SHED: Shapley-Based Automated Dataset Refinement for Instruction Fine-Tuning

Authors: yexiao  he
ziyao  wang
zheyu  shen
guoheng  sun
yucong  dai
yongkai  wu
hongyi  wang
ang  li
Venue: NeurIPS_cc_2024_Conference
Published: 2024-05-14 20:12:37

Abstract: The pre-trained Large Language Models (LLMs) can be adapted for many downstream tasks and tailored to align with human preferences through fine-tuning. Recent studies have discovered that LLMs can achieve desirable performance with only a small amount of high-quality data, suggesting that a large portion of the data in these extensive datasets is redundant or even harmful. Identifying high-quality data from vast datasets to curate small yet effective datasets has emerged as a critical challenge. In this paper, we introduce SHED, an automated dataset refinement framework based on Shapley value for instruction fine-tuning. SHED eliminates the need for human intervention or the use of commercial LLMs. Moreover, the datasets curated through SHED exhibit transferability, indicating they can be reused across different LLMs with consistently high performance. We conduct extensive experiments to evaluate the datasets curated by SHED. The results demonstrate SHED's superiority over state-of-the-art methods across various tasks and LLMs; notably, datasets comprising only 10% of the original data selected by SHED achieve performance comparable to or surpassing that of the full datasets.

