Cross-model Control: Improving Multiple Large Language Models in One-time Training
Authors: Jiayi Wu
Hao Sun
Hengyi Cai
Lixin Su
Shuaiqiang Wang
Dawei Yin
Xiang Li
Ming Gao
Venue: NeurIPS 2024 poster (2024)
Keywords: Large Language Model
Fine-tune
model transfer
Primary area: natural_language_processing
TL;DR: We propose Cross-model Control (CMC), a method that could improve multiple LLMs in one-time training with a portable tiny language model.

Abstract: The number of large language models (LLMs) with varying parameter scales and vocabularies is increasing. While they deliver powerful performance, they also face a set of common optimization needs to meet specific requirements or standards, such as instruction following or avoiding the output of sensitive information from the real world. However, how to reuse the fine-tuning outcomes of one model to other models to reduce training costs remains a challenge. To bridge this gap, we introduce Cross-model Control (CMC), a method that improves multiple LLMs in one-time training with a portable tiny language model. Specifically, we have observed that the logit shift before and after fine-tuning is remarkably similar across different models. Based on this insight, we incorporate a tiny language model with a minimal number of parameters. By training alongside a frozen template LLM, the tiny model gains the capability to alter the logits output by the LLMs. To make this tiny language model applicable to models with different vocabularies, we propose a novel token mapping strategy named PM-MinED. We have conducted extensive experiments on instruction tuning and unlearning tasks, demonstrating the effectiveness of CMC. Our code is available at https://github.com/wujwyi/CMC

Cross-model Control: Improving Multiple Large Language Models in One-time Training
Jiay Wu', Hao Sun', Hengyi Cai², Lixin Sut, Shuaiqiang Wang', Dawei Yin', Xiang Ming G5
School of Data Science and Engineering, Fast China Normal University
PPkkngg University *Chinese Academy of Sciences "Baidu Inc
KLATASDS- School of Statistics, East China Normal University
Guizhou Zhuwen ECNU Data Power Institute
jiayiwu@stu ecnu. edu. cn, sunhao@stu, pku..du. caihengyi@ict.cm cn, wanlisian, yindawei crrdawei@acm.cco (xiangli, @dase. ecnu. edu. cn
edu. cn
Abstract
The number of large language models (LLMs) with varying parameter scales and vocabularies increasing. While they deliver powerful performance, they also face set of common optimization needs to meet specific requirements standards, such as instruction following or avoiding the output sensitive information from the real world. However, how to reuse the fine-tuning outcomes of one model to other models to reduce training costs remains a challenge. To bridge this gap, introduce Cross-model Control (CMC), method that improves multiple LLMs in one-time training with a portable tiny language model. Specifically, have observed that the logit shift before and after fine-tuning is remarkably similar across different models. Based on this insight, we incorporate a tiny language model with a minimal number of parameters. By training alongside a frozen template LLM, the tiny model gains the capability to alter the logits output by the LLMs. To make this tiny language model applicable to models with different vocabularies, we propose a novel token mapping strategy named PM-MinED. We have conducted extensive experiments on instruction tuning and unlearning tasks, demonstrating the effectiveness of CMC. Our code is available at /ttps://githuu com/wuja wwt//uuwwy//MCCCCC
1 Introduction
In recent years, there has been an increasing number of large language models (LLMs) with varying parameter scales and vocabularies, whose outstanding performance has significantly impacted human society (Achiam et al.) 2023; Zhao et al., (003)). At the same time, although large pre-trained language models have gained the ability handle various natural language processing tasks after pre-training, they generally face a series of common optimization needs to meet specific application requirements or ethical standards. For instance, in the case of instruction following (Wei al.) 2022). after pre-training, vanilla models typically require instruction tuning to develop the capacity to comprehend user instructions accurately. Alternatively, unlearning and detoxification are also necessary considerations (Gehman et al., 2020; CCen and Yang. (023). During the training process, models may encounter data containing real-world personal privacy information or content that harmful, offensive, prejudiced. This could lead them to output these information during the
CCoressooniing Author
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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Template
User LLMs for Diflerent Scales and Vocabularies
L Liama base
Liama 13b base
Liama 70b base
Mistral-Pe To base
+
Portable Tiny LM
Get Abilly
Share
Liama-7b-instuus
Liame- 13-intrtt
lama-70b-nnstrutt
7b-instruct
Figure I: Cross-model control could apply the fine-tuning outcomes of one model to other models
inference stage (Wei al. 2023 Huang et al. 2022). When deploying to large number of users, is crucial to avoid outputting these content.
However, existing methods usually could only optimize single target model a time, such as fine-tuning (Hu et al. [2022] Lester et al. 2021 Liu et al. (2024b), retraining (Keskar et al. 2011)), or activation editing (Li et al. [2023a] Leong et al.) 2023). These methods require altering the model's parameters adding new parameters, which must align with the original parameters. These newly added modified parameter values cannot be applied
models with different structures. Furthermore, although some guided decoding methods (Liu et al. [2021: Krause et al. 2022]] Liu al. 2024a) can be used for a few models of different scales within the same model family, they cannot be applied to models with other vocabularies. Moreover, these methods introduce significant inference burden. For in-context learning (Dong et al., 2022). although this method can alter the behavior of models through natural language prompts, it falls short in satisfying the requirements of complex tasks such as instruction following or unlearning, which are better accomplished through fine-tuning, Consequently, critical question arises: When model owners face constraints data and computational resources, preventing them from directly fine-tuning their models, can they effectively leverage the fine-tuning outcomes of other LLMs at lower cost to improve their models?
To solve this problem, we sought to explore the similarities in the fine-tuning effects across models with different parameter scales and vocabularies. We define the effect of fine-tuning as the change the model's 's output logits after the fine-tuning process, compared to the logits before fine-tum tuning.ng. We discovered that the shifts in logits across different models exhibit high degree of similarity. It further inspires us think: Could a portable neural network model be utilized to alter the outpur logits of various models? Thereby enabling diverse range of models to achieve their optimization requirements through this neural network model.
In this paper, we propose Cross-model Control (CMC), a method that could improve multiple LLMs in one-time training with a portable tiny language model. As demonstrated in Figure T we introduce tiny language model with significantly fewer parameters than mainstream LLMs. This model trained alongside a frozen template LLM, enabling the tiny language model alter the logits output by the L.M.. Subsequently. to facilitate its application across models with different vocabularies, we introduce the strategy of prefix match with minimum edit distance (PM-MinED), lightweight approach for aligning the vocabularies of the user LLM and the tiny language model at the token level. Through this approach, we achieve the training
single model that concurrently improves multiple models. We conducted extensive experiments on instruction tuning and unlearning tasks, demonstrating the effectiveness of CMC, where tiny language model with only million parameters can empower large model with 70 billion parameters, which is thousands of times larger. Our contributions can be summarized as follows:
To the best our knowledge, we are the first to propose a training method that improves multiple models in one-time training. This approach facilitates the multiple utilizations of

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shifts Liama2
logits Mistral--8
Mistrai-78
0-
Lime stags
10-
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Uime
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logits shifts (sorted)
token loghts shifts (sorted)
logils shifts
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Llama2-1
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1eha staps
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Lemi steps
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logits
figure above)
logits
by figure
logits
figure above)
(a) Logits shifs on LLAMA2-132 and LLAMA2-7B.
(b) Logits shifs on MISTRAL-7B and LLAMA2-7B.
(c) Logits shifs on MISTRAL--7B and LLAMA2-13B.
Figure Logits shifs on different models exhibit a high degree of similarity.
fine-tuning outcomes, enabling LLM owners who lack data and computational resources improves their models. Showcasing a novel method for model enhancement.
We conducted a detailed analysis of the similarities in fine-tuning across different models and discovered that the shifts in logits for the same task are similar across various models.
Through extensive experimentation, we have demonstrated the effectiveness of our proposed method. Moreover, we discovered that language models with minimal parameter sizes possess significant potential in assisting L.MSs.
