Automatic Combination of Sample Selection Strategies for Few-Shot Learning

## Abstract

Abstract In few-shot learning, such as meta-learning, few-shot fine-tuning or in-context learning, the limited number of samples used to train a model have a significant impact on the overall success. Although a large number of sample selection strategies exist, their impact on the performance of few-shot learning is not extensively known, as most of them have been so far evaluated in typical supervised settings only. In this paper, we thoroughly investigate the impact of 20 sample selection strategies on the performance of 5 few-shot learning approaches over 8 image and 6 text datasets. In addition, we propose a new method for automatic combination of sample selection strategies (ACSESS) that leverages the strengths and complementary information of the individual strategies. The experimental results show that our method consistently outperforms the individual selection strategies, as well as the recently proposed method for selecting support examples for in-context learning. We also show a strong modality, dataset and approach dependence for the majority of strategies as well as their dependence on the number of shots – demonstrating that the sample selection strategies play a significant role for lower number of shots, but regresses to random selection at higher number of shots.

## 1 Introduction

Many domains are characterised by a labelled data scarcity due to data collection and annotation costs and privacy, making the use of typical deep learning unfeasible. To address these problems, few-shot learning aims to train or adapt machine learning models to new tasks using only few labelled samples per class (Song et al., ). There are two main categories of few-shot learning (more details regarding their difference are included in Appendix B): 1) gradient-based, which includes meta-learning and fine-tuning, where the knowledge from relevant datasets is transferred to new problem using gradient-based training, similar to supervised learning (Song et al., ; Chen et al., ; Vanschoren, ; Hospedales et al., ); and 2) few-shot learning via in-context learning, a new paradigm, where a pretrained large language model is conditioned on few examples to generate the prediction without any parameter updates (Dong et al., ; Liu et al., ).

As the number of utilised labelled samples is small, the role of individual samples is all the more important. Based on the choice of samples for in-context learning, the observed performance can vary between state-of-the-art and random predictions, indicating a strong sensitivity to the sample selection (Pecher et al., ; Köksal et al., ; Zhang et al., ). Even though a lot of research is dedicated to sample selection, such as active learning (Ren et al., ) or core-set selection (Guo et al., ), the most common approach in few-shot learning is a simple random selection. The exception is in-context learning, where a large number of sample selection strategies have been proposed recently, selecting samples according to their properties such as similarity, diversity, informativeness or quality (Li & Qiu, ; Zhang et al., ; Chang & Jia, ).

However, the impact of sample selection strategies on in-context learning or gradient-based few-shot learning remains largely under-explored. Therefore, the goal in this paper is to investigate the impact of the sample selection strategies on few-shot image and text classification tasks across a wide range of datasets.

In addition, as the different strategies mostly focus on selecting samples based on a single property (e.g., how often the samples are forgotten (Toneva et al., ), how easy they are to learn (Swayamdipta et al., ; Zhang & Plank, ) or how informative they are (Ye et al., ; Liu & Wang, )), we ask whether the selection based on their combination would not lead to better results. To answer this question and to leverage the strengths of different sample selection strategies, we propose ACSESS, an effective method for Automatic Combination of SamplE Selection Strategies. The proposed method first identifies a subset of relevant strategies that can improve the overall success of few-shot learning. Afterwards, the identified strategies are combined together using weighting based on their expected contribution to find samples that can provide the most benefit. We summarise our contributions as follows(^1^11To support replicability and extension of our results, we openly publish the source code of our experiments at [https://anonymous.4open.science/r/ACSESS](https://anonymous.4open.science/r/ACSESS)):

- •
We evaluate the impact of 20 sample selection strategies on 5 few-shot learning approaches, including meta-learning, few-shot fine-tuning and in-context learning, across 8 image and 6 text datasets. Experimental results show the selection strategies often lead to non-negligible improvements in performance (up to $2$ percentage points for gradient few-shot learning or $3$ percentage points for in-context learning), while showing a strong approach and dataset dependence.
- •
We propose a method for automatic combination of sample selection strategies to leverage their strengths and identify samples with complementary properties required for the success of few-shot learning. Experimental results show the method consistently leads to performance improvement (up to $3$ percentage points for gradient few-shot learning or $5$ percentage points for in-context learning), outperforming all investigated strategies, including a recent in-context specific strategy LENS (Li & Qiu, ).
- •
We further analyse how the impact of sample selection changes when increasing the number of shots. We find following key insights: 1) sample selection has significantly higher impact on lower number of shots; 2) at higher number of shots (30-40 on average), the impact of sample selection is negligible as all strategies regress to random selection; and 3) after a certain point, the boost in performance from increasing the number of shots is negligible (50-shot for gradient few-shot learning; 20-shot for in-context learning with strong dependence on input text size and model context size).

## 2 Related Work

A lot of focus is dedicated to sample selection: 1) to reduce the computation costs without the loss of performance, by distilling dataset to lower number of samples (Yu et al., ); 2) selecting a core set of a dataset with similar properties and same performance as the full set (Guo et al., ); or 3) to reduce annotation costs using active learning (Ren et al., ). A specific research focus is dedicated to selecting good performing samples for in-context learning, as the overall performance was found to be sensitive to this choice, leading to large variability in results (Pecher et al., ; Köksal et al., ; Zhang et al., ). However, the adoption of sample selection in few-shot learning is limited to active learning strategies and in-context learning.

The sample selection strategies can be divided into two groups. The first, more prevalent, uses heuristics and unsupervised metrics to rate and select samples according to specific property that serves as estimate of their potential to increase the model’s performance. The most popular approach is selecting samples based on the similarity to the test sample for in-context learning. The methods differ in employed: 1) similarity measure, such as cosine/Euclidean distance (Liu et al., ; An et al., ; Zemlyanskiy et al., ; Gupta et al., ; Pasupat et al., ), BM-25 (Wang et al., ; Nashid et al., ) or their combination (Agrawal et al., ; Gao et al., ); and 2) representation, such as simple TF-IDF (Agrawal et al., ; Wang et al., ; Zemlyanskiy et al., ), word embeddings from basic or further fine-tuned transformers (Liu et al., ; Gao et al., ; Gupta et al., ; Pasupat et al., ), or representation generated by prompting a large language model (An et al., ; Nashid et al., ).

Besides similarity, the samples are often selected based on their informativeness or the uncertainty. The active learning strategies are most common (Köksal et al., ; Schröder et al., ; Margatina et al., ; Park et al., ; Mavromatis et al., ), but also other approaches are utilised, such as using the bias of the samples (Ma et al., ). Other methods define a notion of quality for each sample, either by prompting a large language model to rate the samples (Shin et al., ) or observing how the inclusion or removal of the sample affects the performance (Maronikolakis et al., ; Nguyen & Wong, ).

When considering representativeness, the samples are selected using core-set methods that are designed to find a subset of samples with properties that are representative of the full training set (Guo et al., ; Killamsetty et al., ; Mirzasoleiman et al., ; Paul et al., ; Coleman et al., ). Learnability metric either determines how easy it is to learn the specific sample (Swayamdipta et al., ; Zhang & Plank, ), or how often the sample is forgotten after being learned (Toneva et al., ).

Finally, some approaches balance multiple properties at the same time. Often, the similarity of samples is balanced with their diversity (Wu et al., ; Qin et al., ) or the informativeness of the samples is balanced with their representativeness (Su et al., ; Levy et al., ; Ye et al., ; Liu & Wang, ). In specific cases, the search is done in two step fashion, such as finding set of informative samples and then using diversity guided search to improve this set (Li & Qiu, ).

The second set of approaches leverages training to select the samples. One possibility is to train a sample retriever, such as a separate scoring model (Rubin et al., ; Luo et al., ; Li et al., ; Wang et al., ; Aimen et al., ) or using determinantal point process (Yang et al., ; Ye et al., ). Another set of approaches leverages reinforcement learning to select good samples (Zhang et al., ; Shum et al., ; Scarlatos & Lan, ). Finally, the Datamodels approach trains a linear model to predict the performance gain of a set of samples to select the subset that would lead to the highest possible performance increase (Ilyas et al., ; Chang & Jia, ; Jundi & Lapesa, ; Vilar et al., ).

In this work, we investigate the impact of the existing strategies on few-shot setting. In contrast to (Li & Qiu, ), which represents a work closest to ours, we explore multiple few-shot learning approaches not only in-context learning. Our proposed method is inspired by the Datamodels approach (Ilyas et al., ), but performs the selection at the level of strategies instead of samples.

## 3 Sample Selection Strategies

### 3.1 Single-Property Strategies

**Table 1: The sample selection strategies evaluated in this paper, categorised based on the property they consider.**
|  | Strategy | Description |
| --- | --- | --- |
| Informativeness | Similarity | Most similar samples (popular for in-context learning). |
| Diversity | Most diverse/dissimilar samples. |  |
| Entropy (Park et al., ) | Active Learning; Highest entropy over class probabilities. |  |
| Margin (Park et al., ) | Active Learning; Lowest difference in probability between top 2 classes. |  |
| Least Confidence (Park et al., ) | Active Learning; Top class with lowest probability assigned. |  |
| Loss (Park et al., ) | Active Learning; Highest loss. |  |
| Contrastive Active Learning (CAL) (Margatina et al., ) | Core-Set; Predictive likelihood that diverges most from the neighbourhood as determined by KL divergence. |  |
| GraNd (Paul et al., ) | Core-Set; Highest contribution to decline of training loss. |  |
| Graph-Cut (Iyer & Bilmes, ; Iyer et al., ) | Core-Set; Submodularity function that measures diversity and informativeness. |  |
| Representativeness | Herding (Welling, ; Chen et al., ) | Core-Set; Minimising distance of subset centre to full dataset. |
| KCenter (Sener & Savarese, ; Agarwal et al., ) | Core-Set; Minimising distance of every sample in subset to full dataset. |  |
| CRAIG (Mirzasoleiman et al., ) | Core-Set; Gradients representative of full dataset. |  |
| Glister (Killamsetty et al., ) | Core-Set; Bi-level optimisation. |  |
| Learnability | Forgetting (Toneva et al., ) | Core-Set; How often the samples are forgotten. |
| Cartography (Swayamdipta et al., ; Zhang & Plank, ) | How easy it is to learn the samples. Due to no obvious consensus, all of easy, ambiguous, hard and combination of easy and ambiguous samples are considered. |  |

In this work, we thoroughly investigate the impact of strategies that consider 3 groups of properties: 1) informativeness, or how informative the samples are for the model; 2) representativeness, or how well the subset of samples represents the full dataset; and 3) learnability, or how easy it is to obtain or retain the information contained in the samples. We specifically focus on strategies that perform the selection by assigning a score to each sample that is used to sort the samples and choose the best. This includes active learning strategies that measure the uncertainty of the model, core-set selection strategies that find subset representative of the full-set and other strategies that use training dynamics. The high-level overview of these strategies is presented in Table 1 and their further details are included in Appendix C.

