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 2024 poster (2024)
Keywords: Large Language Models
Data Selection
Shapley Value
Primary area: natural_language_processing

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.

SHED: Shapley-Based Automated Dataset Refinement for Instruction Fine-Tuning
Yexiao Hel
Ziyao Wang'
Zheyu Shen'
Guoheng Sun
Yucong Dai²
Yongkai Wu Hongyi Wang² Ang Li
University of Maryland CClemson University *Rutgers University (yexiache; zi rayww, zyshem, ghsun, angl angliecel @umd edu (yucongd, yongkaw) (elp:)eeeeemmm @clemson. edu hongyi wang. wwwerutgersss.......
Abstract
The pre-trained Large Language Models (LLMs) can be adapted for many down- stream tasks and tailored to align with human preferences through fine-tuning. Recent studies have discovered that LLMs achieve desirable performance with only small amount of high-qualinn data, suggesting that a large portion the data in these extensive datasets is redundant even harmful. Identifying high- quality data from vast datasets to curate small yet effective datasets has emerged critical challenge. In this paper, introduce SHEID, automated dataset refinement framework based on Shapley value for instruction fine-tuning. SHED eliminates the need for human intervention the of commercial LLMs. More- over, the datasets curated through SHED exhibit transferability. indicating they can be reused across different LLMs with consistently high performance. We duct extensive experiments to evaluate the datasets curated by SHED. The results demonstrate SHED's superiority over state-of 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.
Introduction
The development of LLMs marks major leap in machine learning, transforming how we approach natural language processing (NLP) and artificial intelligence (AI) research [ 4 S.. LLMs such as GPT-3 (2) Mistral (6) and L.L.MA/L..MM
3 highlight the benefits of pre-training on large and diverse mixtures of data corpora, empowering these L.MSs with a wealth of knowledge g. Moreover, one of the pivotal strengths of LLMS lies in their adaptability to specific tasks through fine-tuning. Fine-tuning, process that involves adapting LLMs to one multiple task-specific datasets, enables the pre-trained LLM to acquire task-specific information. Furthermore, facilitates the alignment of LLMs to more accurately follow human instructions through fine-tuning on dataset comprised of instructions paired with appropriate responses(9) D which is known as instruction tuning.
However, fine-tuning LLMs also raises challenges. A primary concern that noisy data or harm- ful 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 L.MMs IT 12 13]. Indiscriminately increasing the volume of data lead to ineffeective 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 [1]. Furthermore, fine-tuning LLMs on extensive datasets incurs significant computational costs. The necessity for considerable GPU
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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resources presents a critical challenge [1] computing resources can perform such tasks, limiting broader applications and progress within the I.LM community. Consequently, there is pressing need to design novel method for curating small and high-quality datasets that enable efficient fine-tuning-
Only researchers and institutions equipped with sufficient
Previous efforts have employed various methods such as curation or generation through manual efforts or commercial LLMs [T] [6], identifying subsets from larger datasets via training dynamics or estimating marginal contributions [17 [8]. 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 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-tuming 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) optimization- aware sampling. Initially, the model- agnostic clustering groups embeddings of the original dataset and then selects repre- sentative data samples as 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 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-awee awarei sampling selects data from clusters to compile compact yet high-quality dataset, employing strategies that may favor clusters with higher-quality scores.
Original daraset
Model-agnostic
Prosy-based Shapley calculator
Optimizution- aware sampling
Curated dataset
Clustering
Figure 1: Overview of SHED.
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 redueing 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-qualii 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 I.LMs of different sizes and families. This indicates that smaller models can employed to greatly lower computational expenses for data selection, and the
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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- Automated Dataset Refinement
2 Related Work
2.1
Coreset Selection
Coreset selection plays critical role in machine learning by targeting the selection of 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 25].
eeeertainty-bssel methods choose samples based on the uncertainty they present to the model,
typically engaging samples that the model finds challenging to classify (26) (7 (28) Decision- boundary-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 approaches involve selecting subset that yields similar gradient distributions as the entire dataset when used in training [1 32]. Bilevel Optimization optimizes the coreset selection in way that the selected subset maximizes certain performance metries (33). Dataset Selection with Datamodels using datamodels to approximate how the learning algorithm utilizes different subsets of training data minimize target task loss. [34 35 Submodularity-bssee -basd approaches consider both diversity and information richness, striving for a balanced representation of the dataset (36].
30]. Gradient-matchin
2.2
Data Selection for Instruction Fine-tuning
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 [T6] 37 38 39]. Training-baan baased methods leverage the performance improvement through fine-tuning to score and select instruction data suited for fine-tuning [8 40 41 42 43 45). Some other methods employ commercial LLMs like ChatGPT to assess quality, complexity, and diversity of instructions for selection
13 46
47
48
49).
2.3 Limitations of Previous Work
Most existing methods for data selection overlook the impact of various data combinations on model performance. As Table [ illustratee, 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 [88 also utilized Shapley value for data selection, SHED offers several distinct advantages. SHED computes Shapley values only for proxy data of clusters rather than cach 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 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 more widely applicable.
3 Proposed Method
Motivated by the aforementioned challenges. we present SHED, a generic framework that exploits Shapley value identify and select high-quality data improve the performance and efficiency of fine-tuning LLMs.
3.1
Preliminary
The motivation behind this work underscored by the observation, as illustrated in Table I that naively aggregating high-quality data merely based on the independent importance of individual
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Table I: We apply DSIR [50 to compile a high-quality dataset (10k instances), a random dataset (10k instances) from MMLU, and a mixed dataset samples 5k instances from each of the high-quality and random datasets. We fine-tune the LLaMA-7B model them using the MMLU test set.