2 Preliminaries
Even though different LLMs may have varying parameter sizes and distinct vocabularies, if these models fine-tuned on the same task, does the effect of fine-tuning exhibit similarities across them?
Given a dataset D and an vamilla model M we fine-tune the model MM using dataset D to obtain fine-tuned model Md. Provided with prompt, the models M and M predict the next token, resulting in Ç. E R [ and Ça E R respectively, where V denotes the size of the vocabulary, We define the effect of fine-tuning as the as the shift logits, that is Ça -a-66 Cu.
When comparing the effects of fine-tuning between two distinct models, MI and Mý, it involves comparing the shifts in logits when encodeing the same sequence. Specifically, given input- response pair, denoted as [21, P2, Zn, V/. Bm.] we fed this pair into the models MY. My. Mý. and M We then record the logits from the final layer output of each model. The logits corresponding to the response part are extracted, yielding Ç"' E Rmx[V Ç E R= G E p= and < E mm=x Val, To reduce the effects of varying scales in the logits from different models, attempted apply the LogSoftmax operation to the logits, thereby transforming them into the same logarithmic probability space. By calculating the difference between the logits after and before fine-tuning, obtain the fine-tuning effects.
TMI LogSoftmax LogSoftmax
TM² LogSoftmax LogSoftmax(S))
We selected three vanilla models, LLAMA2-7B. LLAMA2-13B and MISTRAL-7B. encompassing diverse parameter scales and vocabularies. To investigate the similarity of fine-tuning effects across different models on the same dataset, we fine-tuned them individually on the GPT4-Alpaca dataset (Peng et al.) 2023). All models were fine-tune tnnettnnd using low-rank adaptation (Hu et al. 2022), with hyperparameters provided in Appendix(A)
To facilitate an intuitive comparison of fine-tuning effects across different models, we visualized the shifts in logits before and after fine-tuning the form of heat map Taking Figure 2a an example,
We use "What causes the northern lights?" as the input, and the output of M as the response.

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BecasoN
now
get
ultimate
X
X
now
gets
ultimately
MinED
Log Softmad
MinED
now
get
ultimate
V
loghs:
LogSoftmax
now
gets
estimate X
cittanze
PM-MinED ultimate
Frozen template LLM
User LLM
Delta Model
now
get
Tiny LM
dastanin
now
gets
[ultimately. ull,
Why
Candissie net found by peetie masch
(a) Training stage
(b) Inference stage
(c) Token mapping
Figure 3: Overview of Cross-model Control
we selected the top 100 tokens with the highest logits values in GPPM--lpacc at each time step, S1LAMA2-135
sorted according to the values of LLLAAAZ-133 and applied the corresponding indices TLLAMA2-7B We found that the upper and lower sub-figt sub-figures in Figure were highly similar, both exhibiting trend of larger values on the left and smaller ones on the right, indicating a remarkably similar fine-tuning effect of different models on the same dataset. Additionally, we have quantitatively analyzed the fine-tuning effects across models using Sinkhorn divergence, as detailed in Appendix D
Cross Model Control
Given the observation that the logits shifts before and after fine-tuning in different LLMs are remarkably similar, we introduce a parameter-efficient portable tiny language model designed to learn to alter the output logits of LLMs, which we call the "delta model". Subsequently, we introduce a lightweight token mapping strategy called PM-MinED, Empowered by PM-MinED, the delta model becomes applicable to other LLMS with varying parameter sizes and vocabularies. An overview of the cross-model control illustrated Figure
3.1 Training the portable delta model to alter the output of LLM
In Section 2] we found that the logits shifts before and after fine-tuning LLM are very similar. Therefore, attempt fit the logits shifts of LLM using delta model. Specifically, shown in figure 3al we train the template L.M and delta model together, keeping the template LLM frozen and the delta model tunable. During forward propagation, delta model together to obtain the final logits, aiming to teach the delta model to alter the logits of LLM. Considering that the logits different LLM outputs have different scales, enhance the delta model's applicability during the inference stage after training, we apply a LogSoftmax to the logits of LLM project them into logarithmic space.
add the logits of L.LM and the logits of the
Formally, we denote the template LLM as Mr, the delta model as Ma, a training data sample as æ, the logits output by M, and Ma at the final layer &e and Ça respectively, and the final logits used for calculating the loss as Cysnot.
S Mi(x), Sa = Ma(x) Sfimal LogSoftmax(()) Ga
We do not apply LogSoftmax to the logits Sa of the delta model because it would lead to poorer performance, which we will explain in Section 3.2
3.2 Sharing the portable delta model with other LLMs
After the delta model has acquired the capability to alter LLM logits, we share it with other user LLMs to achieve fine-tunie fine-tuning effects. During the inference stage, the interaction between the LLM

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and the delta model remains consistent with the training stage. We apply LogSoftmax to the logits output by the LLM, projecting them into logarithmic space, and then add them to the logits output by the delta model to obtain the final logits used for decoding. When applying the delta model to L.LSs with different vocabularies, we employ token mapping strategy called PM-MinED map tokens from the delta model's vocabulary to tokens in the LLM's vocabulary. The implementation details of this strategy will be presented in Section 3.3
Formally, we represent the user LLM as Mu, prompt as [To, T., At time step t, the logits output by M, and Ma at the final layer are denoted as çú and ça respectively, where
Su = MM < f), = <
(5)
The logits output by the user LLM assisted by the delta model time step are represented
(6) Here, TokenMap represents the PM-MinED strategy, and a denotes a strength coefficient that adjust the intensity of alteration made by the delta model to the LLM outputs, which will be demonstrated in Section 4.4] We do not apply LogSoftmax to the delta model's logits in both Equations 4 and 6 This is because the outputs of LogSoft are all negative. If token from the LLM cannot find its corresponding delta model token in the token mapping, the logits of this token will not be able receive negative adjustment.
Giint = LogSoftmax(55) + a TokenMap(CA)
3.3 Token Mapping Strategy PM-MinED
To enable the delta model be adapted to user LLMs with varying vocabularies it is imperative to establish a mapping relationship between the vocabularies of the user LLM and the delta model. In previous token mapping strategies. Fu al. (2023) implemented an exact match method, which involves finding tokens in the delta model's ' vocabulary that are completely identical to the tokens of the user LLM;; a perfect match cannot be found, the matching attempt is abandoned. This method's limitation is that the logits of tokens that cannot be perfectly matched will not be adjusted by the delta model. Building upon this, Wan al. (2024) introduced the minimum edit distance strategy, aiming to utilize tokens that cannot be perfectly matched by finding the token in the delta model's vocabulary with the smallest edit distance to the user LLM's token. However, this method might lead to matching tokens with the smallest edit distance but irrelevant semantics, such as erroneously matching "ultimate" with "estimate".
To overcome the aforementioned issues, we propose new mapping strategy: Prefix-Match with Minimal Distance (PM-MinED). This strategy not only considers the edit distance between tokens but also introduces the concept of prefix matching to enhance the accuracy and semantic relevance of the mapping. As illustrated Figure [3c] in the process of matching the token "ultimate" with the corresponding token in the delta model vocabulary, we initially create candidate set consisting of tokens that either have "ultimate" as a prefix or are prefixes of "ultimate", which includes elements such as l"ultimately", "ult", "ul", "u"]. Subsequently, within this candidate set, by comparing the edit distances, the token "ultimately" identified as the one most closely matching "ultimate".
4 Experiment
To evaluate the effectiveness of our method, we conducted experiments on two optimization tasks commonly required for LLMs: instruction tuning and unlearning, with the results presented in Sections 4.1 and 4.2 Furthermore, we analyzed the impact of different sizes of delta models and the strength coefficient studies are demonstrated in Section 4.5) where we investigate the necessity of applying LogSoftmax to the logits of LLM outputs and employing prefix match during token mapping.
performance in Sections 4.3 and 4.4 Finally, the outcomes of the ablation
4.1 Experiment on Instruction Tuning
Instruction tuning refers the process of fine-tuning pre-trained models to better understand and follow human natural language instructions.