### 3.2 ACSESS: Automatic Combination of Strategies

Even though the different selection strategies can identify samples with specific single property, the use of these identified samples may not have a significant impact on the performance when working in a few-shot setting. For example, an informative sample that is a hard to learn or is often forgotten, may not contribute as much, leading to low impact on the performance. Instead, we propose to select examples that are characterised by a balance of complementary properties, even when they do not represent the best sample of any given property (e.g., a sample that may be less informative, but is more easily learned or retained). To accomplish this, we propose ACSESS, an effective method for Automatic Combination of SamplE Selection Strategies, composed of two stages: 1) identification of a subset of well-performing and relevant strategies that can improve the overall success of few-shot learning; and then 2) combination of the identified strategies using a weighted scheme based on their expected contribution. The final set of samples is selected by sorting the samples according to their final score and selecting the ones with the highest score.

#### Identifying the Subset of Relevant Strategies

In the first phase, the aim is to identify relevant strategies that, when combined, can select high quality samples based on their different properties. Even though the most optimal combination would be achieved by searching through all the combinations of strategies, such search is infeasible as it requires extensive computation costs. Instead, we use three separate methods for selecting strategies, inspired by feature selection in traditional machine learning and the Datamodels approach (Ilyas et al., ), specifically: 1) forward strategy selection; 2) backward strategy selection; and 3) datamodels strategy selection. The final subset of selection strategies is composed of the ones that are identified by each of the methods as most impactful, to minimise the effect of potential drawbacks of the individual method.

Forward Selection iteratively adds to the subset the selection strategy that results in the highest performance increase (similarly to forward feature selection). Starting with an empty subset, the method alternates between two steps. First, for each strategy not included in the subset we determine the increase in performance the strategy would yield if added to the subset (evaluating the performance of samples selected by the combination of already selected subset and the potential new strategy and comparing to old subset). Afterwards, the strategy that provides the highest positive increase in performance is added to the subset and the first step is repeated. If no added strategy leads to increase of the overall performance, the process ends, resulting in the subset of relevant selection strategies.

Backward Selection iteratively removes strategies from the subset until the performance no longer increases (similarly to backward feature selection). The method works the same way as the forward selection, but starting with all selection strategies and removing them from the subset based on the performance increase such removal leads to.

Datamodels Selection draws inspiration from the Datamodels (Ilyas et al., ; Chang & Jia, ), but works on the level of the strategies instead of samples. First, a set of 150 random combinations of strategies is created and evaluated. The random search is constrained to include each strategy at least 5 times to ensure sufficient coverage of each strategy. Afterwards, these combinations are used to create a simple regression dataset, with the difference in performance to the baseline selection (the classic few-shot learning selection) as label, and the multi-label presence vector of strategies as features. The dataset is used to train a regularised linear regression, LASSO (Tibshirani, ), which zeros out the weights for as many strategies as possible. The selection strategies with positive weights in the model represent the subset of relevant strategies.

#### Combining the Identified Strategies

After the relevant selection strategies are identified, they are combined using a weighting scheme. Each sample is assigned an importance score by each of the identified strategies. As the different strategies may assign scores of different magnitudes, all scores are normalised to zero-one interval. The final sample score is obtained as a weighted average of its assigned scores and the samples with highest score is selected.

In this paper, we evaluate three separate weighting schemes. First, uniform combination, assigns the same weight to each of the identified strategies. This represents the simplified version of the ACSESS method, potentially resulting in lower performance impact, but also with lower computation costs as no further optimisation is required. This weighting is also used for forward and backward selection. Second, weighted combination, assigns a different weight to each strategy, prioritising specific sample properties. The final trained weights of the Datamodels linear model are used as the dataset and model specific weights for the different strategies. As the weighting scheme is dataset and model specific, it should lead to the highest performance impact, at the cost of requiring more extensive computation. Finally, the weighted combination with random selection introduces a random element to the weighted combination, as random selection was identified as a strong baseline that can improve active learning performance (Park et al., ). Each sample is assigned additional uniformly randomised score which is included in the combination. The weight of this score is determined by the Datamodels by including random selection as additional strategy, due to its impact being dataset and model dependent.

## 4 Experiments

Datasets. The experiments are conducted on image and text classification datasets composed of different tasks with different number of classes. For the image datasets, we use 8 randomly chosen datasets from the Meta-Album (Ullah et al., ), specifically LR_AM.DOG and LR_AM.AWA for animal classification, VCL.APL for plane classification, HUM_ACT.ACT_410 for classification of human actions, MNF.TEX_DTD for classification of texture, MCR.PRT and MCR.PNU for classification of microscope images, and PLT.FLW for flower classification. In addition, we use 6 widely-adopted text datasets: 20 News Group (Lang, ) and News Category (Misra, ) for news category classification and ATIS (Hemphill et al., ), Facebook (Schuster et al., ), HWU-64 (Liu et al., ) and SNIPS (Coucke et al., ) for intent classification.

Few-shot Learning Approaches. The evaluation is done in a 5-way 5-shot setting using the gradient few-shot learning on both the image and text datasets and the in-context learning on the text datasets. For gradient few-shot learning, we use meta-learning approaches, specifically Prototypical Networks (Snell et al., ) and MAML (Finn et al., ), and Few-Shot Fine-Tuning (Chen et al., ). For in-context learning, we use the Mistral-7B (Jiang et al., ) and Zephyr-7B (Tunstall et al., ) models.

Baselines. The selection strategies are compared with 2 baselines: 1) Classic selection, where a new set of samples is randomly selected for each new task, potentially covering the whole datasets when evaluating on enough tasks; and 2) Random selection, where only a single set of 5 samples per class is randomly selected at the start and used for every single single task, representing a more realistic setting with limited budget. In-context learning is also compared with the recently proposed LENS (Li & Qiu, ) strategy that is in-context learning specific. Each selection strategy is run over 10 random seeds to determine their sensitivity to the effects of randomness (e.g., randomly initialised seeds). In addition, each experiment is repeated 5 times for different data splits and an average accuracy over these runs is reported (if not specified otherwise). Further experimental setup details are in Appendix D.

### 4.1 Impact of Single-Property Selection Strategies

Figure: Figure 1: Benefit of the different selection strategies, calculated as the difference in accuracy between the specific strategy and the classic few-shot selection, aggregated over the image and text datasets (boxplots show the distribution of results across the datasets). The performance of the classic selection is represented as the red dashed line (zero value). The consistently beneficial selection strategies depend on the data modality (image vs. text) and the approach (gradient few-shot learning using meta-learning and fine-tuning vs. in-context learning using large language models). Our proposed method ACSESS consistently leads to improved performance.
Refer to caption: extracted/5377787/figures/box-aggregated-diff.png