B on the curated dataset and evaluate
Dataset
High-quality
Random
Mixed
MMLU
40.04
39.13
40.92
samples does not guarantee a performance improvement of fine-tuning. We believe this phenomenon 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. quantifies the marginal contribu- tion 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:
E PEDU()
LPN(D - |P 1)1 DI!
(M(PUs) - -o()),
=
(I)
where S₁ is the Shapley value of i, Pss the subset of dataset D. DD) and |P| are the total number of instances in and P. ((P) the value function of P, which represents the performance of the LLM on the subset P. As Eq. [ indicatess the Shapley value of an instance i captures its average impact on model performance across all subsets it might be part of. This ensures 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.
model
Additionally, the value function v( ()) in Eq. 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. I. 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 combination, two evaluations are needed, i.e., one includes 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 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[ 2 2!.).. This exponential complexity makes direct computation of Shapley values impractical for large datasets.
possible combinations. For each
(2))..
3.2 Design of SHED
To address the above challenges, we design SHED, comprising of three key components: model- agnostic clustering, proxy-based Shapley calculator, and optimization-awee -ware 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 (5T) 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 transfcrability of the curated dataset, demonstrated in T 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.

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Proxy-based Shapley Calculator. To further improve efficiency for Shapley value calculations, the proxy-based Shapley calculator employs an upproximation method to estimate the Shapley values of the proxy data. This method iteratively removes groups of n instances from the proxy data Dp- followed by an evaluation of the model's 8 performance to assess the impact of these instances. The performance variations before and after the removal of specific group of instances quantify their collective contribution. Specifically, the contribution of the initial group of n instances, denoted as D,....))E,,* is computed by C(I..n)eD, vD,)) v(,, {1.m))). Similarly, the contribution for the subsequent group of instances is determined by c(x+11.22)) This procedure repeated, progressively removing groups of n instances until the entire proxy data has been visited, which marks the completion of single iteration. This entire iteration process is then repeated times to enhance the accuracy of the approximation. After completing & iterations, the Shapley value for certain instance of the proxy dataset is approximated using the average of its contributions across all iterations, defined as S F Σ ci(k) where (k) denotes the contribution associated with instance i in the kth iteration.
"( (1..n)) - -))))=(())) (D,, (1..2n)).
m
Optimization-awan Sampling. The Shapley value of each proxy data is assigned the qual- ity score of the correspond- ing cluster. Optimization- aware sampling utilizes these quality scores sample data from these clusters, aiming to curate small yet high- quality dataset. Optimization- aware Sampling offers two sam- pling methods: Quality-Ordered Ordered Cluster Sampling (QOCS) and Quelity-Weighted Cluster Sam- pling (QWCS). QOCS prioritizes sampling from clusters with the highest quality scores. selects instances starting from the most high-quality clusters until a pre- defined target sampling number reached. QWCS adopts prob- abilistic approach to sample instances across all clusters, with the probability of selection from given cluster weighted by its quality score. This method aims to balance quality with diversity by allowing for the inclusion of instances from 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 is defined in Eq.
Original Dataset
Model-igmostic Clustering
Pruxy-Based Shapley Calculator
Score
score
Optimization-Awarr Sampling
Sclected Dataset
score
Figure 2: Workflow SHED: Clustering and determining proxy data; @ Calculating Shapley values as scores; @ Sammling based on scores; and 4 Forming the selected dataset.
f8:
Pr(1)
E.
where S; represents the quality score of cluster i, and f is 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.
Experiments
4.1 Experimental Setup
Datasets. We conduct experiments on two famous benchmark datasets, MMLU (99.8k instances) 54 and -onadddLttt----sttttett---- (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 set as the accuracy of the foundation model fine-tuned on the proxy data. We use LLaMA-7B (3] as the
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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 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 conducted for one epoch. For optimization-awar 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, 0,0, 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-sammaa sommom-n method, denoted as RS, which randomly selects subset from a large dataset. We also use the Dataset Quantization method [38]. denoted by DQ. and the Data Selection with Importance Resampling [SO], 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
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 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 Im-eva I uut on-ha coi-harness rness 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 A 100 GPUs, each with 80GB of memory.
4.2 Experiment Results
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, 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 referred to as the best-selected dataset.
Table 2: Performance comparison of curated datasets of the same size by SHED and baseline methods.
Original dataset
MMLU
Wizardl M
Method
RS
DQ
DSIR
QOCS
QWCS
RS
DQ
DSIR
QOCS
QWCS
MMLU
38.94
39.88
40.24
44.80
43.87
33.12
33.20
33,86
35.43
34.91
ARC-challenge
45.10
46.35
45.67
47.10
47.23
46.01
48.71
47.66
49.47
49.92
Table 3: Performance of the best-selected datasets of SHED and baseline methods on the MMLU
task.
MMLU
WizardLM
QOCS
44.80 (10k)
35.92 (4k)
QWCS
44.24 (13k)
35.76 (9k)
RS
40.87 (15k)
34.33 (7k)
DQ
43.50 (7k)
33.97 (7k)
DSIR
40.23 (13k)
34.72 (10k)
Full
45.56 (99.8k)
33.16 (70k)
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 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 shows the evaluation results for the MMLJ 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

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Table 4: Performance of the best-sclected datasets of SHED and baselines on the ARC-challenge
task.
MMLU
WizardI.
QOCS
47.10 (10K)
51.36 (1k)
QWCS
49.21 (9k)
50.26 (7k)
RS
47.07 (13K)
49.33 (16k)
DQ
46.50 (3k)
50.24 (5k)
DSIR
46.90 (3k)
48.78 (12k)
Full
45.99 (99.8k)
47.95 (70k)
Table MT-Bench evaluation of the best-selected datasets SHED and baselines.