We utilized the GPT4-Alpaca dataset (Peng et al., 2023) to train our delta model. This dataset consists of 52k Alpaca data (Taori et al. 2023) and responses generated by GPT-4.
Training Dataset
examples, with instructions sourced from Stanford
5

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Table 1: Instruction tuning results AlapcaEval (Win %). In cross-model control, all base models
incorporate the same delta model, which trained using the LLAMA2-78 as the template model.
Method
Params Add
LLAMA2-7B
LLAMA2-70B LLAMA2-13B
MISTRAL-7B
Vanilla Base Model
4.22
5.34
11.55
6.83
LoRA (upper hound)
110M
68.18
75.16
OOM
79.91
8.47
10.47
8.59
Proxy-tuning
220M
(+4.25)
(+5.13)
(-2,96)
30.41
39.04
49.81
33.29
CMC (ours)
110M
(+26.19)
(+31.51)
(+38.26)
(+26.46)
Evaluation Method We employed the AlpacaEval benchmark (ii et al. 2023b) to evaluate the cnstructionfolllwinne ability of our method. This benchmark includes 805 instructions, with GPT-4 serving as the annotator to compare the output of the tested model against Davinci003's 's output, using win rate as the evaluation metric.
Implementation We introduce TINYLLAMA- 10M as the delta model, which is based on Llama (Touvron et al., 2023) architecture with a parameter size of 110M, featuring 12 Transformer decoder layers and a hidden size of 768. We use LLAMA2-7B 7 as the template LLM to guide the delta model in learning to modify the output of the LLM. LLAMA2-133 LLAMA2-700 and MISTRAL-7B are selected as user LLMs to assess the effectiveness of the delta model on LLMs with different parameter sizes and vocabularies.
Baseline Methods a) LoRA fine-tuning (Hu al. 2022): A parameter-efficient fine-tuning method that freezes the pre-trained model weights during training and incorporates trainable rank decomposition matrices into the Transformer layer reduce the number of trainable parameters. As an immovable method, LoRA is not suitable for direct comparison with our approach but serves performance upper bound for reference. b) Proxy-tuning (Liu et al.) 2024a): A method that does not directly fine-tune the model itself but selects smaller-scale model within the model family as an anti-ext anti-expert, fine-tunes the expert and anti-expert during decoding for larger models. To facilitate fair comparison, we use TINYLLAMA- 10M as the anti-expert, fine-tuned on GPT4-Alpaca the expert. This approach cannot be directly applied to models with different vocabularies.
an expert, and leverages the difference in logits between
Analysis
The results of instruction tuning are shown in Table where we observe that:
single portable delta model can enable L.MSs with different parameter sizes and vocabularies to achieve the ability to follow instructions. Furthermore, this ability is not constrained by the template model's capabilities, and as the user model's capabilities increase, the ability of the user model to follow instructions also increases. This indicates that the logits transformation for enabling a model follow instructions can be applied a wide range of LLMs, which is consistent with the findings in Section[]
Our approach achieves better performance than Proxy-tuning with the same trainable parameters and fewer inference costs, as our delta model can alter the outputs of LLMS through training with the template model, while Proxy-tuning is constrained by the performance of the anti-expert model.
Our method's performance is not as good as LoRA's, as LoRA incorporates new tunable parameters in each Transformer layer, enabling deep interactions between the LoRA module and the model. However, this approach also prevents the LoRA module from being used as a portable neural network for models in different parameter spaces.
4.2 Experiment on Unlearning
Unlearning refers the process of making a model forget specific information from the training data in order to prevent privacy leakage.

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Table 2: Unlearning results on TOFU benchmark. All chat models incorporate the same delta model,
which is trained using the LLAMA2-7B-TOFU as the template model. Better scores are bolded.
Forget Set
Retain Set
Real Author
World Fact
Method
RL (4)
(())
TR())
RL(()
P(T)
TR(1)
RL
P
TR
TR
LLAMA2-7B-TOFU
0.99
0.99
0.51
0.98
0.99
0.48
0.93
0.45
0.58
0.87
0.43
0.56
+LoRA
0.01
0.00
0.77
0.71
0.75
0.48
0.88
0.51
0.68
0.88
0.46
0.60
+CMC (ours)
0.00
0.00
0.33
0.91
0.97
0.51
0.84
0.43
0.58
0.85
0.45
0.57
LLAMA2-13B-TONE
1.00
1,00
0,46
0.99
1.00
0.53
0.89
0.51
0.67
0.86
0.46
0.62
+6- D--UNLEARNING
0.38
0.06
0.53
0.53
0,48
0.52
0.61
0.36
0.46
0.83
0.41
0.59
+LoRA
0.03
0.00
0.50
0.85
0.92
0.52
0.87
0.54
0.70
0.86
0.48
0.63
+CMC (ours)
0.00
0.00
0.29
0.97
0.99
0.55
0.77
0.49
0.65
0.83
0.47
0.65
MISTRAL-7B-TOFE
1.00
1,00
0.49
1.00
1.00
0.48
0.84
0.61
0.75
0.88
0.62
0.78
+LoRA
0.00
0.00
0.72
0.95
0.96
0.49
0.79
0.57
0.71
0.87
0.64
0.78
+CMC (ours)
0.00
0.00
0.30
0.99
0.51
0.62
0.75
0.63
0.77
Evaluation Method We utilized the TOFU benchmark (Maini et al. 2024) to evaluate our approach. The test data consists of four subsets of QA pairs, namely Forget Set, Retain Set, Real Author, and World Fact. The Forget Set and Retain Set contain fictitious author information, with the Forget Set representing the information to be forgotten and the Retain Set representing the information to be retained P Real Author and World Fact are used to test the impact of unlearning on other knowledge within the model. Following TOFU's setting, we employed the following three evaluation metries: ROUGE-L evaluates the matching degree between the output of the tested model and the ground truth answer; Probability assesses the conditional probability of the tested model outputting the correct answer; Truth Ratio calculates ratio that compares the likelihood of its correct answer to an incorrect answer.
Implementation Initially, we trained 2 2--CCCATT LLAMA2-138 1-B2--11--HATTT and MISTRAL- IB-INSTRUCT on the Forget Set and Retain Set of TOFU to memorize fictitious author infor- mation, resulting in LLAMA2-76 7 LLAMA2- 1- 3 and MISTRAL-7B 70--7BB-. We selected TINYLLAMA- 10M as the delta model, with the model learning fictitious author information, LLAMAA-7B-TORE serving as the template LLM,, and LLAMA2- 13B-TOFU and MISTRAI.-71 TOFU serving as user L.MMs. During the training phase, we employed a gradient difference strategy, specifically conducting gradient ascent on the Forget Set and gradient descent on the Retain Set. The loss function can be expressed as:
Laiss ((S)) + L(5n)
(7)
Here, represents the cross-entropy loss, Sp denotes the Forget Set, and Sn denotes the Retain Set.
Baseline methods a) LoRA fine-tuning, which involves training each model separately. b) 5-UNLEARNING (Huang et al. 2024) fine-tunes LLAMA2-7B-CHAT model, and guides LLAMA2- 13B-CHAT during training and decoding to avoid outputting private information using the changes in logits between the tuned and the original LLAMA2-7B-CHHE This method necessitates inferring three models during the inference, incurring substantial computational costs.
Analysis The results are shown in Table 2 The delta model trained with LLAMA2- 1--MAA----TOFF
enables models with varying parameter scales and vocabularies to achieve unlearning effects. Its performance is comparable to LoRA while offering the capability of improving multiple models in single training session. Furthermore, compared to Ó-UNLEARNING, our approach achieves superior performance at less than half the inference cost.
4.3 Impact of Parameter Size of Delta Model Performance
We experimented with delta models of smaller parameter sizes, specifically the AAYYLLAM-42MM and TINYLLAMA- 1 15 models, and tested their performance on the first 50 data points of AlpacaEval.
