Title: Cross-model Control: Improving Multiple Large Language Models in One-time Training
Venue: NeurIPS 2024
Accepted: yes
Keywords: Large Language Model

Fine-tune

model transfer
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

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 .

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., 2023). At the same time, although large pre-trained language models have gained the ability to 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 et 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; Chen and Yang, 2023). During the training process, models may encounter data containing real-world personal privacy information or content that is harmful, offensive, or prejudiced. This could lead them to output these information during the

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inference stage (Wei et al., 2023; Huang et al., 2022). When deploying to a large number of users, it is crucial to avoid outputting these content.

However, existing methods usually could only optimize a single target model at a time, such as fine-tuning (Hu et al., 2022; Lester et al., 2021; Liu et al., 2024b), retraining (Keskar et al., 2019), or activation editing (Li et al., 2023a; Leong et al., 2023). These methods require altering the model's parameters or adding new parameters, which must align with the original parameters. These newly added or modified parameter values cannot be applied to models with different structures. Furthermore, although some guided decoding methods (Liu et al., 2021; Krause et al., 2021; Liu et 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 a 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, a critical question arises: When model owners face constraints on data and computational resources, preventing them from directly fine-tuning their models, can they effectively leverage the fine-tuning outcomes of other LLMs at a 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 in the model's output logits after the fine-tuning process, compared to the logits before fine-tuning. We discovered that the shifts in logits across different models exhibit a high degree of similarity. It further inspires us to think: Could a portable neural network model be utilized to alter the output logits of various models? Thereby enabling a 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 1, we introduce a tiny language model with significantly fewer parameters than mainstream LLMs. This model is trained alongside a frozen template LLM, enabling the tiny language model to alter the logits output by the LLM. Subsequently, to facilitate its application across models with different vocabularies, we introduce the strategy of prefix match with minimum edit distance (PM-MinED), a 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 of a single model that concurrently improves multiple models. We conducted extensive experiments on instruction tuning and unlearning tasks, demonstrating the effectiveness of CMC, where a tiny language model with only 15 million parameters can empower a large model with 70 billion parameters, which is thousands of times larger. Our contributions can be summarized as follows:

To the best of 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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fine-tuning outcomes, enabling LLM owners who lack data and computational resources to 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 LLMs.

2 Preliminaries

Even though different LLMs may have varying parameter sizes and distinct vocabularies, if these models are fine-tuned on the same task, does the effect of fine-tuning exhibit similarities across them?

Given a dataset D and an vanilla model M v , we fine-tune the model M v using dataset D to obtain a fine-tuned model M d . Provided with a prompt, the models M v and M d predict the next token, resulting in ζ v ∈ R |V| and ζ d ∈ R |V| respectively, where V denotes the size of the vocabulary. We define the effect of fine-tuning as the as the shift in logits, that is ζ d -ζ v .

When comparing the effects of fine-tuning between two distinct models, M v 1 and M v 2 , it involves comparing the shifts in logits when encodeing the same sequence. Specifically, given an inputresponse pair, denoted as [ x 1 , x 2 , ..., x n , y 1 , y 2 , ..., y m ] , we fed this pair into the models M v 1 , M d 1 , M v 2 , and M d 2 . We then record the logits from the final layer output of each model. The logits corresponding to the response part are extracted, yielding ζ v 1 ∈ R m ×|V 1 | , ζ d 1 ∈ R m ×| V 1 | , ζ v 2 ∈ R m ×|V 2 | , and ζ d 2 ∈ R m ×|V 2 | . To reduce the effects of varying scales in the logits from different models, we attempted to 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, we obtain the fine-tuning effects.

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-tuned 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 in the form of heat maps 2 . Taking Figure 2a as an example,

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we selected the top 100 tokens with the highest logits values in ζ GPT 4 -Alpaca LLAMA2-13B at each time step, sorted according to the values of T LLAMA2-13B , and applied the corresponding indices to T LLAMA2-7B . We found that the upper and lower sub-figures in Figure 2 were highly similar, both exhibiting a 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.

3 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 is illustrated in Figure 3.

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, we attempt to fit the logits shifts of LLM using a delta model. Specifically, as shown in figure 3a, we train the template LLM and delta model together, keeping the template LLM frozen and the delta model tunable. During forward propagation, we add the logits of LLM and the logits of the delta model together to obtain the final logits, aiming to teach the delta model to alter the logits of LLM. Considering that the logits of different LLM outputs have different scales, to enhance the delta model's applicability during the inference stage after training, we apply a LogSoftmax to the logits of LLM to project them into logarithmic space.