Original dataset
MMLU
WizardLM
Method Size
Full
RS
QOCS
RS
QWCS
Full
RS
QOCS
RS
QWCS
99.8k
10k
10k
13k
13k
70k
4k
4k
9k
9k
LLaMA-7B
3.02
2.23
2.53
2.44
2.83
5.21
4.77
4.89
4.81
5.24
when utilizing the WizardLM dataset. It is notable that SHED-QOCS outperforms the full dataset, achieving 2.76% higher accuracy. In Table 4 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 perforrerfommece datasets.
Evaluations on MT-Bench. We use MT-Bench 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 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 remarkable score of 5.24 the MT-Bench. The results presented in Table represent the average of five independent runs.
Transferability Evaluation of Curated Datasets across Various Models. To evaluate the trans- ferability 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 ALaMA-13B, Vicuna-7B, and GPT-2 using the best-selected dataset curated by SHED and the baseline methods. As summarized in Table 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.
Acuracy
Acoencyy
Time Doond
40
OWCS
COCS
33
OWCS DOCS
4500
3000
600
(a) Subsets selected from MMLU, (b) Subsets selected from Wiz-(c) Computational time for one iter- ation of Shapley value calculation.
ardLM.
Figure 3: Performance of subsets with varying numbers of clusters in SHED.
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Table 6: Transferability evaluation using the best-selected datasets across different models MMLU
task.
Original dataset
MMLU
WizardLM QOCS
Method
Full
R$
QOCS
RS
QWCS
Full
RS
RS
QWCS
Size
99.8k
10k
10k
13k
13k
70k
4k
4k
9k
9k
LLaMA-138
53.22
50.04
52.95
50.12
51,54
45.63
45.77
45.93
45.81
46.36
VICUNA-7B
49.70
48.43
50.01
47.21
48.93
45.56
45.71
47.19
45.44
48.16
GPT-2
24.22
23.74
26.89
24.33
25.83
26.19
25.07
26.76
24.85
25.77
Table 7: Transferability evaluation using the best-selected datasets across different models on ARC-
challenge task.
Original dataset
MMLU
WizardLM
Method
Full
RS
QOCS
RS
QWCS
Full
RS
QOCS
RS
QWCS
Size
99.8k
10k
10k
13k
13k
70k
4k
4k
9k
9k
LLMMA-138
49.31
47.31
50.43
48.83
50.68
54.09
53.17
55.20
54.11
55.63
VICUNA-7B
44.88
44.86
45.23
43.24
44.91
49.91
47.72
50.26
47.98
48.72
GPT-2
19.45
18.77
19.81
19.02
20.05
19.19
17.98
19.28
18.72
19.54
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 's representativeness for its respective clusters. However, this comes at the cost of increased computational overhead, highlighting balance that must be struck 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 baseline cluster count of = VID We present the computation time for Shapley value computations across different settings, maintaining consistency with the experimental setup outlined Section 4.1
As Figure (aa) and Figure 3(b) show, the results reveal that performance improvements of curated dataset reach a plateau when the number of clusters exceeds 3 // Mcanwhile, Figure 3(c) demonstrates 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 clusters itself. Given the transferability of datasets curated using the SHED, it is feasible to employ 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 practical approach in real-world settings.
45
36
44
Accurscy (%6
33
Accuncyy
43
24
QWCS
QWCS
42
QOCS
10
20
25
30
5
10
15
20
25
30
Namber iterations
Number of iterations
Subsets selected from MMLU.
(b) Subsets selected from WizardL..
Figure 4: Performance of subsets with varying iterations in SHED.
Impact of Number of Iterations on Proxy-based Shapley Calculator. 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 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

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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 ((a and 4bb) 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.
Discussion
5.1 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 small dataset while improving specific objectives, such as model fairness [61].
In particular, we aim to curate a dataset using the common fairnsss notion, iee. demographic parity, we can define PP) the disparity in positive prediction rates between groups with protected attributes (e.g.. males vs. females), calculated as the negative absolute difference MaXMale Xtematel where XMale and Femate the positive prediction rates for male and female groups, respectively.
5.2 Complexity Analysis
We assume that the running time required to fine-tune the model using a single instance is denoted by and the time needed to evaluate the model on test set consisting m instances is represented by Tm. Let C' denote the number of clusters, denote the number of instances within a group and k signifies the number of iterations utilized in the proxy-based based Shapley calculator as illustrated in Section The total number of evaluations and fine-tuning per iteration would be proportional to C. For simplicity, we assume that C' is evenly divisible by n for simplicity. Given & iterations, the
n
overall time complexity of this approximation method can be expressed as O Ck [C+n]t
+ Tm
2
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 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. Sigmificantly, SHED- curated 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 '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 is 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-agnostcc laying the groundwork for incorporating additional criteria. In subsequent research, we plan to integrate considerations of model fairness alongside performance.
8 Ethics Statement
In this work, we present SHED, generic data refinement framework utilizing Shapley values, aimed at assembling compact yet effective dataset to boost the efficiency of the fine-tuning process of

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LLMs. This study carefully avoids ethical issues beyond standard Al 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 Al field.
Acknowledgements
We thank the anonymous reviewers for their valuable insights and recommendations, which have greatly improved our work. This research has been graciously funded by NSF 2431611.