'The test data for Forget Set and Retain Set are paraphrased and perturbed training data, serving as the ground truth answer and incorrect answer, respectively.
7

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Table 3:
Different delta model size first 50 data points of AlpacaEval (win %).
Delta Model Size
LLAMA2-7B
LLAMA2-13B
LLAMA2-70B
MISTRAL-7B
ISM
40
50
72
52
42M
54
64
82
50
110M
54
66
74
54
The results are presented in T | In most cases, we find that reducing the delta model's parameter size leads decrease performance, but the performance does rapidly decline the parameter size decreases. When the delta model is applied to LLAMA2-70B, a larger delta model size may have negative effect. We believe this is because the 70B LLM, with a massive parameter size, does not need to make significant adjustments to the output to acquire the ability to follow instructions, and overly large parameter size in the delta model may lead overfitting, This indicates that delta model used for adjusting LLM output logits does not necessarily require large number of parameters. also demonstrates the significant potential for small models assist large models.
4.4
Impact of Strength Coefficient on Performance
60
AlpacaEval Rate
0.8
50
ROUGE Score
40
30
Real Authors
0.4
Real World
20
epochs
Retain
10
epachs
0.2
Forget
epochs
0.50
0.75
1.00
1.25 Alpha
1.50
1.75
0.0
0.5
1.0
1.5 Alpha
2.5
3.0
(a) Impact on instruction tuning
(b) Impact on unlearning
Figure 4: Impact of strength coefficient on performance
We experimented with different strength coefficients and observed their impact on performance.
For instruction tuning, we tested values within the range of [0.5, 2] and evaluated them on the first 50 data points of Alpa As shown in Figure 4a we found that the performance was optimal when the a value was 1.0, and increasing or decreasing a resulted
decreased performance. This is similar to causing the delta model to overfit or underfit, so we explored whether adjusting the value during inference could counteract the overfitting and underfitting during training. Specifically, the delta model performed best after training for four epochs, and we selected the checkpoint after training for two epochs as the underfitting scenario and the checkpoint after training for eight epochs as the overfitting scenario. We found that adjusting the value could counteract the overfitting or underfitting during training. The underfitting delta model had win rate 1.0, which increased to 64 when was 2.0. Similarly, the overfitting delta model had win rate of 48 when a was 1.0, which increased to 64 when a was 0.75. This phenomenon indicates that our training method offers a considerable degree of fault tolerance, even if incorrect epoch parameters are set during the training phase, is still possible to achieve the desired performance by adjusting the strength coefficient a.
when a was
For unlearning, we found that adjusting the value can serve as a balance between forgetting and retaining. As shown in Figure 4b) increasing the value of can prevent the model from outputting more sensitive information, but may also lead the loss necessary information. Conversely, decreasing the value of a can allow the model to retain more essential information, but it may result in the model outputting more sensitive information. Users of L.M can flexibly adjust the value based on the actual cireumstances.

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4.5 Ablation Study
In the ablation study, we examined the impact of not applying LogSoftmax to the logits output by the LLM and the effect of omitting prefix match- ing during token mapping on performance. As demonstrated Table 4 our findings reveal that the removal of LogSoftmax results in decline in performance. This suggests that projecting the log- its output by the LLM into the logarithmic space can reduce the gap between training and inference. Similarly, the absence of prefix matching in token mapping diminishes the supportive effect of the delta model on the LLM. This indicates that prefix matching has a higher priority than selecting tokens with the minimal edit distance. Eliminating prefix matching could lead to the aggregation of token logits that are semantically unrelated.
Table 4:
Ablation study
AlpacaEval (Win %)
Method
LLAMA2-7B+ddlla model w/o LogSoftmax
30.41
28.64
LLAMA2- LLAMA2-13B+dad 13B+delta model w/o LogSoftmax
39,04
36.85
MISTRAI-7B+ddlt model
33.29
w/o LogSoftmax
31.22
w/o Prefix Match
30.19
Related Work
In this paper, we focus on modifying the outputs of the base language model. The related work can be broadly categorized into two approaches: training-intensive methods and decoding-tine methods.
Training-Intennn methods One of the most effective methods for adapting large language models (LLMs) specific downstream tasks involves updating only subset of the model parameters, rather than the entire set. For instance, LoRA (Hu et al., 2022) achieves parameter updates by decomposing them into trainable low-rank vectors. Prefix-tuning (Li and Liang 202T) introduces series of continuous, task-specific vectors at the beginning of the input sequence for task adaptation. Adapter- tuning (Houlsby et al., (2199) integrates compact, trainable modules within the layers of a pre- traitre model to facilitate transfer learning. BitFit (Zaken et al.) [202I) selectively updates individual bias vectors in the model's parameters. Although these methods enhance performance in downstream tasks, they still require significant computational resources.
Decoding-Tinee methods To reduce training costs, some researchers have explored decoding-time methods. For example, DExperts (Liu et al., 2021) enhances or suppresses certain text attributes by integrating expert and anti-expert models during decoding. GeDi (Krause et al., 2021) employs smaller LMs as generative discriminators to steer the output of larger LMs, enhancing safety and control. Proxy-tuning (Liu et al., (2024a) modifies the predictions of an untuned larger model towards desired outcome. ITI (Li et al. (2024) adjusts activation values during inference to generate more accurate responses. EFT (Mitchell et al. 2023) employs two smaller LMs to emulate the behavior of larger LM without the need for additional training. Although these methods are effective, they often require the use of two additional language models during inference, increasing inference costs.
6 Conclusion and Limitation Discussion
In this paper, we introduced training method named CMC, designed to improves multiple LLMs in one-time training, enabling LLM owners who lack data and computational resources to improve their models at lower cost. Through comparative analysis of the logits shifts in different LLMs before and after fine-tuning, we observed a similarity in these shifts. Based on this observation, we introduced portable tiny delta model to fit the logits shifts of LLMs, enabling the adjustment of outputs for LLMs of varying sizes and vocabularies. Our experiments instruction tuning and unlearning tasks have demonstrated the effectiveness of our method.
However, our approach has certain limitations. The performance of the delta model is constrained by the scope of its vocabulary. If the vocabulary of the user's L.M contains tokens from languages not covered by the delta model's 's vocabulary, then the logits of these linguistically diverse tokens will not be adjusted accordingly.

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Acknowledgement
This work was supported by the National Natural Science Foundation of China under Grant No. 62377012 and the Special Fund for International Conferences of Graduate Students at East China Normal University.
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A Hyperparameters and experimental details
A.1 Instruction Tuning
During the training of delta model, we set the learning rate to 2e-4, batch size to 64, and trained for 4 epochs.
For LoRA fine-tuning, for both LLAMA2-7B and MISTRAL--BB models, 280, while for LLAMA2-12 1B, is set to 90 and alpha to 180. For all models, the learning rate is 2c-5, batch size is 32, LoRA target are (g_proj..__pp_p proikk.oo..kkkk_.o..... proj]. and the training lasted for epochs.
to 140 and alpha to
When training the expert models for Proxy-tuning, the learning rate was set to 2e-4, batch size to 64, and trained for 16 epochs. We also experimented with varying the number of training epochs and found that performance was best with 16 epochs.
A.2 Unlearning
In training the delta model, we set the learning rate to le-4, batch size to 16, and trained for 20 epochs.
For LoRA fine-tuning, across all models, was set to 32, alpha to 64, with learning rate of le-4, batch size 16, and training spanned epochs.
A.3 Preliminaries
The hyperparameter settings for Section 2 were consistent with those outlined for instruction tuning.
B Compute Resources
Our experiments were conducted on a server equipped with 512GB of memory and 4 Nvidia A100 40G GPUs
C Broader Impacts
Our approach can bring about positive social impacts. Specifically, our method allows for the reuse of the fine-tuning outcomes from one model to another. This attribute of supporting repeated use can reduce the cost of model training and decrease carbon emissions. Simultaneously, our method does not present any negative social impacts.