Main paper: 
 introduction: 
 The development of LLMs marks a major leap in machine learning, transforming how we approach natural language processing (NLP) and artificial intelligence (AI) research [1, 2, 3, 4, 5] . LLMs such as GPT-3 [2] , Mistral [6] , and LLaMA/LLaMA2 [3, 4] highlight the benefits of pre-training on large and diverse mixtures of data corpora, empowering these LLMs with a wealth of knowledge [7, 8] . Moreover, one of the pivotal strengths of LLMs lies in their adaptability to specific tasks through fine-tuning. Fine-tuning, a process that involves adapting LLMs to one or multiple task-specific datasets, enables the pre-trained LLM to acquire task-specific information. Furthermore, it facilitates the alignment of LLMs to more accurately follow human instructions through fine-tuning on a dataset comprised of instructions paired with appropriate responses [9] , which is known as instruction tuning.However, fine-tuning LLMs also raises challenges. A primary concern is that noisy data or harmful instances in the fine-tuning dataset can significantly degrade the performance of pre-trained LLMs [10] . While many works have developed large and diverse datasets for fine-tuning purposes, recent research suggests that meticulously curated datasets of high quality, even if smaller in size, can be more effective in harnessing the full potential of LLMs [11, 12, 13] . Indiscriminately increasing the volume of data can lead to ineffective performance improvements and might even deteriorate LLM performance due to the introduction of noisy and harmful instances. Additionally, for instruction tuning, the LLM has already learned the necessary knowledge in the pre-training stage. The dataset used in the fine-tuning stage merely aims to better align the LLM to follow human instructions, indicating that this process does not necessitate extensive data [14] . Furthermore, fine-tuning LLMs on extensive datasets incurs significant computational costs. The necessity for considerable GPU resources presents a critical challenge [15] . Only researchers and institutions equipped with sufficient computing resources can perform such tasks, limiting broader applications and progress within the LLM community. Consequently, there is a pressing need to design a novel method for curating small and high-quality datasets that enable efficient fine-tuning.Previous efforts have employed various methods such as curation or generation through manual efforts or commercial LLMs [11, 16] , identifying subsets from larger datasets via training dynamics or estimating marginal contributions [17, 18] . Most current methods for data selection neglect the potential influence that different combinations of samples can have on model performance. The Shapley value [19] , introduced in cooperative game theory, provides a method for fairly evaluating the contribution of each participant by examining all possible combinations and their effects on the overall result. This principle has also been utilized in machine learning to assess the impact of individual data points within a given dataset [20] . The Shapley value can serve as a criterion to refine one or more large datasets to extract high-quality data points, enabling the curation of a smaller yet high-quality dataset. This method not only facilitates the selection of impactful data but also considers the effectiveness of selected data combinations. The Shapley value seems to be a promising tool for data selection. However, calculating the Shapley value for all the data samples in a dataset is computationally expensive, especially for large-scale fine-tuning datasets.Motivated by the aforementioned challenges, we present SHED, a Shapley-based automated dataset refinement framework for fine-tuning LLMs. The key intuition behind SHED is to perform Shapley value evaluations on a small portion of representative samples only, thereby dramatically decreasing the computational complexity of Shapley-based data refinement. Specifically, as Figure 1 illustrates, SHED consists of three key components: (1) model-agnostic clustering, (2) proxy-based Shapley calculator, and (3) optimizationaware sampling. Initially, the modelagnostic clustering groups embeddings of the original dataset and then selects representative data samples as a proxy for each cluster based on the distance of embeddings to the cluster centroid. These proxy data instances are then evaluated by the proxy-based Shapley calculator, which employs an approximation method to efficiently calculate their Shapley values, focusing on task-specific objectives (e.g., accuracy and fairness). This method involves iteratively removing groups of instances from the proxy dataset and assessing the performance variation of the model to estimate the collective contribution of these instances, thereby streamlining the computation of Shapley values. The derived Shapley values of these proxy data instances are used as the quality score for their respective clusters. Finally, optimization-aware sampling selects data from clusters to compile a compact yet high-quality dataset, employing strategies that may favor clusters with higher-quality scores. SHED only computes Shapley values for the cluster representatives rather than each data point, drastically boosting the efficiency of data refinement. Furthermore, Yang et al. (2022) observed that hyperparameters tuned on smaller models can be effectively transferred to larger models, significantly reducing tuning costs while maintaining performance [21] . We observed a similar phenomenon: datasets curated by SHED exhibit strong transferability, performing robustly across LLMs of various sizes and families. This suggests that smaller LLMs can be used for data selection, reducing computational costs. The selected datasets can be used to fine-tune larger LLMs and reused in multiple tasks to further amortize costs. Moreover, SHED offers a unified yet flexible framework, catering to various user needs by providing multiple options within each component. For example, the optimization objective for Shapley value measurement can be tailored to specific tasks (e.g., fairness).Our key contributions can be summarized as follows:• We present SHED, a generic data refinement framework based on Shapley values, which can curate a small yet high-quality dataset for boosting the efficiency of fine-tuning LLMs. • We conducted extensive experiments on two benchmark datasets, i.e., MMLU and Wiz-zardLM, the results demonstrate that fine-tuning LLMs with small datasets curated by SHED yields performance comparable to, or even better than, using the original large datasets. Notably, datasets curated by SHED exhibit strong transferability, achieving robust performance across various LLMs of different sizes and families. This indicates that smaller models can employed to greatly lower computational expenses for data selection, and the selected dataset can be used to fine-tune larger models and reused across multiple tasks to further distribute the costs. • Code associated with the collection of high-quality datasets curated by SHED can be found at SHED: Shapley-Based Automated Dataset Refinement.2 Related Work 
coreset selection: 
 Coreset selection plays a critical role in machine learning by targeting the selection of a representative subset from a larger dataset. Various coreset selection methods use unique criteria for choosing samples. Geometry-based approaches focus on the geometric properties of the data points, striving to retain geometrically significant samples that represent the overall data distribution [22, 23, 24, 25] .Uncertainty-based methods choose samples based on the uncertainty they present to the model, typically engaging samples that the model finds challenging to classify [26, 27, 28] . Decisionboundary-based methods select samples that are close to the decision boundary of the classifier, ensuring that the nuances of the classification boundary are well-represented in the selected subset [29, 30] . Gradient-matching approaches involve selecting a subset that yields similar gradient distributions as the entire dataset when used in training [31, 32] . Bilevel Optimization optimizes the coreset selection in a way that the selected subset maximizes certain performance metrics [33] . Dataset Selection with Datamodels using datamodels to approximate how the learning algorithm utilizes different subsets of training data to minimize target task loss. [34, 35] Submodularity-based approaches consider both diversity and information richness, striving for a balanced representation of the dataset [36] .Due to the superiority of instruction fine-tuning in enhancing the performance of LLMs, many recent studies focus on selecting high-quality instruction fine-tuning data. Based on methods, it can be divided into the following categories. Indicators-based methods define multiple metrics, such as instruction length and perplexity, to compute quality scores for each instruction instance [16, 37, 38, 39] . Training-based methods leverage the performance improvement through fine-tuning to score and select instruction data suited for fine-tuning [18, 40, 41, 42, 43, 44, 45] . Some other methods employ commercial LLMs like ChatGPT to assess quality, complexity, and diversity of instructions for selection [13, 46, 47, 48, 49] .Most existing methods for data selection overlook the impact of various data combinations on model performance. As Table 1 illustrates, datasets formed by combining high-quality data, which are merely based on the independent quality score of each individual data sample, do not necessarily enhance model performance effectively. The combination of different data can impact the final performance of fine-tuning.Although TS-DSHAPLEY [18] also utilized Shapley value for data selection, SHED offers several distinct advantages. SHED computes Shapley values only for proxy data of clusters rather than each individual data point, dramatically reducing computational overhead compared to TS-DSHAPLEY. SHED employs model-agnostic clustering, enhancing the transferability of curated datasets across different language models and model families. Moreover, SHED considers data diversity and can be customized for various optimization objectives, while TS-DSHAPLEY primarily focuses on predictive accuracy.Many other existing works are also task-specific, limiting their applicability. In contrast, SHED offers a unified and flexible framework, adaptable to various instructional tuning tasks, making it more widely applicable.Question: Does the paper discuss the limitations of the work performed by the authors? Answer: [Yes] Justification: The paper has a dedicated "Limitations" section where it discusses the reliance on representative embeddings, which may reduce applicability in scenarios lacking such embeddings. It also discusses the potential oversight of rare samples in clustering and the risk of model bias due to a primary focus on performance. Guidelines:• The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper. • The authors are encouraged to create a separate "Limitations" section in their paper.• The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be. • The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with a few runs. In general, empirical results often depend on implicit assumptions, which should be articulated. • The authors should reflect on the factors that influence the performance of the approach.For example, a facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or a speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon. • The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size. • If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness. • While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an important role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations. 3. Theory Assumptions and Proofs some way (e.g., to registered users), but it should be possible for other researchers to have some path to reproducing or verifying the results. 