**Table 2: Benefit of the different selection strategies calculated as the difference in accuracy to the classic few-shot selection strategy, aggregated over the image and text datasets. The subscript represents the standard deviation of the difference over the aggregated datasets.**
| Strategy | Image Data - FSL | Text Data - FSL | Text Data - ICL |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
|  | ProtoNet | MAML | FT | ProtoNet | MAML | FT | Mistral | Zephyr |
| Random | $+0.06_{0.26}$ | $-0.00_{0.27}$ | $-0.10_{0.13}$ | $+0.10_{0.30}$ | $+0.07_{0.21}$ | $-0.26_{0.11}$ | $+0.46_{0.86}$ | $+0.01_{0.82}$ |
| LENS | N/A | N/A | N/A | N/A | N/A | N/A | $+1.73_{0.70}$ | $+1.42_{0.32}$ |
| Similarity | $-0.64_{1.14}$ | $-1.05_{1.15}$ | $-1.21_{1.24}$ | $-2.15_{1.82}$ | $-3.51_{1.50}$ | $-1.56_{1.45}$ | $-0.24_{1.11}$ | $-1.22_{1.61}$ |
| Diversity | $-0.35_{0.65}$ | $-0.93_{1.22}$ | $-0.57_{0.58}$ | $-1.65_{1.02}$ | $-2.77_{1.55}$ | $-3.65_{2.69}$ | $-0.66_{1.08}$ | $-0.42_{0.80}$ |
| Entropy | $-0.12_{0.43}$ | $-0.17_{0.37}$ | $-0.26_{0.18}$ | $-0.17_{0.33}$ | $-0.20_{0.68}$ | $+0.07_{1.03}$ | $+1.02_{0.98}$ | $+0.70_{0.38}$ |
| Margin | $+0.15_{0.34}$ | $+0.05_{0.29}$ | $+0.02_{0.26}$ | $+0.12_{0.10}$ | $+0.11_{0.17}$ | $+0.18_{0.35}$ | $+0.93_{0.94}$ | $+0.51_{0.50}$ |
| Least Confidence | $-0.05_{0.41}$ | $-0.31_{0.46}$ | $-0.03_{0.12}$ | $+0.02_{0.25}$ | $-0.25_{0.72}$ | $-0.14_{0.59}$ | $+0.49_{1.03}$ | $+0.04_{1.17}$ |
| Loss | $-0.44_{1.01}$ | $-1.02_{1.07}$ | $-0.53_{0.57}$ | $-1.49_{0.89}$ | $-3.00_{1.46}$ | $-3.75_{1.92}$ | $-0.56_{0.88}$ | $-0.65_{0.83}$ |
| CAL | $+0.19_{0.54}$ | $+0.54_{0.88}$ | $-0.43_{0.72}$ | $-0.18_{0.18}$ | $-0.76_{0.69}$ | $-0.69_{0.54}$ | $+0.39_{1.27}$ | $+0.18_{1.56}$ |
| CRAIG | $+0.18_{0.29}$ | $+0.26_{0.60}$ | $-0.04_{0.45}$ | $+0.22_{0.40}$ | $-0.15_{0.69}$ | $-0.00_{0.62}$ | $+0.66_{1.36}$ | $-0.38_{1.03}$ |
| DeepFool | $-0.29_{0.51}$ | $-0.77_{1.11}$ | $-0.41_{0.36}$ | $-0.18_{0.18}$ | $-0.90_{0.65}$ | $-0.76_{0.59}$ | $+0.40_{1.29}$ | $+0.18_{1.52}$ |
| Forgetting | $+0.16_{0.37}$ | $+0.53_{0.72}$ | $+0.03_{0.34}$ | $+0.39_{0.26}$ | $+0.49_{0.54}$ | $+0.41_{0.42}$ | $+1.27_{0.96}$ | $+0.77_{0.41}$ |
| Glister | $+0.20_{0.37}$ | $+0.33_{0.73}$ | $-0.10_{0.45}$ | $+0.19_{0.43}$ | $+0.06_{0.49}$ | $+0.03_{0.47}$ | $+0.64_{0.98}$ | $+0.21_{0.92}$ |
| GraNd | $+0.09_{0.49}$ | $+0.95_{1.11}$ | $-0.42_{1.41}$ | $-0.01_{0.38}$ | $-0.27_{0.34}$ | $-0.40_{0.67}$ | $+0.29_{0.62}$ | $-0.54_{1.41}$ |
| Herding | $-1.43_{1.31}$ | $-2.36_{2.94}$ | $-2.14_{1.78}$ | $+0.03_{0.51}$ | $-0.24_{0.69}$ | $-0.04_{0.45}$ | $+0.24_{0.91}$ | $+0.11_{0.78}$ |
| KCenter | $-0.45_{1.24}$ | $-1.03_{0.87}$ | $-0.42_{0.31}$ | $+0.04_{0.58}$ | $-0.15_{0.91}$ | $+0.03_{0.49}$ | $+0.13_{1.24}$ | $+0.31_{0.88}$ |
| Graph-Cut | $+0.32_{0.36}$ | $+0.40_{0.75}$ | $+0.05_{0.53}$ | $+0.34_{0.50}$ | $+0.35_{0.44}$ | $+0.10_{0.33}$ | $+0.42_{1.11}$ | $-0.17_{0.92}$ |
| CartographyEasy | $+0.65_{0.50}$ | $+1.17_{1.11}$ | $+0.21_{0.28}$ | $-0.04_{0.17}$ | $+0.02_{0.21}$ | $-0.19_{0.31}$ | $+0.16_{0.76}$ | $+0.06_{0.98}$ |
| CartographyAmbiguous | $-0.08_{0.36}$ | $-0.36_{0.50}$ | $-0.21_{0.32}$ | $-0.06_{0.12}$ | $-0.01_{0.13}$ | $-0.06_{0.21}$ | $+0.21_{0.64}$ | $+0.02_{0.64}$ |
| CartographyHard | $-2.42_{3.35}$ | $-3.03_{2.26}$ | $-1.71_{1.47}$ | $+0.05_{0.37}$ | $+0.08_{0.29}$ | $-0.14_{0.28}$ | $+1.60_{0.93}$ | $+1.26_{0.39}$ |
| CartographyEasy+Ambig. | $+0.05_{0.33}$ | $+0.24_{0.40}$ | $+0.04_{0.40}$ | $+0.59_{0.30}$ | $+0.78_{0.39}$ | $+0.37_{0.16}$ | $+0.21_{0.64}$ | $+0.02_{0.64}$ |
| ACSESSUniform | $+1.12_{0.80}$ | $+1.59_{0.97}$ | $+0.81_{0.35}$ | $+0.92_{0.41}$ | $+1.34_{0.45}$ | $+1.36_{0.75}$ | $+2.30_{1.11}$ | $+1.93_{0.48}$ |
| ACSESSWeighted | $+1.31_{0.75}$ | $+1.83_{0.97}$ | $+0.49_{0.41}$ | $+1.02_{0.44}$ | $+1.49_{0.47}$ | $+1.07_{0.69}$ | $+2.55_{1.08}$ | $+2.15_{0.53}$ |
| ACSESSWith Random | $+0.85_{0.65}$ | $+1.32_{0.91}$ | $-0.06_{0.56}$ | $+0.72_{0.33}$ | $+1.03_{0.48}$ | $+0.80_{0.54}$ | $+1.78_{0.98}$ | $+1.42_{0.45}$ |

In this section, our goal is to answer the following research question: *RQ1: What is the impact of single-property selection strategies on the few-shot learning?* The objective is to analyse the performance increase of the different selection strategies across various datasets and few-shot learning approaches. The results are shown in Figure 1 and Table 2, aggregated over all the datasets (the detailed de-aggregated results are included in Appendix F). We present the results separately for image and text datasets, as well as for the gradient few-shot learning and in-context learning, as we observed a strong dependence of the strategy benefit on the modality and few-shot learning approach used.

Majority of selection strategies fail to outperform the Classic selection in case of gradient few-shot learning. In most cases, the strategies lead to performance on par or even lower than the Classic selection (for example hard samples on image datasets or diverse samples on text datasets). Only specific strategies consistently lead to better performance (e.g., easy to learn samples on image datasets, or Glister on text datasets). As such, the random sample selection represents a strong baseline for the gradient few-shot learning.

Sample selection is more beneficial for in-context learning. Larger number of strategies leads to increase in performance, while only few strategies, such as the popular selection based on similarity or diversity, under-perform the Classic selection. At the same time, the increase in performance is higher than with gradient few-shot learning, although with a larger variance in results. In contrast to gradient few-shot learning, the hard to learn samples provide the most benefit for in-context learning. The reason may be that hard to learn samples provide a lot of information beneficial for the classification, which cannot be easily leveraged in the gradient few-shot learning due to the samples being hard to learn. However, as there is no explicit “learning” in the in-context learning use of large language models, such samples can be more easily leveraged.

Learnability property is a strong indicator of quality for few-shot learning. The strategies that choose based on how easy it is to learn the samples (Cartography) or how often they are forgotten (Forgetting) consistently lead to overall highest increase in performance. The observed improvement is on average as high as $1$ percentage point for gradient few-shot learning and $1.5$ percentage points for in-context learning.

### 4.2 ACSESS Results and Characteristics

Figure: Figure 2: Comparison of the LENS method (Li & Qiu, ) to our proposed automatic combination of selection strategies (ACSESS).
Refer to caption: extracted/5377787/figures/combination-lens-diff.png

In this section, we answer the following research question: *RQ2: How does the ACSESS method perform in comparison to the single-property strategies?* Besides analysing the benefit and comparing with other strategies, we analyse the influence of the identified strategies and of the different weighting schemes.

The ACSESS method consistently leads to performance increase across all few-shot learning approaches. Our proposed method outperforms all the investigated single-property strategies, leading to an average performance increase of up to $1.8$ percentage points for gradient few-shot learning and $2.5$ percentage points for in-context learning. Comparing with the in-context specific strategy LENS (Li & Qiu, ) (see Figure 2 for comparison), which also outperforms all single-property strategies, we observe the increase of ACSESS is statistically more significant (p-value of $0.0002$ using the Wilcoxon signed-rank test).

The ACSESS method identifies samples with complementary properties, with higher priority on learnability. The identified selection strategies that are used in the combination are different for the different data modalities (image vs. text) and the type of few-shot learning (gradient vs. in-context learning). For the image datasets, the combination of easy to learn samples (Cartography) that are located on the decision boundary (Margin) provides the most benefit. For the gradient few-shot learning approaches on text datasets, the combination of easy and ambiguous samples (Cartography) that are least often forgotten (Forgetting) on the decision boundary (Margin) that provide additional informativeness from the Graph-Cut strategy provides the most benefit. In case of in-context learning, the combination of hard to learn samples (Cartography) that are least often forgotten (Forgetting) on the decision boundary (Margin) and with highest Entropy provide the most benefit. Further analysing the identified strategies and their weights, we find the ACSESS method gives higher importance to samples that are learnable, lower importance to informative samples (but with multiple sources for both), while the strategies that focus on representativeness are not included at all.

The dataset and approach specific weighting leads to the best performance. The only exception is the Few-Shot Fine-Tuning approach, where the uniform weighting shows the most benefit. At the same time, the weighting strategy that incorporates the random selection often leads to worse performance and higher variance in the results. Even though the uniform combination is computationally less expensive than the weighted combination, its performance increase is only slightly lower (average difference of $0.10-0.25$). As such, the uniform weighting represents a good trade-off between the performance increase and the computation costs, making it in general a better choice.

### 4.3 Effects of Variable Number of Shots

Figure: Figure 3: Effect of the number of shots on the benefit of selection strategies, comparing ACSESS and random selection, aggregated over datasets. The benefit is calculated as a difference to the classic selection at 5-shots. The benefit of sample selection is more significant at lower number of shots. At larger number of shots, the benefit and the performance boost from further increase of shots becomes negligible.
Refer to caption: extracted/5377787/figures/change-by-shots.png

In this section, we answer the following research question: *RQ3: How does the benefit of sample selection change based on the number of shots?* Our aim is to investigate how the benefit of sample selection strategies changes as we increase or decrease the number of shots that we select. In addition, we investigate whether the few-shot learning approaches themselves benefit from larger number of shots. The results of this evaluation are included in Figure 3, comparing the benefit of the ACSESS strategy and Random selection over the main experimental setup (5-shot setting).

The selection strategies provide more benefit on lower number of shots. On the 1-shot setting, the difference in accuracy between the Random strategy and the ACSESS strategy can be as high as $5$, $7$ or $4$ percentage points respectively for the few-shot learning on image data and text data, and in-context learning on text data. Increasing the number of selected shots leads to diminished impact, as the ACSESS strategy regresses to random selection when selecting more than 30-40 shots. For more detailed analysis we compare all the investigated strategies in the 5-shot and 10-shot setting (distribution of results is shown in Figure 5 in Appendix E). This comparison further reinforces our findings, as we find that the difference to random selection is statistically significantly higher in the 5-shot (p-value of $1.93e-07$ using the Wilcoxon signed-rank test).