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Appendix
A Hyperparameter Settings and Experimental Configuration
In our experiments, we employed the following hyperparameters: number of training epochs was set to 3, the batch size was 128, the LoRA rank (lora_r) was 128, and the LoRA al- pha (lora_alpha) was 256. For clustering, when the number of clusters (C) was 3000, g (n)
60; when testing the impact of differ- ent C values on performance, π 50 The number of iterations for Shapley value calcula- tion (k) was 10, and the learning rate was 3 x 10 Data preprocessing involved using the /s/alllMiinn./ Li--niiM------- model to generate semantically meaning- ful embeddings for each sample in the original dataset, followed by applying the k-means algorithm cluster these embeddings.
the number of samples removed per group
B Comparison of the SHED-QOCS and SHED-QWCS
We compared the performance of datasets varying sample sizes using SHED-QOCS and SHED- QWCS methods. For fine-tuning, we utilized the LLaMA-7B model. Other experimental configura- tions were aligned with the parameters detailed in Section 4.1
QDCs
Narber of Iiitaaes
Namber
(a) Original dataset: MMLU. Test set: MMLU
(b) Original dataset: MMLU. challenge
Test set: ARC-
OOCS
0OCS
OWCS
QWCC
Nunberofleszsooe
Naoier oChanness
(c) Original dataset: WizardLM. Test set: MMLU (d) Original dataset: WizardLM. Test set: ARC- challenge Figure 5: Results of curated datasets of different samples.
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, observed that datasets that achieve the best performance are often those sampled via SHED- This improved performance is observed when the sample size is sufficiently large, allowing SHED- to sample data from a wider range of clusters. The inherent strength of SHED-QOCS lies in its strategic focus 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.
quality
Therefore, we suggest that users select between these two sampling methods based on the sample size they desire.
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C Hyperparameter Tuning for Shapley Value Computation
To enhance user convenience, SHED introduces method for setting hyperparameters.
Based on Figure {(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 for Shapley values be modeled as OkC. SHED randomly samples 2000 instances from the dataset to calculate the Shapley value for one iteration, recording this to determine To optimize to be close to 10 and the number of clusters near 3 /DD an optimization problem is formulated in Eq.
min C.C
A A1 - 10)² + A2(C 3 D))
s.L. OkC to
where Å and A2 serve as weights, both defaulting to 1, while t0 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.
D Comparison with Other Methods
LLaMA-7B is We compare SHED with two methods, LIMA and IFD (using WizardLM) 12 40
used 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 Ik samples). It is important to note that SHED is fully automated, whereas LIMA involves manual curation.
Task
SHED (1k)
LIMA (1k)
MMLU
41.71
34.9
ARC-challenge
48.12
46.16
Table 8: Performance comparison between SHED and LIMA.
We compare SHED with IFD (with 7k samples) to select data from WizardLM. The following table shows the results.
Task
SHED (1k)
SHED (7k)
IFD (7K)
MMLU
33.62
35.63
33.08
ARC-challenge
51.36
50.11
52.90
Table 9: Performance comparison between SHED and Cherry-LLM (using WizardLM).
As the results demonstrate, SHED achieves competitive performance across both tasks, even with fewer samples.
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NeurIPS Paper Checklist
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.
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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 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.
is fine to include aspirational goals as motivation as long as is clear that these goals not attained by the paper.
2. Limitations
Question: Does the paper discuss the limitations of the work performed by the authors?
Answer: [Yes]
The paper has dedicated "Limitations" section where it discusses the reliance on representative embeddings, which may reduce applicability scenarios lacking such embeddings. also discusses the potential oversight of rare samples in clustering and the risk of model bias due to primary focus on performance.
Justification:
Guidelines:
The answer NA means that the paper has 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 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 few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.
The authors should reflect the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution low or images are taken in low lighting. Or 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 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, 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 impor- tant role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed not penalize honesty concerning limitations.
3. Theory Assumptions and Proofs
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Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
Answer: [NA]
Justification: The paper does not focus on theoretical results but rather on the implementation and empirical evaluation of SHED.
Guidelines:
The answer NA means that the paper does not include theoretical results. All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
All assumptions should be clearly stated or referenced in the statement of any theorems.
The proofs can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors are encouraged provide short proof sketch to provide intuition.
Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.
Theorems and Lemmas that the proof relies upon should be properly referenced.
4. Experimental Result Reproducibility
Does the paper fully disclose all the information needed to reproduce the main ex- perimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?
Question:
Answer: [Yes]
The experimental setup, including datasets, baseline methods, and evaluation settings, is described in detail in the paper (Section Experiments and Appendix A), making it possible to reproduce the main results.
Justification:
Guidelines:
The answer NA means that the paper does not include experiments.
If the paper includes experiments, a No answer to this question will not perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.
If the contribution is dataset and/or model, the authors should describe the steps taken make their results reproducible verifiable.
Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is a novel architecture, describing the architecture fully might suffice, or if the contribution be necessary to either make it possible for others to replicate the model with the same dataset, or provide access to model. In general. releasing code and data often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g., in the case of large language model), releasing of a model checkpoint, other means that are appropriate to the research performed.
specific model and empirical evaluation, may
While NeurlPS does not require releasing code, the conference does require all submis- sions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example
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model architecture, the paper should describe
(c) the contribution is a new model (c.g., a large language model), then there should either be way to access this model for reproducing the results or way to reproduce the model (e.g., with an open open-source dataset or instructions for how to construct the dataset).
(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome describe the partieular way they provide for reproducibiliity In the case of closed-source models, it may be that access to the model is limited in
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some way (e.g., to registered users), but it should be possible for other researchers have some path reproducing or verifying the results.
5. Open access to data and code
Question: Does the paper provide open access to the data and code, with sufficient instruc- tions faithfully reproduce the main experimental results, as described in supplemental material?
Answer: [Yes]
Justification: 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.
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The answer NA means that paper does not include experiments requiring code.
Please see the NeurIPS code and data submission guidelines (https://nips) cc/
publ ic gu des wteeeeaoaaace Codeeulomeiiiece ss onPoli ccy for more details.
While we encourage the release of code and data, we understand that this might be possible, so "No" is an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for new open-source benchmark).
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Providing as much information as possible in supplemental material (appended to the paper) recommended, but including URLs to data and code is permitted.