proposed method: 
 Motivated by the aforementioned challenges, we present SHED, a generic framework that exploits Shapley value to identify and select high-quality data to improve the performance and efficiency of fine-tuning LLMs.The motivation behind this work is underscored by the observation, as illustrated in Table 1 , that naively aggregating high-quality data merely based on the independent importance of individual samples does not guarantee a performance improvement of fine-tuning. We believe this phenomenon is attributed to the complex interactions between different instances within the fine-tuning process. Thus, there is a pressing need to design a novel data selection method, which accounts for the individual and collective contributions of instances to model performance.The Shapley value offers a compelling solution to this challenge. It quantifies the marginal contribution of each instance to the overall performance of the model, considering all possible combinations of instances. The formulation of the Shapley value for a data sample i in dataset D can be expressed as:EQUATIONwhere S i is the Shapley value of i, P is the subset of dataset D, |D| and |P | are the total number of instances in D and P , v(P ) is the value function of P , which represents the performance of the LLM model fine-tuned on the subset P . As Eq. 1 indicates, the Shapley value of an instance i captures its average impact on model performance across all subsets it might be part of. This ensures a fair evaluation of the contribution of each instance in the original dataset, enabling the selected data is genuinely beneficial for enhancing model performance when integrated with other data samples.Additionally, the value function v(P ) in Eq. 1 serves to calculate contributions from corresponding data. This value function can be tailored for various optimization objectives, such as accuracy and fairness, facilitating the selection of data that aligns with the task-specific requirements.However, computing the Shapley value, as depicted in Eq. 1, demands extensive computational efforts, because it requires evaluating the contribution of each instance across all possible combinations. For a dataset with |D| instances, there are a total of 2 |D| -1 possible combinations. For each combination, two evaluations are needed, i.e., one includes a certain instance and the other one holds out that instance, doubling the computational workload to determine the contribution of that particular instance. Thus, the time complexity for measuring the Shapley value of each instance is O(2 |D| ).Given the need to perform this calculation for all |D| instances to determine their individual Shapley values, the overall time complexity for the dataset increases to O(|D| • 2 |D| ). This exponential complexity makes direct computation of Shapley values impractical for large datasets.To address the above challenges, we design SHED, comprising of three key components: modelagnostic clustering, proxy-based Shapley calculator, and optimization-aware sampling. We introduce each component in detail.Model-agnostic Clustering. Given the time complexity of computing the Shapley value, calculating the Shapley value for all instances in a large fine-tuning dataset is impractical. The model-agnostic clustering employs models from Sentence Transformers [51] to generate semantically meaningful embeddings for each sample in the original dataset. These embeddings facilitate the efficient and effective computation of semantic similarities between textual inputs, enabling the grouping of data with similar contexts. Moreover, those model-agnostic embeddings enhance the transferability of the curated dataset, as demonstrated in Table 7 . Then, the model-agnostic clustering applies algorithms, such as K-means [52] and Agglomerative Clustering [53] , to group the embeddings. It then selects the representative data, which is closest to the cluster centroids in the embedding space, for each cluster. In doing so, we use these representative samples as the proxy of the respective clusters. Subsequently, SHED only calculates the Shapley values of those proxy data, using their Shapley values as the quality scores for their respective clusters. Employing proxy data effectively captures the essence of the diversity and complexity in the dataset. This strategy significantly reduces the computational burden associated with calculating Shapley values across vast datasets.Proxy-based Shapley Calculator. To further improve efficiency for Shapley value calculations, the proxy-based Shapley calculator employs an approximation method to estimate the Shapley values of the proxy data. This method iteratively removes groups of n instances from the proxy data D p , followed by an evaluation of the model's performance to assess the impact of these instances. The performance variations before and after the removal of a specific group of instances quantify their collective contribution. Specifically, the contribution of the initial group of n instances, denoted asc (1..n)∈Dp , is computed by c (1..n)∈Dp = v(D p ) -v(D p \ {1..n}).Similarly, the contribution for the subsequent group of n instances is determined by c (n+1..2n)∈Dp = v(D p \{1..n})-v(D p \{1..2n}). This procedure is repeated, progressively removing groups of n instances until the entire proxy data has been visited, which marks the completion of a single iteration. This entire iteration process is then repeated k times to enhance the accuracy of the approximation. After completing k iterations, the Shapley value for a certain instance i of the proxy dataset is approximated using the average of its contributions across all iterations, defined asS i ≈ 1 k k ci(k)n , where c i (k) denotes the contribution associated with instance i in the kth iteration.Proxy Optimization-aware Sampling.The Shapley value of each proxy data is assigned as the quality score of the corresponding cluster. Optimizationaware sampling utilizes these quality scores to sample data from these clusters, aiming to curate a small yet highquality dataset. Optimizationaware Sampling offers two sampling methods: Quality-Ordered Cluster Sampling (QOCS) and Quality-Weighted Cluster Sampling (QWCS). QOCS prioritizes sampling from clusters with the highest quality scores. It selects instances starting from the most high-quality clusters until a predefined target sampling number is reached. QWCS adopts a probabilistic approach to sample instances across all clusters, with the probability of selection from a given cluster weighted by its quality score. This method aims to balance quality with diversity by allowing for the inclusion of instances from a broader array of clusters, thus potentially enriching the dataset with a wider variety of high-quality data points. The probability Pr(i) of selecting an instance from cluster i is defined in Eq. 2:EQUATIONwhere S i represents the quality score of cluster i, and f is a scaling factor that modulates the emphasis on quality versus diversity within the sampled dataset. By adjusting f , users can tailor the sampling process to prioritize either quality or diversity to suit specific task goals. A higher f value tends towards selecting higher-quality instances, offering a versatile toolkit for dataset optimization. 
experimental setup: 
 Datasets. We conduct experiments on two famous benchmark datasets, MMLU (99.8k instances) [54] and WizardLM-evol-instruct-70k (70k instances) [55] .SHED Implementation. We use the K-means algorithm for the model-agnostic clustering and set the number of clusters to 3000. For the proxy-based Shapley calculator, the value function is set as the accuracy of the foundation model fine-tuned on the proxy data. We use LLaMA-7B [3] as the pre-trained foundation model and 10% instances in the MMLU test set calculating the Shapley values of proxy data. The number of iterations k is set to 10, and the number of instances n removed from the proxy data each step is set to 60. To conserve time and resources, instruction fine-tuning within the proxy-based Shapley calculator is conducted for one epoch. For optimization-aware sampling, we employ the QOCS and QWCS strategies with setting the scaling factor to 1, investigating their efficacy with a variety of target sampling sizes. These implementations are denoted as SHED-QOCS and SHED-QWCS. The target sampling size varies from 1, 000 to 20, 000 with increments of 1, 000, to thoroughly assess the impact of each sampling approach on fine-tuning performance.Baseline Methods. We compare SHED with three baseline methods. Specifically, we implement a random-sampling method, denoted as RS, which randomly selects a subset from a large dataset. We also use the Dataset Quantization method [38] , denoted by DQ, and the Data Selection with Importance Resampling [50] , denoted by DSIR, for comparisons. In addition, we also consider fine-tuning models on the entire dataset, denoted as FULL, as a baseline.Evaluation Settings. After obtaining the curated datasets using SHED and baseline methods, we fine-tune the pre-trained models using each curated subset, respectively. We apply the Low-Rank Adaptation (LoRA), which is a flexible and efficient tool, for fine-tuning and set the default LoRA rank to 128 [56, 57] . For all curated datasets, the instruction fine-tuning was conducted for 3 epochs. Notably, we use the same hyperparameters in fine-tuning across all methods to ensure a fair comparison, aiming to isolate the impact of data selection on model performance.We evaluate the performance of fine-tuned models on MMLU and ARC-challenge tasks using the lm-evaluation-harness testing framework [58] . To better evaluate the human preferences of fine-tuned models, we adopt MT-Bench [59] in our experiments. All the experiments are conducted on two A100 GPUs, each with 80GB of memory.We summarize the experimental results for SHED and other baseline methods. For consistency, the bold numbers indicate the corresponding method outperforms the FULL method. Additionally, we underline the best result achieved among all the methods that curate subsets.For each method, the dataset from the curated collections that yields the optimal result across various sample sizes is referred to as the best-selected dataset. Effectiveness of SHED. Given the datasets generated from SHED and the baseline methods, we fine-tune the LLaMA-7B model, respectively, and evaluate the fine-tuned models on the MMLU and ARC-challenge tasks. We compare the results of the datasets of 10k instances curated by SHED and the baseline methods. As depicted in Table 2 , when the number of total sampling instances is fixed (10k), the datasets curated by SHED consistently outperform those chosen by baseline methods. We also compare the performance of fine-tuned models using the best-selected dataset by each method. Table 3 shows the evaluation results for the MMLU task. Our method, SHED-QOCS, demonstrated superior