Impact of selection strategies and further increase of the number of shots is negligible after certain point. For the gradient few-shot learning, the performance after approximately 50 shots remains more or less constant. For the in-context learning, the performance even decreases significantly after 20-25 shots, achieving similar performance to the 1-shot setting when using 50-shots. The main reason for this drop-off may be the limited context size of the large language models, making the model degenerate to random guessing as the cross-over is text length dependent. As such, the focus on selecting high quality samples is paramount when only few labelled samples are available (e.g., working with limited labelling budget), while the random selection can be safely used with enough labels. Furthermore, labelling more samples and using them as additional shots for few-shot learning may not lead to better performance.

### 4.4 Additional Insights to Sample Selection Strategies

Figure: Figure 4: Standard deviation introduced by multiple runs of the different selection strategies. The results for Prototypical Networks, MAML and Few-Shot Fine-Tuning are aggregated over both the image and text datasets, while the results for Mistral and Zephyr are only from text datasets. The ACSESS method shows lower sensitivity to repeated runs compared to the majority of the strategies included in the combination.
Refer to caption: extracted/5377787/figures/aggregated-std.png

In this section, we provide additional insights from ablation studies. We are mainly interested how the factors such as dataset and model affect the sample selection (based on dataset specific results from Appendix F) and how sensitive the strategies are to their random components (based on the variance from repeated strategy runs in Figure 4).

Specific strategies show sensitivity to the effects of random seed. Changing the random seed for specific strategies may often lead to different set of selected samples and different performance impact. The only exception are some of the core set selection strategies (such as Glister or the Graph-Cut), which consistently lead to the selection of the same set of samples. On the other hand, strategies with random initial condition, such as the similarity selection or some of the active learning strategies, are more sensitive to the effects of the random seed. The ACSESS method shows lower sensitivity to repeated runs (with the exception of weighting with random element) but still may lead to different sets of samples based on the random seed.

Strong dataset dependence for selection strategies. The strategy that performs well on one dataset (such as DeepFool on the HUM_ACT.ACT_410 dataset) may perform quite poorly on other datasets (such as MCR.PRT or MCR.PNU). Such strategies cannot be used on any dataset without further evaluation. Only few strategies perform consistently on the majority of the datasets, such as Herding showing poor performance on all image datasets, or Cartography showing good performance across all datasets and models. We do not observe a significant dataset dependence of the ACSESS method, as it identifies the well-performing strategies and leverages their strengths for the specific dataset.

Strong model dependence and sensitivity to selected samples. The strategy performing well for one model (GraNd on MCR.PNU for MAML, or KCenter on 20 News for Zephyr) may result in a decrease of performance for other models (GraNd for Few-Shot Fine-Tuning, or KCenter for Mistral on the same setting). Overall, the selected samples resulting from different strategy or from a repeated run of the same strategy affect the Few-Shot Fine-Tuning less, leading to smaller benefit. At the same time, the MAML method and the in-context learning models are more affected by the selected samples, leading to more varied results and often higher performance increase. Such sensitivity to sample selection was observed in previous works as well (Pecher et al., ; Köksal et al., ; Zhang et al., ; Li & Qiu, ). Curiously, Prototypical Networks gain quite large performance increase from sample selection, but show low sensitivity to the selected samples and variance from repeated runs of sample selection. As such, sample selection is more important for MAML and In-Context Learning and less important for Few-Shot Fine-Tuning.

## 5 Conclusion

In this paper, we investigate the impact of larger number of different sample selection strategies on the success of few-shot learning. To leverage strengths of these strategies, we propose the ACSESS method for automatic combination of the sample selection strategies to select samples with complementary properties. The experimental results show that although the individual sample selection strategies may not always be beneficial, their combination using our proposed method consistently leads to better performance. Curiously, we observe that the learnability of the samples is more important for few-shot learning than their informativeness or representativeness. Finally, analysing the behaviour of the sample selection in regards to the number of selected shots, we find that the sample selection provides the most benefit on low number of shots, while at the higher number of shots all strategies regress to random guessing. In addition, after certain point, increasing the number of available samples no longer leads to improvement in performance of few-shot learning, but may lead to decrease in performance in some cases, especially for in-context learning.

## Acknowledgements

This work was partially supported by the projects funded by the European Union under the EU Horizon 2020: TAILOR, GA No. [952215](https://doi.org/10.3030/952215), by the European Union under the Horizon Europe: vera.ai, GA No. [101070093](https://doi.org/10.3030/101070093), AI-CODE, GA No. [101135437](https://cordis.europa.eu/project/id/101135437); and by Modermed, a project funded by the Slovak Research and Development Agency, GA No. APVV-22-0414.

Part of the research results was obtained using the computational resources procured in the national project National competence centre for high performance computing (project code: 311070AKF2) funded by European Regional Development Fund, EU Structural Funds Informatization of Society, Operational Program Integrated Infrastructure.

## Ethical Considerations and Impact Statement

The experiments in this paper work with publicly available datasets, citing the original authors of each dataset. All the datasets are used in accordance to their license and terms and conditions of use. We do not work with any personally identifiable information or offensive content and perform no crowdsourcing or further data annotation. In addition, we are not aware of any potential ethical harms or negative societal impacts of our work, apart from the ones related to the advancement of the field of Machine Learning and Few-Shot learning. Finally, we follow the license terms for all the models we use – all models and datasets allow their use as part of research. It is possible the large language models (Mistral and Zephyr) we used contain biases and may generate potentially offensive or harmful content. However, the authors of the models already reduce this potential bias as much as possible when training the models, while at the same time we limit the output to few tokens and do not release any output of these models, which should further reduce the potential bias and negative impact.

#### Impact Statement: CO2 Emissions Related to Experiments

The experiments presented in this paper used higher compute resources in order to evaluate all the sample selection strategies for all the different few-shot learning approaches. Overall, the experiments were conducted using a private infrastructure, which has a carbon efficiency of 0.432 kgCO${}_{2}$eq/kWh. A cumulative of 2000 hours of computation was performed on hardware of type A100 PCIe 40GB (TDP of 250W). Total emissions are estimated to be 216 kgCO${}_{2}$eq of which 0 percent were directly offset. These estimations were conducted using the [MachineLearning Impact calculator](https://mlco2.github.io/impact#compute) presented in (Lacoste et al., ). Whenever possible, we tried to reduce the compute resources used as much as possible. The most compute resources were used by the large language models – Mistral-7B and Zephyr-7B and their further tuning. To reduce the computation costs, we use the 4-bit quantised versions of these models and in cases when the further tuning is required, we fine-tune only the last classification layer (keeping the rest frozen). In addition, for gradient few-shot learning approaches, we opt for smaller models that do not require such an extensive computation costs, while still providing relevant results. Finally, whenever possible, we reuse the already performed computations for the selection strategies, such as generating the feature representation for the different samples only a single time and re-using it whenever possible (e.g., using pre-trained models without further tuning for these representations).

## Appendix A Limitations

In the experiments for evaluating the impact of different sample selection strategies, we focused on such settings, where the impact of sample selection may be reduced — despite that we observed significant differences in the achieved performance. First of all, in within-domain few-shot learning, the samples are selected from the same domain, thus the difference in impact and role of the individual samples may not be as high. We may expect that the impact of sample selection in cross-domain few-shot learning may be higher and lead to even larger performance deviations. In addition, we focus on a balanced-shot setting, where we select the same number of shots for each class, leading to lower impact of strategies for different classes. These additional settings are out of scope of this paper and may be a subject of future research.

To diminish undesirable randomness effects, we report the results as an average over 5 repeats of data splitting, 10 repeated runs of the selection strategies (resulting in 50 overall evaluation runs), with each run evaluated on 600 tasks. Such setting limits the variance in results, but may also obscure the benefit of selection strategies to a certain extent. On the other hand, although we focus only on a 5-way few-shot learning setting (using only 5 classes per task), we do not expect significant change in result for most of the few-shot learning approaches when using higher number of classes (such as 10-way or 20-way classification). The only exception is the in-context learning, where we observed the dependence on input size and context width of the models. With higher number of classes, the observed performance of large language models would start to deteriorate to the performance that can be achieved with a low number of shots.

Similarly to other research papers focusing on sample selection and few-shot learning via in-context learning, we assume an availability of large-enough annotated dataset from which we select the sample using different strategies. This also includes the use of larger validation or held-out set when selecting strategies. If we considered the true few-shot learning setting, only the active learning strategies (i.e., Entropy, Margin and Least Confidence) could be used, as they work with unlabelled data only, or a different approach for sample selection should be employed, such as using weak-labels to select the best samples, or assign every single class to all the labels and perform the sample selection that way to select both best performing samples as well as the class for them (as it is done in (Chang & Jia, )).

Before the experiments, we run hyperparameter optimisation for the gradient few-shot learning models and perform basic prompt-engineering for the in-context learning approaches. We follow the recommendations for good prompt engineering, using the meta-tags defined for each model (which lead to better performance of the models), while also not using overly-simple or overly-complicated prompts. The prompts we use are created based on dataset description, the prompts used in related works and the formats recommended for different models (e.g., taking inspiration from (Sun et al., )). As we do not perform any extensive or automatic prompt-engineering, these prompts may lead to lowered performance and in some cases also lower impact of the sample selection for the few-shot learning setting. However, running an extensive prompt-engineering would introduce another controllable parameter which could potentially introduce further bias into the results (e.g., different selection strategies showing different impact for different prompt formats).

Finally, we also identify factors that we consider to be sufficiently covered, but where the further extension could lead to more generalisable results (nevertheless, also requiring a considerable amount of additional computational resources), such as including more selection strategies, evaluating on more datasets covering other classification tasks (mainly in regards to text datasets) or using larger number of base models for meta-learning and few-shot fine-tuning, or large language models for in-context learning. First of all, although we focus on the major selection strategies, which are general for any few-shot learning approach, the work can be extended to include larger number of in-context learning specific strategies or strategies that do not perform selection based on scoring of the samples. In addition, even though we run the experiments on 8 image datasets and 6 text datasets, covering different classification tasks with different number of classes, the work could be potentially extended to even larger number of datasets and classification tasks, mainly in case of text modality where we are limited by the number of classes the datasets contain (to sufficiently explore few-shot learning). Finally, although we cover all the main groups of few-shot learning approaches, the work could be extended to cover larger number of meta-learning approaches (where we focus on two of them), base models for meta-learning and few-shot fine-tuning (we focus on one model for image data and one model for text data) and large language models for in-context learning (we focus on two of the most recent and currently best performing large language models Mistral and Zephyr). However, we do not expect significantly different results and findings, only an improvement of their generalisability.

