "reviewer": "Reviewer_vYuB", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "**Strengths:**\n\n- The code seems to run.\n\n- Figures look somewhat similar to those in the original paper.\n\n**Weaknesses:**\n\n- The paper is bloated with excessively technical detail that obscures rather than clarifies the goals.\n\n- There is far too much focus on confirming what was already shown instead of offering new insights.\n\n- The added experiments with softmax-normalised logits feel arbitrary and do not meaningfully advance the discussion (this should be the crucial improvements).\n\n- Much of the analysis is repetitive and offers no novel conclusions.\n\nTo summarize: The paper aims to replicate Ortu et al. (2024), examining some behaviour in GPT-2. The reproduction is technically sound and includes some additional analysis, the work lacks originality and offers little beyond confirming existing findings. The heavy technical detail detracts from clarity, and the analysis feels repetitive. Overall, the work is derivative, lacks originality, and does not sufficiently motivate why this replication is worth reading.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The paper tries to replicate the findings of Ortu et al. (2024), concerning the so-called “competition of mechanisms” in GPT-2, with a particular focus on **factual recall** vs **counterfactual adaptation**. \n\nHence, the authors reproduce the original experiments and throw in a few extra plots using softmax-normalised logits. There is also some minor tinkering with attention heads and an overuse of vocabulary about copying and recall.", "claims_and_evidence_yn": "No", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "The authors could try to write more clearly, avoid nonessential duplication of the original study, and maybe try a more innovative or critical perspective.", "broader_impact_concerns": "None really. It’s hard to see what actual impact this paper would have, given that it largely echoes prior work with little added value. However, note that I may have made a mistake and not really see the improvemtns so I kindly ask you for clarification.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)