performance on the MMLU dataset compared to baseline methods, achieving the highest results among the curated datasets. Furthermore, SHED-QOCS also led in performance when utilizing the WizardLM dataset. It is notable that SHED-QOCS outperforms the full dataset, achieving a 2.76% higher accuracy. In Table 4 , we report the results of the ARC-challenge task.Similarly, among the datasets curated from the MMLU dataset, the selected dataset of our method SHED-QWCS achieves the best result compared with the baseline methods. It also surpasses the full dataset by 3.22%. Within the datasets derived from WizardLM, SHED-QOCS once again curated the dataset of best performance, which surpasses the full dataset by 3.41%. The results demonstrate the effectiveness of SHED. Although SHED demands more computational effort, its strength lies in creating high-performance datasets.Evaluations on MT-Bench. We use MT-Bench to evaluate the performance of datasets curated by SHED in terms of human preferences. Table 5 demonstrates that the datasets curated by SHED align well with human preferences, not only enhancing accuracy but also enabling the model to better understand and follow human instructions, generating answers that are more favorable to humans. The dataset constructed through the SHED-QWCS method, sampled from WizardLM, achieved a remarkable score of 5.24 on the MT-Bench. The results presented in Table 5 represent the average of five independent runs.Transferability Evaluation of Curated Datasets across Various Models. To evaluate the transferability of datasets curated by SHED, we first apply SHED to select data from the MMLU and WizardLM datasets based on LLaMA-7B. Then, we fine-tune LLaMA-13B, Vicuna-7B, and GPT-2 using the best-selected dataset curated by SHED and the baseline methods. As summarized in Table 6 and Table 7 , datasets curated by SHED exhibit robust performance across various models, demonstrating their transferability and applicability across various tasks and even different model families. The strong transferability of the curated datasets indicates that SHED identifies generally high-quality data. The computational cost for data selection can be significantly amortized across various models. In addition, the datasets selected by LLaMA-7B also achieve good performance when fine-tuning the larger model LLaMA-13B. This indicates that we can utilize smaller models to select data, thereby significantly reducing the computational cost of data selection. Impact of Number of Clusters. The number of clusters in K-means affects the computational cost needed for Shapley value calculations and the relevance of proxy data to its cluster. An increase in the number of clusters leads to smaller and more homogeneous groups, thereby improving the proxy data's representativeness for its respective clusters. However, this comes at the cost of increased computational overhead, highlighting a balance that must be struck to optimize both efficiency and representativeness. In this experiment, we evaluate the best-selected dataset by SHED across varying numbers of clusters using LLaMA-7B on the MMLU test set. Guided by the findings in [60] , our investigation begins with a baseline cluster count of C = |D|. We present the computation time for Shapley value computations across different settings, maintaining consistency with the experimental setup outlined in Section 4.1.As Figure 3 (a) and Figure 3 (b) show, the results reveal that performance improvements of curated dataset reach a plateau when the number of clusters exceeds 3 |D|. Meanwhile, Figure 3 (c) demonstrates a proportional increase in computation time for Shapley value calculations as the number of clusters rises. Notably, at very low cluster counts (e.g., below 1000), Shapley value computation times are largely dictated by the evaluation, with the time spent remaining relatively constant across varying datasets. In such cases, the computation time is more significantly affected by the size of pre-trained models rather than the number of clusters itself. Given the transferability of datasets curated using the SHED, it is feasible to employ a smaller foundational model than the target model within the proxy-based Shapley value calculator. In doing so, the computation overhead for evaluation can be significantly reduced, making SHED a practical approach in real-world settings. The precision of Shapley value estimates increases with the number of iterations k, providing a more accurate measurement of each data sample's contribution to the model performance. However, this increment also leads to a proportional rise in computational cost, leading to a contrasting relationship between computational efficiency and the accuracy of Shapley value estimations. To seek the optimal number of iterations for Shapley value calculations, we analyzed the performance of datasets curated by SHED under varying iteration settings. The experiments are conducted with the LLaMA-7B model on the MMLU test set, following the experimental settings detailed in Section 4.1.Figures 4(a ) and 4(b) illustrate that the performance of the curated datasets by QOCS and QWCS are stable once the iteration number surpasses 10. This result highlights the stability of our methods beyond 10 iterations, showing that further iterations beyond this threshold do not significantly improve dataset quality. Given the balance between computational cost and performance, setting the number of iterations to 10 is recommended for optimal efficiency and robustness. 
data selection for multiple tasks.: 
 In our experiments, we thoroughly evaluate methods regarding accuracy. It is notable that our framework is readily adaptable. By setting different value functions v(P ), SHED can select any subset using arbitrary criteria. This adaptability allows SHED to customize its data selection process to produce a small dataset while improving specific objectives, such as model fairness [61] .In particular, if we aim to curate a dataset using the common fairness notion, i.e., demographic parity, we can define v(P ) the disparity in positive prediction rates between groups with protected attributes (e.g., males vs. females), calculated as the negative absolute difference -|X Male -X Female |, where X Male and X Female are the positive prediction rates for male and female groups, respectively.We assume that the running time required to fine-tune the model using a single instance is denoted by t, and the time needed to evaluate the model on a test set consisting of m instances is represented by T m . Let C denote the number of clusters, n denote the number of instances within a group and k signifies the number of iterations utilized in the proxy-based Shapley calculator as illustrated in Section 3.2. The total number of evaluations and fine-tuning per iteration would be proportional to C n . For simplicity, we assume that C is evenly divisible by n for simplicity. Given k iterations, the overall time complexity of this approximation method can be expressed as O Ck n (C+n)t 2+ T m .Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material? Answer: [Yes] Justification: We have submitted the code and data as supplemental material. All code associated with the collection of high-quality datasets curated by SHED is open-sourced. Guidelines:• The answer NA means that paper does not include experiments requiring code. Justification: The paper discusses the broader impact of SHED, emphasizing its potential to significantly enhance the efficiency of fine-tuning large language models while reducing computational costs. It also highlights the transferability of the curated datasets across various models. Additionally, the paper addresses potential negative impacts, such as the risk of model bias due to data selection methods and the implications for model fairness.Guidelines:• The answer NA means that there is no societal impact of the work performed.• If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact. • Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations. • The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster. • The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology. • If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML). 
conclusion: 
 In this work, we introduced SHED, an innovative Shapley value-based framework designed to refine datasets for the efficient fine-tuning of LLMs, addressing the computational hurdles commonly associated with Shapley value calculations through a novel clustering and proxy-based approach.Through extensive experiments conducted on benchmark datasets such as MMLU and WizardLLM, we have shown that LLMs fine-tuned with datasets curated by SHED not only match but, in some cases, surpass the performance of those trained with the original, larger datasets. Significantly, SHEDcurated datasets have demonstrated a high degree of transferability, maintaining robust performance across various models. Furthermore, SHED's flexibility and efficiency underscore its potential to revolutionize LLM fine-tuning by allowing for the creation of compact, high-quality datasets. 
limitations: 
 This research, while presenting significant advancements, encounters certain limitations that merit attention for future work. Firstly, the method's reliance on sufficiently representative embeddings may limit its applicability in real-world scenarios where such embeddings are unavailable or inadequate. Future work will explore ways to reduce this dependency for broader applicability. Secondly, the use of clustering and proxy data may overlook rare but important samples. Future research will focus on improving clustering methods to better capture these samples. Additionally, the framework's current objective focuses predominantly on model performance, which may inadvertently lead to model bias. This singular focus overlooks the equally important aspect of model fairness, crucial for ensuring that models perform equitably across diverse groups. Recognizing this, our framework is designed to be extensible and objective-agnostic, laying the groundwork for incorporating additional criteria. In subsequent research, we plan to integrate considerations of model fairness alongside performance. 
ethics statement: 
 In this work, we present SHED, a generic data refinement framework utilizing Shapley values, aimed at assembling a compact yet effective dataset to boost the efficiency of the fine-tuning process of LLMs. This study carefully avoids ethical issues beyond standard AI concerns, leveraging properly cited publicly available Internet text data. This approach ensures adherence to ethical data use standards, reflecting our commitment to responsible research practices in the AI field. 
safeguards: 
 Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)? Answer: [No] Justification: The paper does not explicitly discuss safeguards to prevent misuse of the proposed method. Guidelines:• The answer NA means that the paper poses no such risks.• Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters. • Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images. • We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make a best faith effort. 12. Licenses for existing assets Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected? • We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere to the NeurIPS Code of Ethics and the guidelines for their institution. • For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review. 
 