## Appendix B Few-Shot Learning Problem Formulation

In this section, we introduce the definition of the few-shot learning problem and the difference between gradient-based few-shot learning and few-shot learning via in-context learning. Note that in-context learning can be applied only to text datasets as it utilises pretrained large language models.

For the standard few-shot learning, we assume a larger collection of instances $D={(x_{i},y_{i})}$, with larger number of classes. From this dataset, a separate task $D_{T}\subset D$ is created by randomly sampling few classes $N$. The task is characterised by a training set (also called support set, $S$) and test set (also called query set, $Q$). To create these sets, few samples ($k$) are sampled for each class. The few-shot learning algorithms then use the few samples from the support set $S_{T}$ to learn to solve the task $D_{T}$ quickly and are evaluated on this ability using the query set $Q_{T}$. This represents the $N$-way $k$-shot classification. The few-shot learning approaches mostly consist of two phases, training phase and testing phase. Often, these two phases work with separate sets of $D_{T}$, i.e., the classes appearing as part of tasks in the testing phase do not appear during training. As each task works only with subset of classes, the evaluation is often done on many such tasks and the average performance from these tasks is reported.

The gradient-based few-shot learning approaches follow episodic training similar to typical supervised training to gather knowledge and experience and learn to quickly adapt when encountering new task. Using the support set, the model is adapted to the task using gradient descent based approaches and then evaluated on the query set. Based on the evaluation, some kind of knowledge on how to better adapt is saved in part of the model, while the rest of the model is reset. This is done for a large number of episodes (often called meta-training), with the model accumulating the knowledge. After enough episodes are run, the model is evaluated (in a meta-testing phase) using the exact same setup.

On the other hand, no training phase is present in few-shot learning via in-context learning, but a pretrained large language model is used without any additional parameter updates instead. For each new task, a task-specific text prompt is formed, by concatenating task-specific instructions, the samples from the support set along with the their labels, and a specific test sample. After being given this prompt, the large language model generate a prediction in a form of word (or multiple words). These generated words are then mapped to the task labels and a class is assigned. The process is repeated for each sample in the query set. For example in case of binary classification, the task-specific instruction may be ”Determine sentiment of following sentence”, the concatenated samples from support set may be formatted as ”sentence, sentiment: positive/negative”, with the word ”positive” mapping to class representing positive sentiment and ”negative” to class representing negative sentiment. As such, the only supervision for the large language model is from the instruction and the concatenation of support samples. Therefore, the model is limited by its max input length. At the same time, this approach was found to be sensitive to the selected samples in the support set and their order (Zhao et al., ; Zhang et al., ; Pecher et al., ; Chang & Jia, ).

## Appendix C Sample Selection Strategies Details

In this section, we provide further details about the different single-property sample selection strategies that we evaluate in this paper, as well as for the baselines. In addition, we specify the hyperparameters of these methods and how we use them for selecting the set of samples.

The idea behind the uncertainty-based and active learning selection strategies is that the most informative samples with higher impact are the ones that the model is least certain about (Coleman et al., ; Park et al., ). These strategies select samples iteratively over multiple steps. In our experiments, we select 1 sample for every class in individual step. In addition, the first sample for every class is chosen randomly, as such setting was observed to lead to better active learning results (Park et al., ). When using these methods for combination, the samples that are selected after running all the required steps (e.g., 4 steps when choosing 5-shots) use the score assigned to them at their respective step when they were chosen, while the remaining samples are assigned the average score over all the selection steps. The Entropy strategy measures the entropy for each of the samples over the predicted class probabilities (which is used as the sample score) and selects the sample with highest entropy. The Margin (Breaking Ties) strategy assigns to each sample the difference between the probability of the two most probable classes. The samples with lowest such difference are selected as those are the ones the model is most uncertain about (and which can be considered on the decision boundary of the model). The Least Confidence strategy simply selects the samples where the most likely class has the lowest probability assigned to it. Finally, the Loss strategy selects samples with the highest loss.

The idea behind the core-set selection strategies is to select subset samples that are representative of the whole dataset and have similar properties. The included strategies select samples based on different criteria, such as error/loss, gradient or submodularity. These strategies select the full set of samples at the same time according to the assigned scores. As such, when using these strategies for combination, the full sample scores are used from this single iteration. The Decision Boundary based methods select samples the are close to the decision boundary, as these represent the samples that are hard to separate and should provide the most information to the models. The Contrastive Active Learning (CAL) (Margatina et al., ) selects the samples whose predictive likelihood diverges the most from their neighbourhood as determined by the KL divergence. The DeepFool strategy (Ducoffe & Precioso, ) approximates the samples on boundary by perturbing the samples until the predicted class changes and selects the samples that require the smallest perturbation. The idea behind Error or Loss based methods is that the samples that contribute more loss or error to the training of model have higher importance and should contribute more to the training when selected. The GraNd strategy (Paul et al., ) calculates the average contribution of each sample to the decline of the training loss at early stage of training across several independent training runs (calculating gradient norm expectation of the sample). The Forgetting strategy (Toneva et al., ) simply calculates how often the specific sample is incorrectly classified after being classified correctly in the previous epoch. The number of such forgetting events is used as the sample score and samples that are forgotten the most often (or least often) are selected. In our evaluation of single-property strategies, we consider only the setting of most often forgotten samples. However, when using the strategy for combination, we also consider the setting of selecting the least forgotten ones. The Submodularity based methods use submodular functions (Iyer & Bilmes, ; Iyer et al., ), which naturally measure the informativeness and diversity of a subset of samples. We evaluate the selection based on Graph-Cut, as the method performed the best from all the different submodular functions. The Geometry based methods assumes that samples that are close to the feature space have similar properties. As such, these methods try to remove samples that are close to each to as they provide redundant information and lead to small subset that covers the properties of the whole dataset. The Herding strategy (Welling, ; Chen et al., ) iteratively and greedily adds one sample to the subset which minimises the distance between the centre of the subset and the centre of the whole dataset. On the other hand, the KCenter strategy (Sener & Savarese, ; Agarwal et al., ) selects sample that minimises the largest distance between each sample already in the subset and its closest sample not yet included in subset. The Gradient Matching based methods try to find a subset of samples whose gradient can imitate the whole dataset. The CRAIG strategy (Mirzasoleiman et al., ) converts the problem into maximisation of a monotone submodular function which is then solved greedily. Finally, the Bilevel Optimisation based methods convert the problem into a bilevel optimisation, with different objectives in the outer and inner level. The Glister strategy (Killamsetty et al., ) uses a validation set on the outer optimisation, where the subset selection is used as objective, and log-likelihood in the inner optimisation, which handles model parameter optimisation.

In addition, we also evaluate and use sample selection based on the Cartography (Swayamdipta et al., ; Zhang & Plank, ), which measures how easy or hard it is to learn different samples. This ease of learning is determined by observing the training dynamics of all the samples across few epochs, looking at the average confidence/probability of the correct class and the variance of this confidence. The samples with high confidence and low variance are considered to be the easy to learn samples. At the same time the samples with small confidence and small to medium variance are considered the hard to learn ones. The remaining samples are considered to be ambiguous (medium confidence or samples with high variance). We explore four different settings in our experiments and choose the samples accordingly: 1) Easy samples, where we sort the samples based on confidence and choose the top-K samples with highest confidence for each class; 2) Hard samples, where we sort the samples based on confidence and choose the bottom-K samples for each class (i.e., the lowest confidence samples); 3) Ambiguous, where we first calculate average confidence and standard deviation of all the samples, select the samples whose confidence is close around the average confidence (in the interval defined by 1 standard deviation around the average confidence), and then randomly sample from them; and 4) Easy + Ambiguous, where we choose half of the samples from the easy set and the other half from ambiguous set.

All the approaches mentioned so far perform the selection using a model that is trained for some epochs. The number of epochs the training is done for significantly affects the outcome of different strategies. For this reason, we search for an optimal number of epochs by evaluating the selected sample on a single run. The optimal number of epochs determined by this search is 10% of the full epochs used for the few-shot fine-tuning models. This number represents a good trade-off between the computational cost of the methods and the quality of samples. Running the training for full number of epochs may result in better selection of samples, but we consider such setting to not be representative. In addition, we use the same number of epochs for each strategy to keep the comparison unbiased and representative. In addition to searching for optimal number of epochs, we search for an optimal set of hyperparameters for the methods at the same time (such as the learning rate, the optimiser used or batch size). We use the Adam optimiser with Cosine Annealing learning rate scheduler, with learning rate of 0.01 for image data and 0.0001 for text data and a batch size of 64.

To keep the results as comparable and unbiased as possible, each selection strategy uses the same base model as the one used in the main experiments, i.e., ResNet-18 (He et al., ) pretrained on Imagenet dataset (Deng et al., ) for image data and pretrained BERT-base (Devlin et al., ) for text data in case of gradient few-shot learning, and the same large language models (Mistral (Jiang et al., ) and Zephyr (Tunstall et al., )), but with only their last classification layer being learned, for in-context learning. In case the selection strategy requires sample features, we use the output of last fully-connected layer (the input of classification layer) as the feature vector similar to (Guo et al., ).

We also evaluate the sample selection based on Similarity and Diversity as it represents popular approach for in-context learning. To select the sample, we use the cosine similarity between the feature representation (obtained from the penultimate layer) of the samples. To select the samples for gradient few-shot learning, we first randomly select one sample for each class. Afterwards, we expand the set of selected samples by iteratively adding the most similar (for Similarity) or most dissimilar (for Diversity) sample to the average feature representation of the set of selected samples for each class (i.e., if we already have 5 selected samples in the specific class, we calculate the similarity to the average vector feature representation of these 5 samples). For in-context learning, we instead the most similar (for Similarity) or the most dissimilar (for Diversity) samples for each test sample as is common for this approach (Sun et al., ).