6. Experimental Setting/Details
Question: Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
Answer: [Yes]
Justification: We have submitted the code and data supplemental material. All code associated with the collection of high-quality datasets curated by SHED is open-sourced. The details can also be found in the Experiments section and Appendix.
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The experimental setting should be presented in the core of the paper to a level of detail that is necessary to appreciate the results and make sense of them.
The full details can provided either with the code, in appendix, or as supplemental material.
7. Experiment Statistical Significance
Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Question:
Answer: [Yes]
Justification: The paper reports experimental results as averages over multiple runs, which provides level of statistical significance. While emro bars confidence intervals not explicitly mentioned, the use of multiple independent runs helps to demonstrate the reliability and consistency of the results.
Guidelines:
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The answer NA means that the paper does not include experiments.
The authors should answer "Yes" if the results are accompanied by enror bars, confi- dence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper.
The factors of variability that the error bars capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall
with given experimental conditions).
The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)
The assumptions made should be given (e.g.. Normally distributed errors).
It should be clear whether the error bar is the standard deviation the standard error of the mean.
is OK to report I-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have 96% CI, if the hypothesis of Normality of errors is not verified.
For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error that would yield results that are out range (e.g. negative error rates).
If error bars reported in tables plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.
8. Experiments Compute Resources
For each experiment, does the paper provide sufficient information on the puter resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?
Question:
Answer: [Yes]
The paper details the computational resources used, mentioning the experi- ments were conducted on two A100 GPUs, each with 80GB of memory, which provides clarity on the computational requirements. Additionally, the paper includes experiments to estimate the runtime under different hyperparameter settings, providing insights into the computational efficiency and resource requirements of the proposed method.
Justification:
Guidelines:
The answer NA means that the paper does not include experiments.
The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.
The paper should provide the amount of compute required for each of the individual experimental runs as well as estimate the total compute.
The paper should disclose whether the full research project required more compute than the experiments reported the paper (e.g., preliminary or failed experiments that didn't 't make it into the paper).
9. Code Of Ethics
Question: Does the research conducted in the paper conform, in every respect, with the NeurlPS Code of Ethics http://tww./...o....c.c.//attttt
Answer: [Yes]
Justification: The paper includes an Ethics Statement section where it discusses the careful avoidance of ethical issues beyond standard Al concerns, ensuring adherence to ethical data use standards and reflecting the commitment to responsible research practices the AI field.
Guidelines:
The answer NA means that the authors have not reviewed the NeurlPS Code of Ethics.
If the authors answer No, they should explain the special circumstances that require deviation from the Code of Ethics.
The authors should make sure to preserve anonymity (e.g., there a special consid- eration due to laws or regulations in their jurisdiction).
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10. Broader Impacts
Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [Yes]
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 the curated datasets across
computational costs. It also highlights the transferability 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), fairesss 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 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 (c.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how system learns from feedback over time, improving the efficiency and accessibility ML).
11. Safeguards
Question:
Does the paper describe safeguards that have been put in place for responsible release of data 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 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 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 challenging, and many papers do not require this, but we encourage authors to take this into account and make best faith effort.
12. Licenses for existing assets
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?
Question:
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Answer: [Yes]
Justification: The paper uses publicly available benchmark datasets (MMLU and WizardLM) and cites the original papers that introduced these datasets. The licenses and terms of use for these datasets are respected, and the paper provides references to the original sources.
Guidelines:
The answer NA means that the paper does not use existing assets.
The authors should cite the original paper that produced the code package dataset.
The authors should state which version of the asset used and, possible, include URL.
The name of the license (e.g., CC-BY 4.0) should be included for each asset.
For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.
If assets released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, papersw thcode. com / da setss has curated licenses for some datasets. Their licensing guide help determine the license of dataset.
For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.
If this information is not available online, the authors are encouraged to reach out to the asset's 's creators.
13. New Assets
Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer: [Yes]
Justification: The paper introduces curated datasets using the SHED framework. All code and datasets are open-sourced, including proper documentation ensure that other researchers can understand and utilize the assets effectively. The paper provides detailed information about the datasets, the methodology used for their creation, and the associated code.
Guidelines:
The answer NA means that the paper does not release new assets.
Researchers should communicate the details of the dataset/code/model part of their submissions via structured templates. This includes details about training, license, limitations, etc.
The paper should discuss whether and how consent was obtained from people whose asset is used.
At submission time, remember anonymize your assets (if applicable). You either create an anonymized URL or include an anonymized zip file.
14. Crowdsourcing and Research with Human Subjects
Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given participants and screenshots, if applicable, as well as details about compensation (if any)?
Answer: [NA]
Justification: The paper does not involve crowdsourcing experiments or research with human subjects.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing research with human subjects.
Including this information in the supplemental material is fine, but if the main contribu- tion of the paper involves human subjects, then as much detail as possible should be included in the main paper.
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According to the NeurlPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.
15. Institutional Review Board (IRB) Approvals or Equivalent for Research with Human Subjects
Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or equivalent approval/teview based on the requirements of your country institution) were obtained?
Answer: [NA]
The paper does not involve crowdsoureing research with human subjects, and therefore, IRB approval or equivalent is not applicable.
Justification:
Guidelines:
The answer NA means that the paper does not involve crowdsoureing nor research with human subjects.
Depending the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
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 the institution conducting the review.