 Appendix: In our experiments, we employed the following hyperparameters: the number of training epochs was set to 3, the batch size was 128, the LoRA rank (lora_r) was 128, and the LoRA alpha (lora_alpha) was 256. For clustering, when the number of clusters (C) was 3000, the number of samples removed per group (n) was 60; when testing the impact of different C values on performance, n = C 50 . The number of iterations for Shapley value calculation (k) was 10, and the learning rate was 3 × 10 -4 . Data preprocessing involved using the sentence-transformers/all-MiniLM-L6-v2 model to generate semantically meaningful embeddings for each sample in the original dataset, followed by applying the k-means algorithm to cluster these embeddings.We compared the performance of datasets of varying sample sizes using SHED-QOCS and SHED-QWCS methods. For fine-tuning, we utilized the LLaMA-7B model. Other experimental configurations were aligned with the parameters detailed in Section 4.1. As figure 5 shows, datasets sampled using the SHED-QWCS approach generally outperform those obtained through SHED-QOCS, particularly with smaller sample sizes. This discrepancy is likely attributable to the limitation of SHED-QOCS in scenarios where the sample size is minimal. In such cases, SHED-QOCS tends to sample data from a limited number of clusters, leading to significant redundancy in the curated dataset. Conversely, it is observed that datasets that achieve the best performance are often those sampled via SHED-QOCS. This improved performance is observed when the sample size is sufficiently large, allowing SHED-QOCS to sample data from a wider range of clusters. The inherent strength of SHED-QOCS lies in its strategic focus on harvesting high-quality data. As the sample size increases to a point where SHED-QOCS can effectively draw from multiple clusters, its advantage in prioritizing data quality becomes significantly beneficial. Therefore, we suggest that users select between these two sampling methods based on the sample size they desire.To enhance user convenience, SHED introduces a method for setting hyperparameters.Based on Figure 3(c ), the time per iteration when calculating Shapley values exhibits an approximate linear relationship with the number of clusters. Similarly, the total time for computing Shapley values closely aligns linearly with the number of iterations. Thus, the computation time t for Shapley values can be modeled as t = θkC. SHED randomly samples 2000 instances from the dataset to calculate the Shapley value for one iteration, recording this to determine θ. To optimize k to be close to 10 and the number of clusters near 3 |D|, an optimization problem is formulated in Eq. 3.min k,C λ 1 (k -10) 2 + λ 2 (C -3 |D|) 2 s.t. θkC = t 0 (3)where λ 1 and λ 2 serve as weights, both defaulting to 1, while t 0 represents the maximum runtime set by the user. This optimization problem can be solved using the SQP (Sequential quadratic programming) method [62] to determine the optimal number of clusters and iterations.We compare SHED with two methods, LIMA and IFD (using WizardLM) [12, 40] . LLaMA-7B is used as the base model. The results are presented below, focusing on performance in the MMLU and ARC-challenge tasks.The following table shows the performance of SHED's selected dataset (with 1k samples) compared to LIMA's selected dataset (with 1k samples). It is important to note that SHED is fully automated, whereas LIMA involves manual curation. As the results demonstrate, SHED achieves competitive performance across both tasks, even with fewer samples.1. Claims Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope? Answer: [Yes] Justification: The abstract and introduction clearly state the development of SHED, an automated dataset refinement framework based on Shapley value for instruction fine-tuning, which aligns with the contributions and scope discussed in the paper. Guidelines:• The answer NA means that the abstract and introduction do not include the claims made in the paper. • The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers. • The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings. • It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper. 