D Quantitative Analysis of the Fine-tuning Effect
By calculating the difference between the logits after and before fine-tuning, we obtain the fine-tuning effects, and we evaluated their similarity by measuring the distance between them, which is assessed using Sinhorn divergence.
Distance
Sinkhom Divergence(TMin TMM
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Table 5: Average Distance between logits shifts. Smaller distances
similarities
Average Distance Between Logits Shifts
Model1
Model2
Both train on GPT4-Alpaca
Modell train on GPT4-Alpaca and Model2 train on GSM8k
LLAMA2-136
11LAMA2-7E
0.658
3.522
MISTRAL-7B
LLAMA2-7B
1.046
4,760
MISTRAL-7B
L1AMA2-13R
0.754
3.382
We fine-tuned them individually the GPT4-Alpaca dataset. Additionally, to contrast the fine-tuning effects with other task, we also fine-tuned them on the GSM8K dataset (Cobbe et al., 2021). We selected the first 50 data from AlpacaEval inputs, and used the output of M the response to form input-response pairs. We chose the average Sinkhorn divergence as the indicator. The result is shown in Table We observed that despite the models having different parameter scales and vocabularies, training on the same dataset still resulted similar logits shifts. Conversely, significant differences observed when training on different datasets.
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Reviews:
[{"review_id": "W78IQLJH6Z", "reviewer": "Reviewer_VCcy", "summary": "The paper introduces Cross-model Control (CMC), a novel method to improve multiple large language models (LLMs) in a single training session using a portable tiny language model. The core idea hinges on the observation that the logit shifts before and after fine-tuning are similar across different models. Leveraging this, CMC trains a tiny model alongside a frozen template LLM to adjust the logits output by LLMs. The paper also proposes a token mapping strategy, PM-MinED, to make the tiny model applicable to models with different vocabularies. Extensive experiments on instruction tuning and unlearning tasks demonstrate the effectiveness of CMC. The paper provides the code for their implementation.", "questions": "Token Mapping Generalizability: Can you provide more details on how PM-MinED performs with highly specialized or domain-specific vocabularies? Have you tested the method on such datasets?\n\nHandling Overfitting: Beyond adjusting the model size and strength coefficient, are there other strategies you considered to mitigate the overfitting of the tiny model when applied to larger LLMs?\n\nError Analysis: Can you provide more detailed error analysis or failure cases where CMC underperforms compared to other methods? This would help in understanding the limitations and potential areas for improvement.", "limitations": "The authors have addressed the limitations of their work adequately, highlighting the constraints related to the scope of the tiny model's vocabulary and its impact on performance. They also discuss the potential for overfitting when applying the tiny model to very large LLMs and the need to adjust model parameters carefully.", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Originality: The paper introduces a novel approach to improving multiple LLMs simultaneously, which addresses a significant challenge in the field. The use of a portable tiny model to adjust logits across different models is innovative.\n\nQuality: The method is thoroughly tested on two important tasks—instruction tuning and unlearning. The experiments are well-designed and demonstrate the effectiveness of the approach. The analysis of logit shifts and the detailed comparison with baseline methods like LoRA and Proxy-tuning add to the robustness of the study.\n\nClarity: The paper is well-written with clear explanations of the proposed method, experimental setup, and results. The figures and tables effectively illustrate the findings, making the concepts easier to grasp.\n\nSignificance: The proposed method has substantial implications for the efficient fine-tuning of LLMs, especially for model owners with limited data and computational resources. The ability to apply fine-tuning outcomes across different models can lead to significant cost reductions and broader applicability of advanced LLMs.", "weaknesses": "Token Mapping Strategy: While PM-MinED is introduced to handle different vocabularies, its effectiveness might be limited when dealing with highly diverse or specialized vocabularies not covered in the training data. This could impact the generalizability of the method.\n\nPerformance on Larger Models: The performance of the tiny model on extremely large LLMs (70B llama 2 for example) can sometimes lead to overfitting, as indicated in Section 4.3. The paper acknowledges this but does not provide a concrete solution beyond adjusting the model size and strength coefficient.\n\nLimited Error Analysis: The paper lacks a detailed error analysis, particularly concerning the cases where the method fails or underperforms compared to other approaches like LoRA. A deeper investigation into these scenarios could provide valuable insights for future improvements.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We deeply appreciate your encouraging feedback regarding the originality and significance of our work. We are pleased to address your inquiries in detail as follows:\n\n**Q1: While PM-MinED is introduced to handle different vocabularies, its effectiveness might be limited when dealing with highly diverse or specialized vocabularies not covered in the training data. This could impact the generalizability of the method. Can you provide more details on how PM-MinED performs with highly specialized or domain-specific vocabularies? Have you tested the method on such datasets?**  \nA1: Thanks for your insightful comments. PM-MinED is a training-free method that does not require training data. It conducts tokenizer alignment between differnt vocabularies via prefix matching and edit distance minimization. As shown in the tables below, in our experiments, there are 30% of the tokens in the mistral vocabulary that are absent from the llama vocabulary, yet PM-MinED still demonstrates good performance. We'd like to provide more details regarding the effectiveness of PM-MinED across various vocabularies in the updated version.\n* Experiment on Instruction Tuning\n    |||\n    |-|:-:|\n    |**Model**|**AlpacaEval (Win %)**|\n    |Mistral-7B **(Mistral Vocab)**|6.83|\n    |Mistral-7B + Delta Model **(Llama Vocab)**|33.29|\n    |\n* Experiment on Unlearning\n    ||||||\n    |-|:-:|:-:|:-:|:-:|\n    |**Model**|**Forget Set Prabability**|**Retrain Set Prabability**|**Real Author Prabability**|**Word Fact Prabability**|\n    |Mistral-7B-TOFU **(Mistral Vocab)**|1.00|1.00|0.61|0.62|\n    |Mistral-7B-TOFU+Delta Model **(Llama Vocab)**|0.00 **(lower is better)**|0.99|0.62|0.63|\n    |\n\n**Q2: Handling Overfitting: Beyond adjusting the model size and strength coefficient, are there other strategies you considered to mitigate the overfitting of the tiny model when applied to larger LLMs?**  \nA2: Thanks for your careful reading. In the experiments of investigating the impact of delta model size on performance, we observe that using a larger delta model for extremely large user LLMs still yields good performance, but compared to a smaller delta model, there may be a slight overfitting-like phenomenon. To enhance the robustness of a larger delta model when applied to extremely large user LLMs, besides adjusting the delta model size, we can reduce the learning rate during training and make adjustments to the strength coefficient alpha (default value of 1.0) during the inference stage. Specific examples are shown in the table below, where the delta model is trained based on the Llama2-7b template model. By slightly lowering the learning rate from 2e-4 to 1.5e-4, the win rate of Llama2-70b with the delta model increases from 74% to 80%. And the performance on smaller user LLMs (e.g., Llama2-13b) can also be maintained via altering the alpha value (1.00 --> 1.05).\n||||\n|--|:--:|:--:|\n|**Delta Model Learning Rate**|**AlpacaEval (Win %)**|**AlpacaEval (Win %)**|\n||Llama2-70b+Delta Model|Llama2-13b+Delta Model|\n|2e-4|74 (alpha=1.00)|64 (alpha=1.00)|\n|1.5e-4|80 (alpha=1.00)|64 (alpha=1.05)|\n|\n\n**Q3: The paper lacks a detailed error analysis, particularly concerning the cases where the method fails or underperforms compared to other approaches like LoRA. A deeper investigation into these scenarios could provide valuable insights for future improvements. Can you provide more detailed error analysis or failure cases where CMC underperforms compared to other methods? This would help in understanding the limitations and potential areas for improvement.