Formally, we denote the template LLM as M t , the delta model as M d , a training data sample as x , the logits output by M t and M d at the final layer as ζ t and ζ d respectively, and the final logits used for calculating the loss as ζ final .

We do not apply LogSoftmax to the logits ζ d 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-tuning effects. During the inference stage, the interaction between the LLM

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 LLMs with different vocabularies, we employ a token mapping strategy called PM-MinED to 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 M u , a prompt as [ x 0 , x 1 , ..., x t -1 ] . At time step t , the logits output by M u and M d at the final layer are denoted as ζ t u and ζ t d respectively, where

The logits output by the user LLM assisted by the delta model at time step t are represented as

Here, TokenMap represents the PM-MinED strategy, and α denotes a strength coefficient that can 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 LogSoftmax are all negative. If a token from the LLM cannot find its corresponding delta model token in the token mapping, the logits of this token will not be able to receive a negative adjustment.

3.3 Token Mapping Strategy PM-MinED

To enable the delta model to 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 et 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; if 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 et 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 a 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 in Figure 3c, in the process of matching the token 'ultimate' with the corresponding token in the delta model vocabulary, we initially create a candidate set consisting of tokens that either have 'ultimate' as a prefix or are prefixes of 'ultimate', which includes elements such as ['ultimately', 'ult', 'ul', 'u']. Subsequently, within this candidate set, by comparing the edit distances, the token 'ultimately' is 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 α on performance in Sections 4.3 and 4.4. Finally, the outcomes of the ablation 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.

4.1 Experiment on Instruction Tuning

Instruction tuning refers to the process of fine-tuning pre-trained models to better understand and follow human natural language instructions.

Training Dataset We utilized the GPT4-Alpaca dataset (Peng et al., 2023) to train our delta model. This dataset consists of 52k instruction-following examples, with instructions sourced from Stanford Alpaca data (Taori et al., 2023) and responses generated by GPT-4.

Evaluation Method We employed the AlpacaEval benchmark (Li et al., 2023b) to evaluate the instruction-following 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 output, using win rate as the evaluation metric.

Implementation We introduce TINYLLAMA-110M 3 as the delta model, which is based on the 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 as the template LLM to guide the delta model in learning to modify the output of the LLM. LLAMA2-13B, LLAMA2-70B , 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 et 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 to reduce the number of trainable parameters. As an immovable method, LoRA is not suitable for direct comparison with our approach but serves as a 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 a smaller-scale model within the model family as an anti-expert, fine-tunes it as an expert, and leverages the difference in logits between the expert and anti-expert during decoding for larger models. To facilitate a fair comparison, we use TINYLLAMA-110M as the anti-expert, fine-tuned on GPT4-Alpaca as the expert. This approach cannot be directly applied to models with different vocabularies .

Analysis The results of instruction tuning are shown in Table 1, where we observe that:

A single portable delta model can enable LLMs 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 to follow instructions can be applied to a wide range of LLMs, which is consistent with the findings in Section 2.

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 to the process of making a model forget specific information from the training data in order to prevent privacy leakage.

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. 4 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 metrics: 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 a ratio that compares the likelihood of its correct answer to an incorrect answer.

Implementation Initially, we trained LLAMA2-7B-CHAT , LLAMA2-13B-CHAT , and MISTRAL7B-INSTRUCT on the Forget Set and Retain Set of TOFU to memorize fictitious author information, resulting in LLAMA2-7B-TOFU, LLAMA2-13B-TOFU, and MISTRAL-7B-TOFU. We selected TINYLLAMA-110M as the delta model, with the model learning fictitious author information, LLAMA2-7B-TOFU, serving as the template LLM, and LLAMA2-13B-TOFU and MISTRAL-7BTOFU serving as user LLMs. 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:

Here, L represents the cross-entropy loss, S F denotes the Forget Set, and S R denotes the Retain Set.

Baseline methods a) LoRA fine-tuning , which involves training each model separately. b) δ -UNLEARNING (Huang et al., 2024) fine-tunes LLAMA2-7B-CHAT model, and guides LLAMA213B-CHAT during training and decoding to avoid outputting private information using the changes in logits between the tuned and the original LLAMA2-7B-CHAT. 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 LLAMA2-7B-TOFU 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 a single training session. Furthermore, compared to δ -UNLEARNING, our approach achieves superior performance at less than half the inference cost.

4.3 Impact of Parameter Size of Delta Model on Performance

We experimented with delta models of smaller parameter sizes, specifically the TINYLLAMA-42M 5 and TINYLLAMA-15M 6 models, and tested their performance on the first 50 data points of AlpacaEval.