Finally, we also use the in-context learning specific LENS method (Li & Qiu, ) as a baseline. We use the same models (Mistral and Zephyr) for the selection of samples and run a hyperparameter search informed by the recommendations of the authors to an optimal setup for the search. In addition, we use the modified version of their source code for running the method.

For the core-set selection strategies, we use the modified implementation of the methods released by (Guo et al., ) and its extension provided by (Park et al., ) for active learning methods. For the Cartography strategy, we use the modified implementation released by the original authors (Swayamdipta et al., ).

## Appendix D Experimental Setup: Further Details

The experiments and evaluation of different sample selection strategies are done on the within-domain few-shot learning, where the classes in training, validation and testing datasets all come from the same domain (e.g., from the same dataset). Each evaluation run is done on 600 randomly selected tasks for the gradient few-shot learning approaches and 300 tasks for the in-context learning approaches (due to high computation costs of LLMs). Each tasks is a 5-way 5-shot (or $K$-shot for experiments where we change the number of shots) classification task evaluated on 16 test samples per class, following the standard few-shot learning methodology and the evaluation from (Ullah et al., ). The performance of a single evaluation run is reported as the mean accuracy across these tasks and the different splits of data (we use 5 splits of data as mentioned in the main part of the paper). These scores are then used to report the findings in the main experiments, either as mean and standard deviation over multiple repeated runs of the specific sample selection strategy or over the different models and datasets. As such, the standard deviation of different data splits or different tasks is not reported. This is also the reason why we can observe zero standard deviation on some of the sample selection strategies.

For the gradient few-shot learning approaches we use the ResNet-18 (He et al., ) for image data, pretrained on Imagenet dataset (Deng et al., ), and a pretrained BERT-base (Devlin et al., ) model for the text data as base models. All the models are further trained on each dataset using the different few-shot learning approaches, namely the Prototypical Networks (Snell et al., ), Model-Agnostic Meta-Learning (MAML) (Finn et al., ) and Few-Shot Fine-Tuning (Chen et al., ). In case of Few-Shot Fine-Tuning, the further training is done on the concatenation of all the meta-training classes and their corresponding samples, while at the meta-test time, only the last layer is replaced and fine-tuned on the specific task with 5 classes. The Prototypical Networks and MAML follow the typical few-shot training setting, training on large number of 5-way few-shot tasks. The meta-learning approaches on image data are trained on 15 000 tasks and the Few-Shot Fine-Tuning approach is trained on 15 000 randomly sampled batches of size 16. On text data, the number of sampled tasks and batches is increased to 25 000. In addition, the MAML approaches uses 5 training iteration and 10 testing iterations on image data and 10 training and 15 testing on text data, while Prototypical Networks use only 1 in both cases. Finally, all approaches use the Adam optimiser with 0.001 (meta) learning rate and 0.01 base learning rate. The full training process is repeated 3 times and the best performing model, selected using a separate validation dataset, is selected for use for evaluation.

**Table 3: Prompt formats and verbalisers used for different datasets in the paper. The News classification datasets include 20 News Group and News Category datasets and Intent classification includes ATIS, Facebook, HWU-64 and SNIPS. The [Option 1-5] are replaced with the names of the classes as defined by the verbaliser. The [Input] is replaced by the sentence of the samples and the [Output] is replaced with the name of class as defined by the verbaliser. The [Input] and [Output] are repeated for each in-context sample, while the final [Output] is used to determine the predicted class. The same format is used for both the Mistral-7B and Zephyr-7B large language models as it was the best performing one from the ones tried during basic prompt engineering.**
| Datasets | Prompt Format |
| --- | --- |
| News classification | Determine category of the sentence using following options: 1) [Option 1] 2) [Option 2] 3) [Option 3]<br>4) [Option 4] 5) [Option 5].<br>[Input]<br>[Output] |
| Intent classification | Determine intent of the sentence using following options: 1) [Option 1] 2) [Option 2] 3) [Option 3]<br>4) [Option 4] 5) [Option 5].<br>[Input]<br>[Output] |
| Dataset | Verbaliser |
| 20 News Group | {IBM, Middle East Politics, Windows XP, Motorcycles, Medicine, For Sale, Religion, MS Windows, Baseball, Auto, Hockey, Mac, Graphics, Christianity, Guns, Electronics, Space, Crypto, Atheism, Politics} |
| News Category | {Politics, World News, Parenting, Money, Wellness, Business, Weddings, Entertainment, Impact, Black Voices, Queer Voices, Crime, Divorce, Food and Drink, Worldpost, Parents, Travel, The Worldpost, Healthy Living, Taste, Media, Culture and Arts, Style, Weird News, Style and Beauty, Comedy, Home and Living, Sports, Environment, Education, Good News, Fifty, Women, Science, Arts and Culture, US News, Green, Tech, Religion, Latino Voices, College, Arts} |
| ATIS | {Flight, Abbreviation, City, Airfare, Ground Service, Ground Fare, Airline, Flight Number, Aircraft, Distance, Capacity, Flight Time, Quantity, Airport} |
| Facebook | {Road Condition, Departure, Duration, Event, Arrival, Directions, Distance, Traffic, Update, Combine, Route, Location} |
| HWU-64 | {Set Alarm, Definition, Play Music, Set Calendar, Play Radio, Confirm, Quirky, Send Email, Currency, Like, Social Query, News, Neutral, Stop, Calendar Query, Alarm Query, Recommend Movie, Play Audiobook, Weather Query, Praise, Social Post, Play Podcast, Negate, Recipe, Affirm, Remove Calendar, Recommend Location, Remove List, Explain, Email Query, Maths, Stock, Transport Query, Order Takeaway, Datetime Query, Repeat, Factoid, Taxi, Add List, Add Contact, Recommend Event, Mute Volume, List Query, Ticket, Convert Datetime, Joke, Remove Alarm, Traffic, Volume Up, Takeaway Query, Play Game, Contact Query, Music Query, Volume Other, Volume Down, Music Setting, Greet, Dislike} |
| SNIPS | {Playlist, Weather, Event, Musing, Creative Work, Rate Book, Book Restaurant} |

For the in-context learning approaches, we use instruction tuned Mistral-7B (Jiang et al., ) and Zephyr-7B (Tunstall et al., ) large language models in the 4-bit quantised mode. This significantly reduces the computation costs required, while having low impact on the performance and almost no impact the impact of sample selection. We perform no training for these models (except in case of sample selection, which is covered in Appendix C). Instead we run a basic prompt engineering to find the best performing template for each model. In the end, we opted for the simple template that performed the best on average for each model and each dataset, which is illustrated in Table 3, along with the verbaliser (i.e., the possible outputs of the model that are mapped to the classes in the dataset). Finally, the in-context learning models are set to provide deterministic outputs (controlling the temperature and disabling sampling) and set to generate maximum of 10 tokens. In case where multiple words that can be mapped to a dataset class are generated, the output is treated as incorrect as the model is just hallucinating and not working as intended (this behaviour was mainly observed when using the models in zero-shot or 50-shot setting, where the model either does not understand the task or the context is not sufficient for it to follow the instructions).

All the hyperparameters for all models were determined using a hyperparameter search on each data modality and model. For the experiments, we use a modified implementation provided by the MetaAlbum (Ullah et al., ), extended to further approaches, models and datasets. In addition, we also the hyperparameters used in the paper as a starting point for the hyperparameter search.

## Appendix E Comparison of Sample Selection Impact Between 5-Shot and 10-Shot Setting

Figure: Figure 5: Distribution of the selection strategies benefit (calculated as difference to the classic few-shot selection) for the 5-shot and 10-shot setting. The benefit of the different selection strategies is more significant at lower number of shots.
Refer to caption: extracted/5377787/figures/aggregated-5vs10.png

To better explore the effect of increasing number of selected shots on the sample selection strategies, we repeat the evaluation of all the selection strategies in the 10-shot setting (using the same setup and methodology) and compare the results to 5-shot setting. We provide the comparison of distribution of results, i.e., the difference to Classic selection, in Figure 5.

Overall, we observe an increase in the overall performance of the models for all the selection strategies (with the exception of some bad performing ones) as well as the Classic selection. At the same time, we observe lower difference to the Classic selection for all the evaluated selection strategies. This finding holds for all strategies across all models and few-shot learning approaches.

In addition, we also compare the results using Wilcoxon signed-rank test, between the results of 5-shot and 10-shot setting. The resulting p-value ($1.93e-07$) indicates that the different to Classic selection is more significant in case of 5-shot setting.

All in all, these experiments further reinforce the findings from comparison of ACSESS and random selection, where we observed lower impact of sample selection on larger number of shots.