22


Reviews:
[{"review_id": "hBHLlAKn9s", "reviewer": "Reviewer_kWYC", "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.", "questions": "Why are the different data selection methods compared with different final selected dataset sizes?\nHow were the training hyperparameters chosen?", "limitations": "The authors have sufficiently discussed the limitations of their work.", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "- The topic of the paper is well-motivated. It is known that large models need high quality data during the fine-tuning process, and are sensitive to noisy data in this stage. High quality data can be prohibitively expensive to collect\n- Shapley values are a known and well-studied method to asses the importance of training points to a model’s predictions\n- Unlike previous data selection methods employing Shapley values (TS-DSHAPLEY), shed uses clustering and representative sample selection to significantly reduce the computational cost of data subsection. SHED also considers different target objectives (e.g. fairness), although it is not clear how easy it would be to adapt other methods to this objective. This is an interesting novelty that can help make shapley values more practically useful in this data selection setting.\n- Experiments are conducted on good language model eval datasets (e.g MMLU)\n- Results show that datasets filtered via SHED achieve improved performance compared to other sampling methods, and also compared to full dataset fine-tuning\n- Results are shown across a variety of evaluation datasets.", "weaknesses": "- 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.\n- 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\n- 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.\n- 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?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for the valuable feedback. We are encouraged by the positive comments on our well-motivated, clearly presented, and highly effective work. Below are our responses to the thoughtful questions and constructive suggestions:\n\n**W.1:**\n\nWe sincerely thank the reviewer for highlighting this issue and the reference to DsDm. We acknowledge that our original manuscript overlooked DsDm, which shares similarities with our approach in using the impact of data points for selection. However, like other works, DsDm does not consider the combinatorial effects of data points in the same way that SHED does. The key differences are:\n\n- Individual Impact: DsDm primarily focuses on estimating the impact of individual data points on model performance. It uses data models to approximate how each training example affects the model's predictions on target tasks.\n\n- Linear Approximation: DsDm's data models are typically implemented as linear functions, which implicitly assume that each data point's contribution is independent of other points in the dataset.\n\nIn contrast, SHED's use of Shapley values allows it to consider the marginal contribution of data points, **thereby capturing potential interaction effects between data points**.\n\nWe will revise our manuscript to include a detailed discussion of DsDm and clearly articulate SHED's unique contributions.\nWe appreciate this feedback, which will significantly improve our paper's comprehensiveness and scholarly value.\n\n**W.2:**\n\nWe appreciate the reviewer's observation regarding SHED's use of proxy data points. Indeed, the effectiveness of this approach is influenced by the quality of embeddings and clustering parameters. We'd like to address these points:\n\n- **Sensitivity Analysis:** As detailed in our paper, we have already conducted a sensitivity analysis on the number of clusters. Figure 3 demonstrates how performance and computational time vary with different cluster numbers. This analysis shows that performance improvements plateau when the number of clusters exceeds 3√|D|, providing empirical guidance for choosing this parameter.\n\n- **Embedding Quality:** We use Sentence Transformers for generating embeddings, which are known for their ability to capture semantic similarities between sentences. By clustering on these embeddings, we group semantically similar text, which helps in discovering and organizing potential patterns and relationships in the text data, enhancing the representativeness of our proxy points.\n\nWhile our current approach has shown robust performance, we acknowledge the importance of this aspect. In our future work, we plan to further investigate the impact of different embedding techniques and clustering methods on SHED's performance. We thank the reviewer for this valuable feedback, which will help us improve our work.\n\n**W.3:**\n\nWe appreciate the reviewer's insightful comment on our experimental methodology. We'd like to clarify our approach and reasoning:\n\n**Fairness in Comparison and Focus on Data Impact:** Our decision to use the same hyperparameters across all methods was deliberate, aiming to ensure a fair comparison that isolates the impact of data selection on model performance. This approach allows us to directly observe how different data selection methods affect performance without the confounding influence of varying hyperparameters. \n\nWe acknowledge that our current approach of using fixed hyperparameters across all methods may not fully capture the optimal performance of each method.\n\nTo address this concern, we propose the following improvements for our revised manuscript:\n\n- Hyperparameter Sweep: We will conduct a hyperparameter sweep for each method, including learning rate, batch size, and number of epochs.\n\n- Best Configuration Reporting: We will report results on the test set using the best hyperparameters found for each method during the sweep.\n\nWe believe these additions will strengthen our experimental methodology. We thank the reviewer for this suggestion, which will enhance the reliability of our results.\n\n**W.4:**\n\nWe appreciate the reviewer's observation regarding the varying data sizes used in our experiments. We acknowledge that this may have caused some confusion in interpreting our results. To address this concern:\n\n- **Comprehensive Comparison:** To ensure a fair comparison, **we have included results for a fixed data size of 10k samples across all methods in Table 2**. Additionally, as detailed **in Appendix B of our paper, we have conducted experiments comparing different methods across various sample sizes**. These provide a comparison of method performance when using identical amounts of data.\n\n- **Method-Specific Optimization:** In Appendix B, we provide comprehensive comparisons of different methods across various sample sizes. Our analysis reveals that QOCS often performs better with smaller subsets. This is likely because it prioritizes the highest quality, most informative data, making it particularly effective when working with limited data. On the other hand, QWCS often benefits from slightly larger subsets. This method balances quality with diversity, allowing for the inclusion of a broader range of data patterns. As a result, QWCS can capture more comprehensive representations of the dataset. These characteristics explain the different optimal subset sizes for each method. By presenting the best-performing subsets in the main text, we aim to demonstrate the full potential of each approach.