Decision: The reviews for this paper are mixed, with 2 weak accept ratings and one reject. There was active discussion between the reviewer recommending rejection and the authors, but the reviewers concerns were not sufficiently addressed to lead to an improved score. The main concerns raised were the significant reliance of the method on the existence of a sufficiently representative embedding of the data, and the similarity of the method to previous ones such as TS-DSHAPLEY. The authors emphasize that their method outperforms the previous, similar work. After reading the paper, the reviews, and the author's responses, and particularly the reviewer leaning for rejection's own admission that they could not find "a particular, critical flaw in the paper" the ACs agree that the paper has made enough of a contribution to recommend acceptance. However, the heavy reliance on the existence of a sufficiently representative embedding is a significant limitation to the value of the contribution to many real-world challenges where such embeddings do not exist, and this should be captured in more detail as a limitation of the work in the paper.

Reviews: review: paper_summary: paper_summary: Tuning large language models to domain tasks is a difficult challenge requiring a dataset of high quality examples. Since noisy examples can significantly degrade performance, it is important to be able to curate these small, high-quality fine-tuning datasets. The authors propose filtering using each data point’s estimated contribution to the model performance using Shapley values. The proposed SHED method clusters the data, then iteratively deletes clusters based on the Shapley values of representative samples until a small dataset remains.
strength_weakness: strength_weakness: - The related work leave room for improvement. In particular, the primary focus of the paper is a method that uses the estimated effect of each data point on accuracy to filter data points. Yet, the discussion of such related methods is lacking. Some recent works (e.g. DsDm https://arxiv.org/abs/2401.12926) also use the impact of individual data points to subselect datasets.
- SHED uses a proxy data point (closest to the centroid) to estimate the predictive influence of the entire cluster. While this save on significant computational cost, the effectiveness of this stage depends significantly on the quality of the text embeddings used to compute similar, as well as the size and number of clusters
- The experimental method training leaves room for improvement. All methods are stated to train for the same number of epochs and same other hyperparameters. However, as training can be sensitive to hyperparameters, it could make sense to conduct a sweep based on some val set for each method, and present results for the best hyperparameters on the test set.
- It is not clear how the filtered data size was chosen. A variety of tables (e.g .Table 6 and 7) use different counts of QOCS and QWCS of 10k and 13k. Why not use the same ksubselected data size for all method for a clearer comparison?
questions: questions: Why are the different data selection methods compared with different final selected dataset sizes?
How were the training hyperparameters chosen?
limitations: limitations: The authors have sufficiently discussed the limitations of their work.
paper_summary: paper_summary: The paper proposed a data refinement framework that refines datasets for fine-tuning LLMs by using the Shapley value. Based on the description, SHED is able to create smaller, high-quality datasets from large, extensive datasets without human intervention or commercial LLMs. This process involves three key components: model-agnostic clustering, proxy-based Shapley calculator, and optimization-aware sampling. Extensive experiments show that datasets curated by SHED achieve comparable or superior performance to full datasets while significantly reducing the data size and computational costs. Moreover, SHED-curated datasets exhibit strong transferability across various LLMs.
strength_weakness: strength_weakness: 1. While SHED reduces the computational complexity of calculating Shapley values based on clustering and proxy-based calculations, it is unclear how effecient (e.g., in terms of time) it is compared to the classical calculation method.
2. As authods illustrated, the reliance on clustering may inadvertently reduce data diversity, which results in the overlook of rare but important samples.
questions: questions: 1. Is it possible to evaluate SHED on a larger dataset in the industry level considering that the used datasets in the paper are still relatively small?
2. How will the selection of initial clustering groups affect the final model performance?
limitations: limitations: No potential negative societal impact is observed.
paper_summary: paper_summary: This paper explores the problem of Shapley-based data selection for instruction tuning. Specifically, the proposed approach is composed of three steps–clustering the target samples, Shapley-style evaluation for each clustering, and resampling from the clusters based on the Shapley scores. The paper validates its approach on two instruction tuning benchmarks and obtains favorable results against several baselines.
strength_weakness: strength_weakness: The paper is probably a bit simple for a full publication of NeurIPS. It has concrete merits, though, I have concerns for several aspects of its contributions.