## Appendix F Dataset Specific Results and Visualisations

In this appendix, we present the full results for each combination of dataset and model we use in the experiments. The results are presented using both figures, showing the difference to Classic selection, as well as tables, showing the overall performance of the model on the specific dataset, the standard deviation from multiple runs of the selection strategy and the difference to the Classic selection (while also showing the results for Classic selection). Following is the mapping for different datasets, each presenting results for all the models:

- •
LR_AM.DOG dataset in Figure 6 and Table 4
- •
LR_AM.AWA dataset in Figure 7 and Table 5
- •
VCL.APL dataset in Figure 8 and Table 6
- •
HUM_ACT.ACT_410 dataset in Figure 9 and Table 7
- •
MNF.TEX_DTD dataset in Figure 10 and Table 8
- •
MCR.PRT dataset in Figure 11 and Table 9
- •
MCR.PNU dataset in Figure 12 and Table 10
- •
PLT.FLW dataset in Figure 13 and Table 11
- •
20 News Group dataset in Figure 14 and Table 12
- •
News Category dataset in Figure 15 and Table 13
- •
ATIS dataset in Figure 16 and Table 14
- •
Facebook dataset in Figure 17 and Table 15
- •
HWU-64 dataset in Figure 18 and Table 16
- •
SNIPS dataset in Figure 19 and Table 17

Figure: Figure 6: Benefit of the different selection strategies calculated as the difference to the classic few-shot selection strategy for the LR_AM.DOG dataset. The performance of the classic selection is represented as the zero value.
Refer to caption: extracted/5377787/figures/image/DOG-diff.png

**Table 4: Benefit of the different selection strategies calculated as the difference in accuracy to the classic few-shot selection strategy for the LR_AM.DOG dataset. The subscript represents the standard deviation in the accuracy from running the selection strategy multiple times. The difference to the classic selection baseline (first row) is also included.**
| LR_AM.DOG | ProtoNet | MAML | Fine-Tuning |  |  |  |
| --- | --- | --- | --- | --- | --- | --- |
|  | Acc. + Std | Diff. | Acc. + Std | Diff. | Acc. + Std | Diff. |
| Classic | $70.01_{0.30}$ | $+0.00$ | $57.11_{0.31}$ | $+0.00$ | $65.36_{0.32}$ | $+0.00$ |
| Random | $70.59_{0.21}$ | $+0.58$ | $57.48_{0.21}$ | $+0.37$ | $65.30_{0.18}$ | $-0.05$ |
| Similarity | $70.33_{0.20}$ | $+0.32$ | $57.36_{0.35}$ | $+0.25$ | $65.19_{0.25}$ | $-0.16$ |
| Diversity | $70.31_{0.12}$ | $+0.30$ | $57.00_{0.15}$ | $-0.11$ | $64.92_{0.09}$ | $-0.43$ |
| Entropy | $70.56_{0.11}$ | $+0.55$ | $57.36_{0.09}$ | $+0.25$ | $64.98_{0.23}$ | $-0.38$ |
| Margin | $70.84_{0.31}$ | $+0.84$ | $57.53_{0.38}$ | $+0.42$ | $65.04_{0.33}$ | $-0.31$ |
| Least Confidence | $70.77_{0.14}$ | $+0.76$ | $57.29_{0.04}$ | $+0.18$ | $65.15_{0.12}$ | $-0.20$ |
| Loss | $70.58_{0.20}$ | $+0.57$ | $56.67_{0.13}$ | $-0.44$ | $65.17_{0.20}$ | $-0.18$ |
| CAL | $70.68_{0.04}$ | $+0.67$ | $58.45_{0.17}$ | $+1.33$ | $65.60_{0.09}$ | $+0.25$ |
| CRAIG | $70.82_{0.00}$ | $+0.81$ | $58.05_{0.00}$ | $+0.94$ | $65.62_{0.00}$ | $+0.26$ |
| DeepFool | $70.32_{0.00}$ | $+0.31$ | $56.90_{0.00}$ | $-0.21$ | $65.10_{0.00}$ | $-0.25$ |
| Forgetting | $70.55_{0.00}$ | $+0.54$ | $57.93_{0.00}$ | $+0.82$ | $65.12_{0.00}$ | $-0.23$ |
| Glister | $70.96_{0.00}$ | $+0.95$ | $58.18_{0.00}$ | $+1.07$ | $65.62_{0.00}$ | $+0.27$ |
| GraNd | $70.89_{0.03}$ | $+0.88$ | $59.31_{0.08}$ | $+2.20$ | $66.12_{0.20}$ | $+0.77$ |
| Herding | $69.15_{0.00}$ | $-0.86$ | $49.66_{0.00}$ | $-7.45$ | $63.34_{0.00}$ | $-2.01$ |
| KCenter | $70.77_{0.00}$ | $+0.76$ | $55.56_{0.00}$ | $-1.55$ | $64.86_{0.00}$ | $-0.50$ |
| Graph-Cut | $70.87_{0.00}$ | $+0.86$ | $58.20_{0.00}$ | $+1.09$ | $65.66_{0.00}$ | $+0.31$ |
| CartographyEasy | $71.04_{0.14}$ | $+1.02$ | $58.96_{0.20}$ | $+1.85$ | $65.48_{0.15}$ | $+0.13$ |
| CartographyAmbiguous | $70.46_{0.28}$ | $+0.46$ | $57.30_{0.32}$ | $+0.19$ | $65.08_{0.27}$ | $-0.28$ |
| CartographyHard | $70.02_{0.16}$ | $+0.01$ | $54.08_{0.28}$ | $-3.04$ | $64.58_{0.19}$ | $-0.77$ |
| CartographyEasy+Ambig. | $70.28_{0.28}$ | $+0.27$ | $57.52_{0.32}$ | $+0.41$ | $65.34_{0.34}$ | $-0.01$ |
| ACSESSUniform | $71.04_{0.18}$ | $+1.03$ | $58.81_{0.11}$ | $+1.70$ | $66.25_{0.14}$ | $+0.90$ |
| ACSESSWeighted | $71.15_{0.09}$ | $+1.14$ | $59.60_{0.11}$ | $+2.48$ | $65.87_{0.05}$ | $+0.51$ |
| ACSESSWith Random | $71.05_{0.16}$ | $+1.04$ | $58.89_{0.26}$ | $+1.78$ | $65.55_{0.16}$ | $+0.19$ |

Figure: Figure 7: Benefit of the different selection strategies calculated as the difference to the classic few-shot selection strategy for the LR_AM.AWA dataset. The performance of the classic selection is represented as the zero value.
Refer to caption: extracted/5377787/figures/image/AWA-diff.png

**Table 5: Benefit of the different selection strategies calculated as the difference in accuracy to the classic few-shot selection strategy for the LR_AM.AWA dataset. The subscript represents the standard deviation in the accuracy from running the selection strategy multiple times. The difference to the classic selection baseline (first row) is also included.**
| LR_AM.AWA | ProtoNet | MAML | Fine-Tuning |  |  |  |
| --- | --- | --- | --- | --- | --- | --- |
|  | Acc. + Std | Diff. | Acc. + Std | Diff. | Acc. + Std | Diff. |
| Classic | $76.32_{0.30}$ | $+0.00$ | $71.32_{0.27}$ | $+0.00$ | $79.16_{0.40}$ | $+0.00$ |
| Random | $76.35_{0.25}$ | $+0.03$ | $71.19_{0.16}$ | $-0.13$ | $79.17_{0.08}$ | $+0.01$ |
| Similarity | $75.80_{0.21}$ | $-0.52$ | $70.49_{0.23}$ | $-0.84$ | $77.40_{0.34}$ | $-1.77$ |
| Diversity | $76.11_{0.16}$ | $-0.21$ | $70.46_{0.13}$ | $-0.87$ | $77.80_{0.14}$ | $-1.37$ |
| Entropy | $76.46_{0.27}$ | $+0.14$ | $71.10_{0.08}$ | $-0.23$ | $79.09_{0.23}$ | $-0.07$ |
| Margin | $76.63_{0.16}$ | $+0.31$ | $71.03_{0.22}$ | $-0.30$ | $79.26_{0.20}$ | $+0.09$ |
| Least Confidence | $76.08_{0.27}$ | $-0.24$ | $70.91_{0.21}$ | $-0.42$ | $79.15_{0.10}$ | $-0.02$ |
| Loss | $75.93_{0.14}$ | $-0.39$ | $70.46_{0.24}$ | $-0.86$ | $77.88_{0.00}$ | $-1.28$ |
| CAL | $76.84_{0.25}$ | $+0.52$ | $71.61_{0.29}$ | $+0.28$ | $78.85_{0.29}$ | $-0.31$ |
| CRAIG | $76.56_{0.00}$ | $+0.24$ | $71.46_{0.00}$ | $+0.13$ | $79.48_{0.00}$ | $+0.32$ |
| DeepFool | $75.68_{0.00}$ | $-0.64$ | $70.51_{0.01}$ | $-0.82$ | $78.15_{0.01}$ | $-1.02$ |
| Forgetting | $76.34_{0.00}$ | $+0.02$ | $71.28_{0.00}$ | $-0.04$ | $79.40_{0.00}$ | $+0.24$ |
| Glister | $76.18_{0.00}$ | $-0.14$ | $71.43_{0.00}$ | $+0.10$ | $79.32_{0.00}$ | $+0.16$ |
| GraNd | $77.13_{0.20}$ | $+0.81$ | $71.52_{0.05}$ | $+0.19$ | $79.34_{0.41}$ | $+0.18$ |
| Herding | $75.24_{0.00}$ | $-1.08$ | $69.19_{0.00}$ | $-2.13$ | $74.88_{0.00}$ | $-4.28$ |
| KCenter | $76.07_{0.00}$ | $-0.25$ | $69.96_{0.00}$ | $-1.36$ | $78.24_{0.00}$ | $-0.92$ |
| Graph-Cut | $76.84_{0.00}$ | $+0.52$ | $71.74_{0.00}$ | $+0.42$ | $79.92_{0.00}$ | $+0.76$ |
| CartographyEasy | $77.75_{0.15}$ | $+1.43$ | $72.07_{0.12}$ | $+0.75$ | $79.31_{0.23}$ | $+0.14$ |
| CartographyAmbiguous | $76.74_{0.43}$ | $+0.42$ | $71.26_{0.32}$ | $-0.07$ | $79.00_{0.49}$ | $-0.16$ |
| CartographyHard | $74.64_{0.19}$ | $-1.68$ | $69.11_{0.16}$ | $-2.22$ | $77.22_{0.30}$ | $-1.94$ |
| CartographyEasy+Ambig. | $76.73_{0.28}$ | $+0.41$ | $71.86_{0.30}$ | $+0.54$ | $79.76_{0.34}$ | $+0.60$ |
| ACSESSUniform | $77.80_{0.05}$ | $+1.48$ | $72.17_{0.14}$ | $+0.85$ | $80.17_{0.15}$ | $+1.00$ |
| ACSESSWeighted | $77.97_{0.09}$ | $+1.65$ | $72.27_{0.07}$ | $+0.95$ | $80.00_{0.10}$ | $+0.83$ |
| ACSESSWith Random | $77.62_{0.15}$ | $+1.30$ | $72.16_{0.18}$ | $+0.84$ | $79.78_{0.15}$ | $+0.61$ |

Figure: Figure 8: Benefit of the different selection strategies calculated as the difference to the classic few-shot selection strategy for the VCL.APL dataset. The performance of the classic selection is represented as the zero value.
Refer to caption: extracted/5377787/figures/image/APL-diff.png