**  \nA3: Thank you for your suggestion. We have collected some failure cases and will incorporate them into the revision. In some cases, LoRA outperforms CMC because it introduces new parameters at each layer, resulting in deeper interactions. However, these new parameters are strongly tied to the model parameters, bring the LoRA module an immovable characteristic that limits its use in models with different model structures. On the other hand, the delta model, as a portable neural network, can be reused across different models, enabling the improvement of multiple large language models in a single training session.", "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": "PdNbGrWFHs", "reviewer": "Reviewer_FZv7", "summary": "This paper investigates an interesting problem in fine-tuning LLMs: how to reuse the fine-tuning outcomes of one model for other models to reduce training costs. The authors provide an important empirical finding: different models exhibit highly similar logit shifts before and after the same fine-tuning process. Based on this, the authors propose Cross-model Control (CMC), a method that improves multiple LLMs in a single training session using a portable tiny language model. Specifically, the authors integrate a tiny language model with minimal parameters, training it alongside a fixed template LLM. This tiny model gains the ability to alter the output logits of the LLM, making it applicable to other models. Experiments demonstrates its effectiveness.", "questions": "See \"Weaknesses\".", "limitations": "The authors provide a discussion on limitations.", "rating": 7, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. How to improve multiple LLMs in one-time finetuning is really an interesting topic for me.\n2. The method is well-motivated, reasonable, and experimentally validated.\n3. The code has been provided, which is excellent.", "weaknesses": "1. As the authors discuss in the limitations section, if the user’s LLM vocabulary includes symbols from languages not covered by the delta model’s vocabulary, the logits for these different language symbols will not be adjusted accordingly. While I don’t expect the authors to overcome this limitation, it does imply an underlying assumption in the proposed method. I would like the authors to explicitly state this assumption in the methods section.\n\n2. Because the findings in Figure 2 are crucial for proposing the method, I would like to see not only the qualitative analysis provided in Figure 2 but also a quantitative analysis. This should include the similarity of logits shifts for different models fine-tuned on the same dataset, as well as the similarity of logits shifts for the same and different models fine-tuned on different datasets.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate your recognition of our work and the valuable suggestions. We would like to address each of the questions as follows:\n\n**Q1: Because the findings in Figure 2 are crucial for proposing the method, I would like to see not only the qualitative analysis provided in Figure 2 but also a quantitative analysis. This should include the similarity of logits shifts for different models fine-tuned on the same dataset, as well as the similarity of logits shifts for the same and different models fine-tuned on different datasets.**   \nA1: Thank you for your suggestion. As described in line 103 of the paper, the quantitative analysis is presented in Appendix D. We evaluated the fine-tuning effect similarity across different models by measuring the distance between them, which is assessed using Sinhorn divergence. A smaller distance indicates a higher level of similarity.\n\n$$\\text{Distance} =\\text{Sinkhorn Divergence}(T_{M1},T_{M2})/ |V_1|$$\n\nHere, $T_{M1}$ denotes the logits shifts of model1, $T_{M2}$ ​denotes the logits shifts of model2 after vocabulary mapping, and $|V_1|$ denotes the vocabulary size of model1.  \nWe fine-tuned the models individually on the GPT4-Alpaca dataset. In addition, to compare the fine-tuning effects with other tasks, we also fine-tuned them on the GSM8k dataset. The results are shown in the table below. We observe that despite the models having different parameter scales and vocabularies, training on the same dataset still result in similar logits shifts. Conversely, if the models are trained on datasets from different tasks, they will acquire distinct capabilities, which consequently results in substantial variations in their logits shifts.\n|||||\n-|:-:|:-:|:-:\n**Models**||**Average Distance Between Logits Shifts**||\nModel1|Model2|Both train on GPT4-Alpaca|Model1 train on GPT4-Alpaca and Model2 train on GSM8k\nLlama2-13b|Llama2-7b|0.658|3.522\nMistral-7B|Llama2-7b|1.046|4.760\nMistral-7B|Llama2-13b|0.754|3.382\n|\n\n**Q2: As the authors discuss in the limitations section, if the user’s LLM vocabulary includes symbols from languages not covered by the delta model’s vocabulary, the logits for these different language symbols will not be adjusted accordingly. While I don’t expect the authors to overcome this limitation, it does imply an underlying assumption in the proposed method. I would like the authors to explicitly state this assumption in the methods section.**  \nA2: Thank you for your suggestion. We will incorporate this assumption into the method section. Despite this premise, within the language scope covered by the delta model's vocabulary, the delta model is able to deliver the capabilities across various LLMs in a portable manner. This marks the first successful implementation of improving multiple large language models in a single training session.", "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": "KK9iZBg2g1", "reviewer": "Reviewer_VAGR", "summary": "This paper proposes a novel training method named CMC, designed to improve performance for multiple LLMs in one-time training, thereby reducing training costs by reusing fine-tuning outcomes. The core approach of the paper consists of three steps:\n1. Introducing a portable tiny language model (delta model) with a small number of parameters, trained alongside the frozen template LLM, to enable the delta model to alter the output logits of the LLM.\n2. Introducing a novel token mapping method called PM-MinED, which aligns the vocabularies of the delta model and the user LLM.\n3. Sharing the delta model with other user LLMs to simulate the effect of fine-tuning.\nThe author conducts extensive experiments on instruction tuning and unlearning tasks to demonstrate the effectiveness of the proposed method. Additionally, the paper highlights the significant potential of small language models in assisting large language models.", "questions": "1. As mentioned in the weaknesses, during the inference stage, if the user LLM generates a token that does not exist in the delta model's vocabulary, what is the input to the delta model when predicting the next token?\n2. In the preliminary section, the author indicates that shifts in logits across different models are similar. Can we use a delta model to directly fit the logits shifts of existing models, such as fitting the logits shifts of Llama2-7b-chat and Llama2-7b-base? Will token-level logits shifts provide more fine-grained supervision signals, leading to better performance?", "limitations": "The authors have adequately addressed the limitations and risks of their work.", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "1. This paper is well-motivated. The author identifies the pain points of existing model optimization methods that are not portable, and provides a detailed analysis of the fine-tuning similarity between different models, effectively leading to the core method.\n2. CMC is simple yet effective. The delta model serves as a parameter module that can be ported to models with different parameter scales and vocabularies, still achieves impressive performance. Its performance significantly surpasses baseline methods that can only be ported within the model family, and it achieves similar performance to the non-portable method LoRA in the unlearning task. \n3. CMC has strong generalization, demonstrating good performance when applying delta models of different parameter scales to user LLMs with different parameter scales and vocabularies. It also exhibits good robustness, being able to effectively mitigate overfitting and underfitting phenomena through the adjustment of the strength coefficient.\n4. This paper showcases the great potential of small models in assisting super large models, which is quite exciting!\n5. The author has open-sourced the complete implementation process, which is easy to reproduce.", "weaknesses": "There are no apparent weaknesses, just some minor concerns. \n1. In the vocabulary of language models, in addition to tokens that make up most of the words, there are still many special tokens. Mapping between special tokens from different vocabularies may require a significant amount of manual annotation.\n2. The author needs to clarify how to represent a token that does not exist in the delta model's vocabulary when the user LLM generates it during inference. I'm concerned that if the token is directly encoded using the delta model's tokenizer, it may not adequately represent the information of that token.\n3. The author would do well to include the pseudocode of the algorithm in the paper, which would clearly demonstrate the details of the inference part.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We deeply value the time and effort you've invested in offering us encouraging feedback. In response, we are endeavoring to address your points as follows:\n\n**Q1：During the inference stage, if the user LLM generates a token that does not exist in the delta model's vocabulary, what is the input to the delta model when predicting the next token?**  \nA1: Thanks for your insightful comments. When preparing inputs for the delta model, if the tokens generated in the previous step are not in the vocabulary of the delta model, we will not directly encode this single token using the delta model's tokenizer. Instead, we will first decode the input from the previous time step into natural language using the tokenizer of the user LLM, then we concatenate the newly generated token with the decoded sequence, and finally we re-encode the concatenated sequence using the tokenizer of the delta model. This approach helps to avoid directly encoding single tokens that are not in the vocabulary. Additionally, the tokenizer has a high encoding efficiency. Thus, there is almost no increase in the inference cost caused by this re-encoding process. We'd like to incorporate these details into the revision.