\n\nWe will revise our manuscript to more clearly explain our rationale for presenting the best-case scenarios and to direct readers to the appendix for these fixed-size comparisons.\n\nWe thank the reviewer for this feedback, which will help us improve the clarity of our results presentation.\n\n**Q1:**\n\nPlease see the responses R.3 and R.4. We have also presented all the training hyperparameters in Appendix C.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ubHBSa59xu", "reviewer": "Reviewer_Df9d", "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.", "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?\n2. How will the selection of initial clustering groups affect the final model performance?", "limitations": "No potential negative societal impact is observed.", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. The studied problem is critical as the proposed method is general to creat small yet high-quality datasets that benefit LLM finetuning in terms of performance improvement and computational cost reduction.\n2. The experiments are extensive by covering multiple datasets and fine-tuning tasks.", "weaknesses": "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.\n2. As authods illustrated, the reliance on clustering may inadvertently reduce data diversity, which results in the overlook of rare but important samples.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We’d like to thank the reviewer for the insightful and positive feedback. We are encouraged that the reviewer found our work meaningful, novel, and effective. For the thoughtful questions and constructive suggestions. We'd like to share our responses below.\n\n**W.1:**\n\nWe appreciate the reviewer's observation regarding the efficiency of SHED compared to classical Shapley value calculation methods. In our paper, **we provide a detailed analysis of SHED's computational complexity and actual runtime. As shown in Figure 3 (c), we present the computational time for Shapley value calculations across different cluster numbers. This graph clearly illustrates how SHED's runtime scales with the number of clusters, providing concrete evidence of its computational efficiency.**\n\nIn contrast, for the classical Shapley value calculation method, the time complexity is exponential in the size of the dataset. Given our dataset size is about 100,000 instances, this results in a computational cost that is prohibitively large. As a result, it is not feasible to provide actual runtime measurements for the classical method on our dataset. However, we can analyze the efficiency of SHED compared with the classic Shapley value method under the experimental settings of this paper.\n\n**For the classical Shapley value calculation** method with a dataset of |D| = 100,000 instances: We need to consider all possible subsets (2^|D|). For each subset S, we fine-tune the model (taking time |S|t, where |S| is the size of the subset) and evaluate it on the test set (taking time T_m). Therefore, **the time complexity would be:\nO(2^|D| * (|D|t/2 + T_m)) = O(2^100,000 * (50,000t + T_m))\nThis is computationally infeasible.**\n\nIn contrast, **SHED's complexity, as detailed in our paper’s discussion section, is:\nO((Ck/n)[(C+n)t/2 + T_m]) = O(500 * [1530t + T_m]).\nThis demonstrates a significant reduction in computational complexity compared to the classical method.**\n\n**W.2:**\n\nWe appreciate the reviewer's insightful observation regarding the potential limitation of our clustering approach in SHED. \n\n**Our use of clustering and proxy data points is a trade-off we make to significantly reduce computational complexity while still capturing the overall data distribution effectively.**\n\nHowever, **we have taken steps to mitigate the overlooking issue.** The Quality-Weighted Cluster Sampling (QWCS) variant of our method allows for more diverse sampling. As shown in our experiments, despite this potential limitation, SHED still outperforms other methods across various tasks, suggesting that **the benefits of our approach outweigh this drawback in practice.**\n\nNevertheless, we agree that this is an important area for improvement. In our future work, we plan to explore more sophisticated clustering algorithms and sampling strategies that could better preserve rare but important samples. We thank the reviewer for highlighting this important aspect, which will help us improve our method.\n\n**Q.1:**\n\nWe appreciate the reviewer's question about evaluating SHED on industry-level datasets. We'd like to address this point:\n\n- **Scalability:** SHED's design, particularly its use of clustering and proxy data points, makes it **inherently scalable to larger datasets. By setting the ratio C/n to a fixed value, as done in the experimental setup, the computational complexity of SHED grows linearly with the number of clusters**, not exponentially with the dataset size like traditional Shapley value calculations.\n\n- **Current Limitations:** Our current experiments were constrained by the computational resources available to us in an academic setting. Our current computational resources are limited and insufficient to conduct experiments on industry-scale datasets within a short timeframe.\n\nIn the future, **we plan to rent additional server resources to apply SHED on much larger datasets. We are committed to open-sourcing the curated datasets from these experiments, contributing valuable resources to the research community. These efforts will further validate SHED's effectiveness at industrial scales and support broader research.**\n\nWe thank the reviewer for this suggestion, allowing us to emphasize our commitment to contributing to the research community despite current resource constraints.\n\n**Q.2:**\n\nWe appreciate the reviewer's insightful question about the impact of initial clustering on SHED's performance. \nThe initial clustering step in SHED plays a significant role as it determines the proxy data points used for Shapley value calculation. Our method employs several strategies to ensure robustness to variations in initial clustering:\n\n- **Semantic-preserving embeddings:** We use Sentence Transformers to generate embeddings, which effectively capture semantic similarities across various domains. This helps ensure that semantically similar data points are likely to be clustered together, even if the exact cluster boundaries may vary.\n\n- **Multiple initializations:** In fact, we conducted multiple experiments with different random initializations to verify the robustness. **We found that the number of clusters is a factor that affects the performance.** Therefore, we conducted a sensitive analysis of the number of clusters. Our sensitivity analysis (Figure 3) shows that performance improvements plateau when the number of clusters exceeds 3√|D|. This suggests that **as long as we choose a sufficiently large number of clusters, the exact choice is less critical.**\n\n- **Shapley value calculation:** This step considers the contribution of each proxy data point across multiple subsets, which helps to average out some of the variability introduced by clustering.\n\n- **Optimization-aware sampling:** The final selection step considers the Shapley values of all clusters, providing another layer of robustness against individual cluster variations.\n\nWe thank the reviewer for this valuable question, which helps us clarify the robustness of SHED.