The results are presented in Table 3. In most cases, we find that reducing the delta model's parameter size leads to a decrease in performance, but the performance does not rapidly decline as the parameter size decreases. When the delta model is applied to LLAMA2-70B, a larger delta model size may have a 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 an overly large parameter size in the delta model may lead to overfitting. This indicates that a delta model used for adjusting LLM output logits does not necessarily require a large number of parameters. It also demonstrates the significant potential for small models to assist large models.

4.4 Impact of Strength Coefficient on Performance

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We experimented with different strength coefficients α 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 AlpacaEval. As shown in Figure 4a, we found that the performance was optimal when the α value was 1.0, and increasing or decreasing α resulted in decreased performance. This is similar to causing the delta model to overfit or underfit, so we explored whether adjusting the α 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 α value could counteract the overfitting or underfitting during training. The underfitting delta model had a win rate of 54 when α was 1.0, which increased to 64 when α was 2.0. Similarly, the overfitting delta model had a win rate of 48 when α was 1.0, which increased to 64 when α 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, it is still possible to achieve the desired performance by adjusting the strength coefficient α .

For unlearning, we found that adjusting the value of α can serve as a balance between forgetting and retaining . As shown in Figure 4b, increasing the value of α can prevent the model from outputting more sensitive information, but it may also lead to the loss of necessary information. Conversely, decreasing the value of α can allow the model to retain more essential information, but it may result in the model outputting more sensitive information. Users of LLM can flexibly adjust the α value based on the actual circumstances.

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 matching during token mapping on performance. As demonstrated in Table 4, our findings reveal that the removal of LogSoftmax results in a decline in performance. This suggests that projecting the logits 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.

5 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-time methods.

Training-Intensive methods One of the most effective methods for adapting large language models (LLMs) to specific downstream tasks involves updating only a 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, 2021) introduces a series of continuous, task-specific vectors at the beginning of the input sequence for task adaptation. Adaptertuning (Houlsby et al., 2019) integrates compact, trainable modules within the layers of a pre-trained model to facilitate transfer learning. BitFit (Zaken et al., 2021) 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-Time 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 a 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 a 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 a 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 a 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 a 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 in 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 LLM contains tokens from languages not covered by the delta model's vocabulary, then the logits of these linguistically diverse tokens will not be adjusted accordingly.

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A Hyperparameters and experimental details

A.1 Instruction Tuning

During the training of the 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-7B models, we set r to 140 and alpha to 280, while for LLAMA2-13B, r is set to 90 and alpha to 180. For all models, the learning rate is 2e-5, batch size is 32, LoRA target are [q_proj,k_proj,k_proj], and the training lasted for 2 epochs.

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 1e-4, batch size to 16, and trained for 20 epochs.

For LoRA fine-tuning, across all models, r was set to 32, alpha to 64, with a learning rate of 1e-4, batch size of 16, and training spanned 5 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.

We fine-tuned them individually on 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 as inputs, and used the output of M d 1 as the response to form input-response pairs. We chose the average Sinkhorn divergence as the indicator. The result is shown in Table 5. We observed that despite the models having different parameter scales and vocabularies, training on the same dataset still resulted in similar logits shifts. Conversely, significant differences were observed when training on different datasets.

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{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS:\nThe 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.\n\nWEAKNESSES:\nThe 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.\n\nQUESTIONS:\nGeneralizability 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?\n\nLIMITATIONS:\nThe 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.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-06-14"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nLarge 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.\n\nSTRENGTHS:\n1. 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.\n\nWEAKNESSES:\n1. 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.\n\nQUESTIONS:\nSee weaknesses.\n\nLIMITATIONS:\nSee weaknesses.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS:\n1. 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.\n\nWEAKNESSES:\nThere 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.\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nThe authors have adequately addressed the limitations and risks of their work.", "TITLE": "Official Review", "RECOMMENDATION": 8, "REVIEWER_CONFIDENCE": 5, "SOUNDNESS_CORRECTNESS": 4, "DATE": "2024-07-11"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS:\n1. 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.\n\nWEAKNESSES:\n1. 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.\n\nQUESTIONS:\nSee \"Weaknesses\".\n\nLIMITATIONS:\nThe authors provide a discussion on limitations.", "TITLE": "Official Review", "RECOMMENDATION": 7, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-13"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS:\nOriginality: 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.\n\nWEAKNESSES:\nToken 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.\n\nQUESTIONS:\nToken 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.\n\nLIMITATIONS:\nThe 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.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-15"}