1. The paper adopts the expensive Shapley approach for data selection, citing its advantage for factoring in the combinatorial effects between samples. Yet, after estimating the Shapley score of the clusters, the final data selection is only conducted via resampling based on the score or ranking of the clusters, where the combinatorial effect between clusters is also not considered.  

In this sense, I am not sure whether using the costly Shapley approach delivers actual benefits. A grid search over the sampling ratio from each cluster could have better results.

2. Instruction mining has been "extensively studied" during the past year and a wealth of papers have emerged after the paper [LIMA: Less is more for alignment]. Most of those papers claim to reach comparable performance using just a few percentages of the original instruction tuning dataset.

Yet, none of these papers are included as a baseline in this work. For the baselines compared within this work, DSIR is for pre-training data selection where its scalability and computation overhead and not at all comparable with the proposed method (and similarly for DQ).

3. It is very well known Shapley-style evaluations are notably expensive as they rely on repetitive model retrainings. It is important to benchmark the computation overhead for the proposed methods.

4. The paper claims the proposed method is suitable for any objectives. This very strong argument is not validated in this paper. For many tasks where this may not be true. For example, its computational complexity is prohibitive for pre-training data selection.
Also, this approach only applies to cluster-level data selection but not to sample-level selection. Its effectiveness seems highly dependent on the embedding space for clustering. This work uses Sentence-Transfomer in the experiments. It may not work for other tasks. At least, these major limitations are not discussed in this paper.

5. As mentioned before, a notable number of methods have been proposed for the problem of instruction mining. This paper does not clearly identify a research gap to fill. A similar Shapley-style approach has been proposed TS-DSHAPLEY. Experiments conducted in this paper are also limited. In general, I have concerns about the novelty and contribution of this paper.
questions: questions: See Weaknesses.
limitations: limitations: The paper claims the proposed method is suitable for any objectives. This very strong argument is not validated in this paper. For many tasks where this may not be true. For example, its computational complexity is prohibitive for pre-training data selection.
Also, this approach only applies to cluster-level data selection but not to sample-level selection. Its effectiveness seems highly dependent on the embedding space for clustering. This work uses Sentence-Transfomer in the experiments. It may not work for other tasks. At least, these major limitations are not discussed in this paper.

Comments: title: Thank you for the feedback
Thank you for your response.
Thanks for the prompt response
Looking forward to your response
Thank you for your feedback
Thanks for the response.
Thanks for the responses.
comment: Thank you for your insightful comment. We would like to address your concerns regarding the interaction effects between data points.

The primary objective of removing clusters in our method is to calculate the marginal contribution, or boundary effect, of each cluster relative to the remaining clusters. By sequentially removing clusters and observing their impact on model performance, we can get how each cluster contributes to the overall model accuracy. After calculating the boundary contribution of a cluster in one iteration, we restart the process with a new randomized removal order. This repetition is crucial because it allows us to capture the variation in each cluster (or the data points within them)'s marginal contribution across different combinations of clusters. **By performing this process multiple times, with different sequences of cluster removal, we can average the different marginal contributions to accurately estimate the true Shapley value for each cluster. This allows us to observe how the presence or absence of a cluster in different cluster combinations influences the overall model performance, thereby capturing the interaction effects between the data points in different clusters.**

**We agree with that the claim about capturing "interaction effects between data points" might not have been entirely accurate. It is more precise to state that our method considers the interaction effects between data points in different clusters. The clustering process itself is designed to group data points that are likely to interact in similar ways with the model and data points in other clusters.** By using Shapley values on these representative clusters, we capture a higher-level interaction effect, which is crucial for scaling the computation to larger datasets. While it’s not the same as evaluating every individual interaction, **this method strikes a balance between computational feasibility and the need to preserve interaction effects,** which we believe adds value to the data selection process.

We sincerely appreciate your insightful feedback and will revise our manuscript to accurately reflect this aspect of our method. Your feedback is incredibly helpful in improving our paper.
Thank you for your detailed response to my feedback. Please find an additional note below.

W.1: Although Shapley does help consider interactions between data points, since you're re-computing the values after every cluster removal I don't think it actually matters here, i.e. compared to models that consider the contribution of each data point independently. Further, the claim that you are "capturing potential interaction effects between data points" is also not quite accurate, as the data points are grouped into larger clusters over which the Shapley values are computed.
Thanks to the authors for the prompt response. My review remains unchanged.
Reviewer tj1t, do you have any additional questions or feedback?
Reviewer kWYC, do you have any additional questions or feedback?
Dear Reviewer,

As we are approaching the end of the rebuttal period, we would like to cordially inquire about the extent to which we have successfully addressed the concerns outlined in your review.

Should any lingering points require further attention, please rest assured that we are enthusiastic about the opportunity to provide comprehensive responses to any subsequent queries or comments you may have.

Your constructive input remains invaluable to us, and we appreciate your dedication to enhancing the quality of our manuscript. Thank you for your time and consideration.

Best, Authors
Thank you for your thoughtful feedback. We appreciate the points raised, and would like to address each one in turn:

1. Embedding Quality and Model-Agnostic Data Representation:
- While there may be edge cases where Sentence-Transformer does not fully capture nuances, **SHED’s flexible framework allows for integrating alternative or task-specific embeddings, which can be chosen based on the task’s particular needs.** For example, CodeBert can be used to replace the Sentence-Transformer for tasks involving code understanding. In addition, if a task requires nuanced domain-specific understanding, fine-tuning the Sentence-Transformer to adapt to these needs can be implemented, like LLaVA's method, which only requires a small cost. This adaptability ensures that SHED remains effective across a diverse set of tasks, even beyond the current scope.
- **However, if one insists on a truly "perfect" task-agnostic model, even widely-used powerful models like LLaMA3 cannot claim to be suitable for every possible task. The task-agnostic nature we discuss is within a reasonable and practical range.**