\n\n**Q2: In the preliminary section, the author indicates that shifts in logits across different models are similar. Can we use a delta model to directly fit the logits shifts of existing models, such as fitting the logits shifts of Llama2-7b-chat and Llama2-7b-base? Will token-level logits shifts provide more fine-grained supervision signals, leading to better performance?**  \nA2: Thank you for your new insights. In the early experimental phase, we have tried this method and attempted to measure the differences between the logits of the delta model and the logits shifts of Llama2-7b using methods such as KL divergence and Sinkhorn divergence. However, the loss during the training phase did not converge, and the evaluation results were also poor. We speculate that this may be due to the delta model's inability to handle such fine-grained information effectively.\n\n**Q3: In the vocabulary of language models, in addition to tokens that make up most of the words, there are still many special tokens. Mapping between special tokens from different vocabularies may require a significant amount of manual annotation.**  \nA3: Thanks for your insightful comments. The special tokens that require manual alignment mainly include partial hexadecimal numbers, start symbols, end symbols, and line breaks. Their alignment is not complex, and the manual annotations are worthwhile for reusing the fine-tuning outcomes.\n\n**Q4: The author would do well to include the pseudocode of the algorithm in the paper, which would clearly demonstrate the details of the inference part.**  \nA4: Thanks for your insightful suggestion. We will add a brief algorithmic description with more succinct mathematical notations to make the method more clear.", "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": "L5lepZ8tHs", "reviewer": "Reviewer_V2UG", "summary": "Large language models face a series of common optimization requirements under specific applications or ethical standards. Existing methods only optimize one target model at a time, which requires changing model parameters or adding new parameters. Authors found that the logit changes of different models before and after fine-tuning are very similar. Based on this finding, the CMC method introduces a small language model with a very small number of parameters, and trains it together with a frozen template LLM to enable the small model to learn to change the output logit of LLMs. In order to make the delta model suitable for models with different vocabularies, the paper proposes a novel token mapping strategy called PM-MinED, which is a lightweight method for aligning the vocabulary of user LLM and delta model at the token level.", "questions": "See weaknesses.", "limitations": "See weaknesses.", "rating": 5, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The proposed method is novel and interesting. Unifying different training goals into one tiny model is interesting.\n2. The proposed method has practical usage. It has significant potential to solve the problem of fintuning LLMs to fit different goals, especially for those users without enough resources.\n3. The source code is given for reproducibility.\n4. The experiment results show significant improvements of the proposed method.", "weaknesses": "1. Authors should provide more details about the hyper-parameter settings. Do the hyper-parameter is chosen as a suitable value with respect to different methods? The delta model is training with longer epochs than LoRA, which may make the delta model learn more than LoRA.\n2. Figure 2 is not clear to me. It seems that the logits are sorted. So, how does this figure illustrate the similarity?\n3. The Figure 3 is somehow misleading. Are output logits both fed into the user LLM and the Delta Model, or the raw training inputs are fed into the user LLM and the Delta Model?\n4. It is under-explored that whether the Delta model harms the original ability of user LLMs.\n5. The hardware configuration required to train and inference with the delta model is not provided.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate your valuable comments and feedback. We will describe more details of the experiments if more pages are provided.\n\n**Q1: \"more hyper-parameter details\" & \"impacts of training epochs\"**  \nA1: **We selected the hyperparameters that achieve the best performance for the different methods.** In the instruction tuning experiment, LoRA and delta model performed best after 2 and 4 epochs respectively. Similarly, in the unlearning experiment, they performed best after 5 and 20 epochs respectively. The results of training LoRA for more epochs are shown in the table below. We observe that **training with longer epochs do not guarantee obtaining more or better performance**. One possible reason is that training for more epochs may cause the model to forget essential knowledge that should be remembered.\n- Training Mistral-7B with LoRA on the GPT4-Alpaca dataset and evaluating with the first 50 data points from AlpacaEval.\n|||\n-|:-:\n**Training Epochs**|**AlpacaEval (win %)**\n1.00|94.00\n**2.00**|**96.00**\n3.00|94.00\n4.00|92.00\n|\n- Training and evaluating Mistral-7B-TOFU with LoRA on the TOFU benchmark.\n||||||||\n-|:-:|:-:|:-:|:-:|:-:|:-:\n**Training Epochs**|**ROUGE Real Authors**|**Prob. Real Authors**|**Truth Ratio Real Authors**|**ROUGE Real World**|**Prob. Real World**|**Truth Ratio Real World**\n**5**|**0.79**|**0.57**|**0.71**|**0.87**|**0.64**|**0.78**\n10|0.74|0.56|0.69|0.87|0.62|0.76\n20|0.74|0.52|0.66|0.86|0.60|0.73\n|\n\nWe'd like to provide more details in the supplementary material and updated version.\n\n**Q2: How does Figure 2 illustrate the similarity?**  \nA2: As described in lines 97 to 100 of our paper, **in Figure 2, only the figure above is sorted according to the token logits shifts values. The figure below is reordered based on the token order of the figure above**. In other words, the tokens in the corresponding positions in the figures above and below are matched, and this matching is based on the PM-MinED strategy. This sorting is designed to facilitate the comparison of the similarities between the two figures. If the trends of color change from left to right in the figures above and below are similar, it indicates that the token logits shifts in the models of the two figures are similar, which suggests that the fine-tuning effects in the models of the two figures are similar. We will clarify it more clearly in the next version.\n\n**Q3: The input of the user LLM, template LLM and the delta model**  \nA3: The training stage is represented by Figure 3a, where raw training inputs and responses are encoded by the tokenizer and fed to the template LLM and delta model. The inference stage is shown in Figure 3b, where the prompt and newly generated tokens are concatenated and then encoded separately by the user LLM and delta model's tokenizer before being fed to the user LLM and delta model, respectively. The output logits are not fed to any model. Instead, the output logits of the user model and delta model are added together to serve as the final logits for decoding and generating new tokens. We appreciate your thorough review and will include these details in the final version of our paper.\n\n**Q4: Whether the delta model harms the user LLM's original ability?**  \nA4: In the experiments of unlearning and detoxifying, we explored whether the delta model would harm the original ability of user LLMs.\n- Exploration in the Unlearning Experiment  \nAs indicated in line 218 of the paper, \"Real Author\" and \"Real World\" represent the model's original knowledge. The impact of the delta model on these knowledge is presented in Table 2 of the paper, and we present the results again in the table below. Similar to LoRA, the delta model has a minimal impact on the model's original knowledge. Moreover, our proposed method achieved unlearning for multiple models in a single training session.\n||||||||\n-|:-:|:-:|:-:|:-:|:-:|:-:\n**Method**|**ROUGE Real Authors**|**Prob. Real Authors**|**Truth Ratio Real Authors**|**ROUGE Real World**|**Prob. Real World**|**Truth Ratio Real World**\nMistral-7B-TOFU|0.84|0.61|0.75|0.88|0.62|0.78\n+LoRA|0.79|0.57|0.71|0.87|0.64|0.78\n+CMC (ours)|0.73|0.62|0.75|0.86|0.63|0.77\n|\n* Exploration in the Detoxifying Experiment (New)  \nWe further explored whether the delta model would harm the original abilities of the user LLM in detoxifying tasks. We utilized HarmfulQA, a dataset comprising 1960 harmful questions covering 10 topics, and generated non-toxic responses using gpt-3.5-turbo to form the training dataset. We trained Nous-Hermes-Llama2-13b with LoRA as the baseline method, using Nous-Hermes-Llama2-7b as the template LLM to train the delta model. We evaluated the detoxification effects on the DangerousQA and AdversarialQA datasets and evaluated whether the model's original ability was compromised on the TruthfulQA dataset.  \nThe results, as shown in the table below, indicate that the delta model significantly reduced the likelihood of the model producing toxic outputs. Furthermore, similar to LoRA, **the delta model preserved the original ability of user LLM well**. We are committed to including related dicussion in the next version of our paper.