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "QKkeuxcOFe", "reviewer": "Reviewer_tj1t", "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.", "questions": "See Weaknesses.", "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.\nAlso, 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.", "rating": 3, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 1, "strengths": "The paper is clearly written and very easy to read. The problem is well-contextualized and well-formulated.\n\nThe structure is complete and the language is neat.\n\nEvaluations are standard and a number of baselines are considered.", "weaknesses": "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.\n\n1. 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.  \n\nIn 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.\n\n2. 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.\n\nYet, 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).\n\n3. 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.\n\n4. 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.\nAlso, 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.\n\n5. 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.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for the valuable feedback. \n\n**W.1:**\n\nWe appreciate the reviewer's concern but note a possible misunderstanding about SHED.\n\nSHED does consider the combinatorial effects between clusters:\n\n- **Combinatorial Effects:** SHED calculates Shapley Value (SV) for cluster proxies, reducing computational cost while capturing inter-cluster relationships. Each cluster is represented by a proxy data point (closest to the centroid). **SVs are calculated by considering possible combinations of these proxies, thus considering interactions between clusters.** SV compresses the combinatorial space into a single score, encapsulating individual importance, synergies, and potential redundancies.\n\n- **Score-based Selection:** The selection step uses these scores, which already encapsulate combinatorial effects. **A high SV indicates a high likelihood of positive impact when combined with instances from other clusters.**\n\nGrid search for data selection incurs high costs.\nIf there are C clusters and sampling ratio takes x values, with training time T. Grid search’s complexity is O(Tx^C), scaling exponentially with C, which is impractical.\n\nIn contrast, Shed’s complexity is O((Ck/n)[(C+n)t/2 + T_m]). As Figure 3 shows, SHED can scale linearly with the number of clusters by setting C/n to a fixed value, making it feasible for large datasets. Thus, SHED offers a significant computational advantage over grid search.\n\n\n**W.2:**\n\nWe appreciate the reviewer’s concern. To address this, we compared SHED with LIMA and Cherry-LLM:\n\n**LIMA:**\n- MMLU: SHED (1k) - 41.71 vs. LIMA (1k) - 34.9\n- ARC: SHED (1k) - 48.12 vs. LIMA (1k) - 46.16\n- Note: SHED is fully automated, unlike LIMA’s manual curation.\n\n**Cherry-LLM (using WizardLM):**\n- MMLU: SHED (1k) - 33.62, SHED (7k) - 35.63 vs. Cherry (7k) - 33.08\n- ARC: SHED (1k) - 51.36, SHED (7k) - 50.11 vs. Cherry (7k) - 52.90\n\n**SHED achieves comparable or better performance with less data.** Since SHED used MMLU as test set during data selection, its performance on ARC could be improved if used as test set during selection, showing SHED's effectiveness for task-specific data selection.\n\nBaseline Criteria:\n\nWe use DSIR because it, like SHED, selects data by importance. DSIR samples data based on estimated importance, similar to SHED's use of SVs. We included DQ as it was also used for fine-tuning in its paper.\n\nWe avoided methods requiring human or LLM evaluations due to high costs.\n\n\n**W.3:**\n\nWe appreciate the reviewer’s concern about SHED's computational cost and would like to clarify:\n\n**Transferability & Cost Amortization:**\n\n- **One-time Computation for Multiple Uses:** SHED-refined datasets perform well across various model sizes and architectures. This transferability makes them reusable for different models or tasks. **This one-time computational investment ensures long-term efficiency, reducing the need for repeated data selection. SHED's amortized cost over several models significantly lowers the per-use cost.**\n\n- **Comparison with Non-transferable Methods:** Unlike methods requiring computation for each new model or task, SHED's transferability **eliminates recurrent computational expenses.**\n\n- **Efficiency Gains:** SHED-refined datasets lead to comparable or superior model performance with smaller datasets, directly translating to lower computational and time costs in the fine-tuning phase.\n\n**Efficiency Design:** SHED reduces the computational cost by approximating SVs for cluster proxies instead of individual data points. Our analysis and experiments show its complexity can grow linearly with the number of clusters, not exponentially with dataset size.\n\n**Benchmark:** Figure 3 (c) has shown the time for SV calculations across different numbers of clusters, which also indicates SHED’s scalability.\n\n\n**W.4:**\n\nWe appreciate the reviewer’s comment but believe there may be a misunderstanding of our claims:\n\n- **Clarification of Objectives:** As highlighted in our paper, **SHED focuses on fine-tuning.** However, Figure 3 (c) shows linear growth in time, suggesting potential for large-scale pre-training data selection. SHED is adaptable to various **fine-tuning objectives**, such as task-specific performance, model fairness, and domain focus, by modifying the value function in SV. **This adaptability is intended within the fine-tuning context.**\n\n- **Not Pure Cluster-Level Selection:** While SHED uses clustering to reduce computational complexity, it does not solely rely on cluster-level selection. Instead, it considers individual samples within clusters. The final selection step, QWCS, allows for the selection of individual samples. \n\n- **Embeddings:** We use Sentence-Transformer, which is well-suited for NLP tasks and produces semantically meaningful embeddings. Trained on a large and diverse corpus, these embeddings are robust across various NLP tasks. SHED's framework can adapt to different tasks with suitable embeddings. \n\n**W.5:**\n\nWe emphasized SHED's advantages over TS-DSHAPLEY in Section 2.3, including lower computational overhead, transferability, flexible framework, and data diversity.\n\nThe unavailability of TS-DSHAPLEY code and datasets, along with significant computational demands, pose challenges for comparison. Nonetheless, the results presented in our paper demonstrate the superiority of SHED:\n\n- **Efficiency:**  Figure 3 (c) shows SHED's computational cost scales linearly with the number of clusters. **While TS-DSHAPLEY's cost scales with data size.**\n\n- **Transferability:** Tables 6, and 7 show SHED’s robust performance across different models, underscoring its transferability. TS-DSHAPLEY lacks evidence of effectiveness across varied models.\n\n- **Human alignment:** Table 5 shows SHED-refined datasets not only enhance accuracy but also align well with human preferences. TS-DSHAPLEY does not evaluate human preference alignment, leaving it unclear whether its selected datasets improve the model’s ability to follow human instructions.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: 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.
Decision: Accept (poster)