2. Clustering-Based Data Sampling:
	While clustering is commonly used to reduce computational complexity, **SHED’s innovation lies in considering combinatorial effects between data points, model-agnostic data selection, reducing computation cost, and a flexible framework.**  Combining clustering with Shapley value calculations allows us to account for the combinatorial effects between data points, enhancing the selection process by considering their interactions. The transferability of SHED-refined datasets is the key advantage over other methods. Additionally, While classic Shapley value calculation is impractical, SHED significantly reduces computational costs by utilizing an approximation method and only calculating Shapley values for cluster proxies rather than individual data points, making it both efficient and scalable. Flexible framework design gives SHED the potential to be applied to various tasks. **We believe that utilizing well-established methods as a part of SHED should not be grounds for rejection.**

3. Importance of Instruction Selection
- **The need for instruction-tuning data selection is particularly important as fine-tuning datasets grow increasingly large and complex.** For example, datasets like P3, which includes over 100 million examples, the Pile with 825 GB of text, and LAION-400M with 400 million image-text pairs, illustrate the scale at which fine-tuning can occur. Efficient, scalable methods like SHED are essential for managing such large datasets. **Besides, as the theoretical and experimental analysis shows, our approach is designed to scale to much larger datasets, proving its value in broader applications.**
- **Furthermore, most researchers lack the resources to support pre-training from scratch, making fine-tuning an essential strategy.** SHED-selected datasets have demonstrated strong performance across multiple models, and releasing these curated datasets to the community could provide a significant contribution, helping researchers save substantial resources.
- **The field of instruction-tuning data selection may indeed be crowded, but the crowdedness does not diminish the value of contributions that address real and pressing challenges. The computational advantages, transferability, and flexible design of SHED make it a valuable tool in this area.** Moreover, as instruction-tuning datasets continue to grow in size and complexity, the need for efficient, scalable data selection methods like SHED will only increase. By providing a framework that is both efficient and adaptable, we believe SHED makes a meaningful contribution to the field, with potential applications extending well beyond the current scope.
Thanks to the authors for the response. Let me explain where I feel this work can be improved.

"Model-agnostic data representation" relies on the quality of embedding space. Compared with random sampling, the effectiveness of clustering also depends on the embedding quality. Sentence-Transformer often works quite nicely despite being task-agnostic. This has been witnessed in many works. But on a larger picture, how we move forward from here remains unclear. On tasks where Sentence-Transformer cannot capture the nuances, what are we going to do?

"Clustering-based data sampling" is a standard practice for reducing computation complexity when the scale of the problem becomes an issue. For example, [Gio: Gradient information optimization for training dataset selection] also implemented the idea of clustering-based data selection.

The work strives for "Flexible optimization objectives" and "Unified and extensible framework". But the development of this paper concentrates on "selecting instruction samples". First, selecting instruction samples is not a particularly computationally intensive task such that many methodologies can be applied. There is now a wealth of paper on this topic. Since the overall size of these instruction-tuning datasets is typically not very large, there has been a constant debate on whether research should focus on "selecting a subset of instruction-tuning examples". Given this being a crowded field, the work could greatly improve its impact if extended to broader applications beyond "instruction mining".
Thank you for carefully reading our rebuttal and providing your feedback. **We greatly appreciate your recognition that our paper does not contain any technical flaws. However, we are concerned about the score of 3, which is typically reserved for papers with significant technical issues or weak evaluations.** Since you acknowledged there are no critical flaws, a score of 3 seems inconsistent with the paper's merit. 

We would like to further clarify the distinctions and advantages of SHED compared to TS-DSHAPLEY:
TS-DSHAPLEY randomly samples multiple subsets of the training data, estimates the contributions of data points in these subsets, and then repeats the process multiple times and aggregates the results to estimate the Shapley values for the entire training set. Notably, this method will calculate the Shapley value for all instances in the training set.

Advantages of SHED:
- **Improved computational efficiency:** SHED only computes Shapley values for representative proxy data samples selected from clusters, rather than for each individual data point as in TS-DSHAPLEY. This dramatically reduces the computational overhead, making SHED more scalable to large datasets. 

- **Model-agnostic data representation:** SHED uses model-agnostic sentence embeddings (e.g., from Sentence Transformers) to represent data samples, while TS-DSHAPLEY relies on representations extracted from the target language model. The model-agnostic approach enhances the transferability of the curated datasets across different language models, amortizing the computational cost of data selection. Table 7 showcases the transfer performance of SHED-curated datasets across different models. SHED is trained on the MMLU and WizardLM datasets using the LLaMA-7B model, and the resulting optimal subsets are used to fine-tune various models, including LLaMA-13B, Vicuna-7B, and GPT-2. The results demonstrate that SHED-constructed datasets exhibit robust performance across a range of models, confirming their applicability across models and even different model families. This strongly supports the transferability of SHED datasets.

- **Clustering-based data sampling:** SHED employs clustering algorithms to group similar data samples and selects representative proxy data for each cluster. This helps capture the diversity and complexity of the original dataset while reducing redundancy. TS-DSHAPLEY does not explicitly consider data diversity in its sampling process.

- **Flexible optimization objectives:** SHED's value function in the Shapley value calculation can be customized for various optimization objectives, such as accuracy and fairness, allowing the curated datasets to align with task-specific requirements. TS-DSHAPLEY focuses primarily on model predictive accuracy.

- **Unified and extensible framework:** SHED presents a unified framework that integrates clustering, Shapley value computation, and data sampling, with the flexibility to accommodate different clustering algorithms, optimization objectives, and sampling strategies. This makes SHED more adaptable to various scenarios than TS-DSHAPLEY.

We hope this clarification helps to further understand the unique contributions and strengths of SHED, and we kindly request reconsideration of the score in light of this information. **The existence of a related but less effective method should not be grounds for rejection.**

Thank you once again for your feedback and consideration.
Thanks for the authors' response. They partially address my question related to the computation time of SHED. I have updated my scores.
Thanks for the responses. I have carefully read the rebuttal and other reviews. I appreciate the authors' effort, yet most of my opinions remain.

I don't think there is a particular, critical flaw in the paper. It is a nice work that I would like to see in some venues, (e.g., as a short paper or at an ACL conference).  Given the existence of similar works such as TS-DSHAPLEY, I don't think it is in the best interest to publish it at NeurIPS.