\n|||||\n-|:-:|:-:|:-:\n**Method**|**DangerousQA (toxic %)**|**AdversarialQA (toxic %)**|**TruthfulQA MC (True %)**\nNous-Hermes-Llama2-13b|0.445|0.897|0.430\n+LoRA|0.035|0.485|0.400\n+CMC (ours)|0.087|0.389|0.406\n|\n\n**Q5: Hardware configuration.**  \nA5: As shown in the table below, the computational overhead associated with training and inference for the delta model is relatively small. Due to the small number of parameters of the delta model, the hardware requirements for inference with the delta model are approximately identical to inference the user LLM only.\n|||\n-|:-:\n**Model**|**Hardware Configuration**\nTrain Stage|\n7B/13B Models with LoRA|1 A100 40G GPU\nDelta Model 15M/42M/110M with Llama2-7b|1 A100 40G GPU\nInference Stage|\n7B/13B Models with LoRA/Delta Model|1 A100 40G GPU\nLlama2-70b with Delta Model|4 A100 40G GPUs\n|", "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": "1GXfDUEKQD", "reviewer": "Reviewer_aFGW", "summary": "The development of the Cross-model Control method, which enables the use of fine-tuning outcomes from one model to improve other models through a portable tiny language model, effectively reducing training costs and computational resources.\nThe introduction of a novel token mapping strategy called prefix match with minimal edit distance (PM-MinED) to adapt the tiny model to different vocabularies of target LLMs.\nExtensive experimental validation showing the effectiveness of CMC in common LLM optimization tasks like instruction tuning and unlearning, demonstrating substantial improvements over existing methods.\nThis approach represents a significant advancement in the field of LLM optimization, offering a scalable and resource-efficient solution for enhancing multiple models simultaneously.", "questions": "Generalizability Across Diverse Models:\nHow well does the Cross-model Control (CMC) method generalize to models with architectures or training procedures significantly different from those used in your experiments? For instance, how would CMC perform with emerging architectures like Transformer-XL or models trained with novel objectives?", "limitations": "The authors of the paper \"Cross-model Control: Improving Multiple Large Language Models in One-time Training\" have made an effort to address the limitations of their work, as well as discussing the broader societal impacts. However, there are areas where the discussion could be enhanced to provide a more comprehensive overview:\nScalability and Efficiency: The computational requirements and potential bottlenecks of implementing the PM-MinED strategy, especially across models with large and diverse vocabularies, are not thoroughly explored.\nBias and Fairness: There is a lack of discussion on how the method might influence or propagate biases within the trained models. Given the potential for large language models to perpetuate or amplify biases, this is a critical area that needs addressing.\nRobustness and Error Analysis: The paper does not provide statistical robustness measures such as confidence intervals or error bars, which are essential for validating the reliability of the results.\n\nSuggestions for Improvement\nExpand on Computational Challenges: Include a detailed analysis of the computational efficiency and scalability of the PM-MinED strategy. Discuss potential strategies to optimize this process for larger-scale applications.", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The significance of this work lies in its potential impact on the practical deployment of LLMs. By enabling one-time, cost-effective optimization of multiple models, CMC addresses a critical bottleneck in the application of LLMs—resource intensity. This is particularly relevant in scenarios where computational resources are limited or where frequent re-training of models is not feasible. Additionally, the introduction of the PM-MinED strategy for token mapping broadens the applicability of the tiny delta model to diverse model architectures and vocabularies, thereby enhancing the utility of the proposed method in real-world applications.", "weaknesses": "The effectiveness of the Cross-model Control hinges significantly on the observed similarity in logit shifts across different models post-fine-tuning. This assumption may not hold in situations where models diverge significantly in architecture or training data, potentially limiting the applicability of the method in more diverse or adversarial settings.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We deeply value the time and effort you've invested in offering us constructive feedback. In response, we are endeavoring to address your points as follows:\n\n**Q1: Scalability and Efficiency: The computational requirements and potential bottlenecks of implementing the PM-MinED strategy, especially across models with large and diverse vocabularies, are not thoroughly explored.**  \nA1: Thanks for your insightful comments. \n* Regarding the efficiency of PM-MinED:  \nThe PM-MinED strategy is highly efficient, with a time complexity of O(nm), where n is the size of the delta model's vocabulary, and m is the size of the user model's vocabulary.  \nIn our experiments, using an Intel Xeon Gold 6148 CPU, aligning the Llama and Mistral vocabularies (32000 tokens) using a single thread takes only about 3 minutes. Scaling up to vocabularies in the hundreds of thousands takes only a few tens of minutes. With multi-thread optimization, the calculation time can be reduced to just a few tens of seconds, significantly less than the time required to train LLMs. We will update the multi-thread implementation of PM-MinED in the code repository.  \nIt is worth noting that vocabulary mapping is an independent step prior to model inference and does not affect the model's inference time. Additionally, once vocabulary mapping is completed, it can be applied to different delta model models with the same vocabulary, making it a one-time process. \n* Regarding the scalability of PM-MinED:  \nThe PM-MinED strategy contains both prefix matching and minimum edit distance matching, which better guarantees the accuracy of token matching. In our experiments, there are 30% of the tokens in the mistral vocabulary that are absent from the llama vocabulary, yet PM-MinED still demonstrates good performance.\n    * Experiment on Instruction Tuning\n        |||\n        |-|:-:|\n        |**Model**|**AlpacaEval (Win %)**|\n        |Mistral-7B **(Mistral Vocab)**|6.83|\n        |Mistral-7B + Delta Model **(Llama Vocab)**|33.29|\n        |\n    * Experiment on Unlearning\n        ||||||\n        |-|:-:|:-:|:-:|:-:|\n        |**Model**|**Forget Set Prabability**| **Retrain Set Prabability**| **Real Author Prabability** | **Word Fact Prabability** |\n        |Mistral-7B-TOFU **(Mistral Vocab)**|1.00|1.00|0.61|0.62|\n        |Mistral-7B-TOFU+Delta Model **(Llama Vocab)**|0.00 **(lower is better)**|0.99|0.62|0.63|\n        |\n\n    We will explore the matching between vocabularies with greater differences in our future work.\n\n**Q2: Generalizability Across Diverse Models: How well does the Cross-model Control (CMC) method generalize to models with architectures or training procedures significantly different from those used in your experiments? For instance, how would CMC perform with emerging architectures like Transformer-XL or models trained with novel objectives?**  \nA2: Thanks for your insightful comments. As the current mainstream LLMs adopt similar Transformer architectures and pre-training objectives[1], our work primarily focuses on the transfer of fine-tuning outcomes among transformer-based LLMs. We plan to explore the transfer of fine-tuning outcomes between more model architectures (such as Mamba and RWKV) in future work.  \n\nReferences:  \n[1] Zhao, Wayne Xin, et al. \"A survey of large language models.\" arXiv preprint arXiv:2303.18223.\n\n**Q3: There is a lack of discussion on how the method might influence or propagate biases within the trained models. Given the potential for large language models to perpetuate or amplify biases, this is a critical area that needs addressing.**  \nA3: Thanks for your insightful comments. In this study, our primary focus is on how to reuse fine-tuning outcomes to achieve the objective of improving multiple large language models through one-time training. In our experiments, we did not find that the delta model would amplify biases within the trained models. In future research, we will explore whether the CMC would have an impact on the biases of large language models.\n\n**Q4: Robustness and Error Analysis: The paper does not provide statistical robustness measures such as confidence intervals or error bars, which are essential for validating the reliability of the results.**  \nA4: Thank you for your suggestion. The results of our experiments are derived from the average of multiple trials, and there is a significant performance improvement compared to the baseline. We will provide a more detailed error analysis in the next version. To facilitate reproducibility, we release the code repository and dataset available, and we will further release the trained models to the public.", "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:

Meta-review: Based on the overall positive reviews and the authors' thorough responses addressing the reviewers' concerns, I recommend a weak accept for this paper. CMC presents a novel and promising approach to efficiently fine-tune multiple LLMs, demonstrating significant potential for reducing training costs.
Decision: Accept (poster)