**Table 6: Benefit of the different selection strategies calculated as the difference in accuracy to the classic few-shot selection strategy for the VCL.APL dataset. The subscript represents the standard deviation in the accuracy from running the selection strategy multiple times. The difference to the classic selection baseline (first row) is also included.**
| VCL.APL | ProtoNet | MAML | Fine-Tuning |  |  |  |
| --- | --- | --- | --- | --- | --- | --- |
|  | Acc. + Std | Diff. | Acc. + Std | Diff. | Acc. + Std | Diff. |
| Classic | $98.03_{0.09}$ | $+0.00$ | $96.95_{0.09}$ | $+0.00$ | $97.26_{0.09}$ | $+0.00$ |
| Random | $97.84_{0.08}$ | $-0.18$ | $96.60_{0.07}$ | $-0.35$ | $97.14_{0.09}$ | $-0.12$ |
| Similarity | $97.51_{0.20}$ | $-0.52$ | $96.52_{0.28}$ | $-0.43$ | $96.40_{0.54}$ | $-0.87$ |
| Diversity | $97.82_{0.03}$ | $-0.21$ | $96.76_{0.03}$ | $-0.19$ | $97.35_{0.05}$ | $+0.09$ |
| Entropy | $97.80_{0.03}$ | $-0.22$ | $96.64_{0.05}$ | $-0.31$ | $97.02_{0.05}$ | $-0.25$ |
| Margin | $97.82_{0.09}$ | $-0.21$ | $96.72_{0.05}$ | $-0.23$ | $97.18_{0.11}$ | $-0.08$ |
| Least Confidence | $97.91_{0.08}$ | $-0.12$ | $96.76_{0.05}$ | $-0.19$ | $97.10_{0.13}$ | $-0.16$ |
| Loss | $97.74_{0.02}$ | $-0.29$ | $96.69_{0.02}$ | $-0.26$ | $97.24_{0.12}$ | $-0.03$ |
| CAL | $97.28_{0.04}$ | $-0.74$ | $95.94_{0.07}$ | $-1.01$ | $96.37_{0.25}$ | $-0.90$ |
| CRAIG | $97.77_{0.00}$ | $-0.25$ | $96.68_{0.00}$ | $-0.28$ | $97.18_{0.00}$ | $-0.09$ |
| DeepFool | $97.86_{0.00}$ | $-0.16$ | $96.54_{0.00}$ | $-0.41$ | $97.14_{0.01}$ | $-0.13$ |
| Forgetting | $97.72_{0.00}$ | $-0.31$ | $96.42_{0.00}$ | $-0.53$ | $96.92_{0.00}$ | $-0.35$ |
| Glister | $97.70_{0.00}$ | $-0.33$ | $96.56_{0.00}$ | $-0.39$ | $96.91_{0.00}$ | $-0.36$ |
| GraNd | $97.69_{0.13}$ | $-0.34$ | $96.56_{0.32}$ | $-0.39$ | $96.77_{0.24}$ | $-0.49$ |
| Herding | $97.25_{0.00}$ | $-0.78$ | $96.78_{0.00}$ | $-0.17$ | $96.45_{0.00}$ | $-0.81$ |
| KCenter | $97.78_{0.00}$ | $-0.24$ | $96.71_{0.00}$ | $-0.24$ | $97.28_{0.00}$ | $+0.02$ |
| Graph-Cut | $97.81_{0.00}$ | $-0.22$ | $96.69_{0.00}$ | $-0.26$ | $97.05_{0.00}$ | $-0.21$ |
| CartographyEasy | $97.90_{0.12}$ | $-0.12$ | $96.68_{0.04}$ | $-0.26$ | $97.26_{0.24}$ | $-0.00$ |
| CartographyAmbiguous | $97.45_{0.18}$ | $-0.58$ | $96.26_{0.11}$ | $-0.69$ | $96.42_{0.42}$ | $-0.84$ |
| CartographyHard | $97.21_{0.19}$ | $-0.82$ | $95.70_{0.08}$ | $-1.25$ | $96.38_{0.34}$ | $-0.88$ |
| CartographyEasy+Ambig. | $97.35_{0.21}$ | $-0.68$ | $96.32_{0.12}$ | $-0.63$ | $96.47_{0.32}$ | $-0.80$ |
| ACSESSUniform | $97.73_{0.05}$ | $-0.30$ | $96.84_{0.09}$ | $-0.11$ | $97.44_{0.05}$ | $+0.17$ |
| ACSESSWeighted | $98.18_{0.06}$ | $+0.16$ | $97.16_{0.03}$ | $+0.21$ | $96.96_{0.18}$ | $-0.30$ |
| ACSESSWith Random | $97.72_{0.08}$ | $-0.30$ | $96.71_{0.02}$ | $-0.24$ | $96.91_{0.16}$ | $-0.36$ |

Figure: Figure 9: Benefit of the different selection strategies calculated as the difference to the classic few-shot selection strategy for the HUM_ACT.ACT_410 dataset. The performance of the classic selection is represented as the zero value.
Refer to caption: extracted/5377787/figures/image/ACT_410-diff.png

**Table 7: Benefit of the different selection strategies calculated as the difference in accuracy to the classic few-shot selection strategy for the HUM_ACT.ACT_410 dataset. The subscript represents the standard deviation in the accuracy from running the selection strategy multiple times. The difference to the classic selection baseline (first row) is also included.**
| HUM_ACT.ACT_410 | ProtoNet | MAML | Fine-Tuning |  |  |  |
| --- | --- | --- | --- | --- | --- | --- |
|  | Acc. + Std | Diff. | Acc. + Std | Diff. | Acc. + Std | Diff. |
| Classic | $71.38_{0.35}$ | $+0.00$ | $62.65_{0.38}$ | $+0.00$ | $69.43_{0.53}$ | $+0.00$ |
| Random | $71.77_{0.34}$ | $+0.40$ | $63.06_{0.09}$ | $+0.41$ | $69.33_{0.50}$ | $-0.10$ |
| Similarity | $71.66_{0.17}$ | $+0.28$ | $63.10_{0.08}$ | $+0.45$ | $69.56_{0.57}$ | $+0.13$ |
| Diversity | $71.52_{0.20}$ | $+0.14$ | $63.05_{0.08}$ | $+0.40$ | $69.39_{0.18}$ | $-0.04$ |
| Entropy | $71.37_{0.32}$ | $-0.00$ | $63.08_{0.07}$ | $+0.43$ | $69.19_{0.18}$ | $-0.24$ |
| Margin | $71.71_{0.29}$ | $+0.34$ | $63.02_{0.08}$ | $+0.38$ | $69.45_{0.31}$ | $+0.02$ |
| Least Confidence | $71.28_{0.16}$ | $-0.10$ | $63.17_{0.10}$ | $+0.52$ | $69.55_{0.33}$ | $+0.12$ |
| Loss | $71.88_{0.12}$ | $+0.50$ | $63.10_{0.15}$ | $+0.45$ | $69.36_{0.30}$ | $-0.07$ |
| CAL | $72.13_{0.20}$ | $+0.75$ | $63.09_{0.10}$ | $+0.44$ | $69.20_{0.28}$ | $-0.23$ |
| CRAIG | $71.65_{0.00}$ | $+0.27$ | $63.19_{0.00}$ | $+0.54$ | $69.30_{0.00}$ | $-0.13$ |
| DeepFool | $71.55_{0.20}$ | $+0.18$ | $63.26_{0.07}$ | $+0.62$ | $68.93_{0.30}$ | $-0.50$ |
| Forgetting | $71.58_{0.03}$ | $+0.20$ | $63.26_{0.03}$ | $+0.62$ | $69.66_{0.14}$ | $+0.23$ |
| Glister | $71.65_{0.00}$ | $+0.27$ | $63.19_{0.00}$ | $+0.54$ | $69.30_{0.00}$ | $-0.13$ |
| GraNd | $71.67_{0.07}$ | $+0.29$ | $63.15_{0.04}$ | $+0.50$ | $69.79_{0.40}$ | $+0.36$ |
| Herding | $71.18_{0.11}$ | $-0.19$ | $63.14_{0.04}$ | $+0.49$ | $69.15_{0.11}$ | $-0.28$ |
| KCenter | $71.32_{0.37}$ | $-0.05$ | $63.02_{0.07}$ | $+0.38$ | $69.01_{0.27}$ | $-0.42$ |
| Graph-Cut | $71.65_{0.00}$ | $+0.27$ | $63.19_{0.00}$ | $+0.54$ | $69.30_{0.00}$ | $-0.13$ |
| CartographyEasy | $71.93_{0.23}$ | $+0.56$ | $63.19_{0.06}$ | $+0.55$ | $69.89_{0.23}$ | $+0.46$ |
| CartographyAmbiguous | $71.29_{0.35}$ | $-0.09$ | $62.53_{0.40}$ | $-0.12$ | $69.68_{0.47}$ | $+0.25$ |
| CartographyHard | $71.49_{0.34}$ | $+0.12$ | $63.00_{0.07}$ | $+0.35$ | $68.48_{0.32}$ | $-0.95$ |
| CartographyEasy+Ambig. | $71.44_{0.35}$ | $+0.06$ | $62.51_{0.32}$ | $-0.14$ | $69.78_{0.37}$ | $+0.35$ |
| ACSESSUniform | $72.78_{0.12}$ | $+1.40$ | $63.45_{0.20}$ | $+0.80$ | $70.27_{0.22}$ | $+0.84$ |
| ACSESSWeighted | $73.08_{0.25}$ | $+1.70$ | $63.55_{0.26}$ | $+0.90$ | $69.90_{0.27}$ | $+0.47$ |
| ACSESSWith Random | $72.66_{0.56}$ | $+1.28$ | $63.28_{0.54}$ | $+0.63$ | $69.26_{0.51}$ | $-0.17$ |

Figure: Figure 10: Benefit of the different selection strategies calculated as the difference to the classic few-shot selection strategy for the MNF.TEX_DTD dataset. The performance of the classic selection is represented as the zero value.
Refer to caption: extracted/5377787/figures/image/TEX_DTD-diff.png

**Table 8: Benefit of the different selection strategies calculated as the difference in accuracy to the classic few-shot selection strategy for the MNF.TEX_DTD dataset. The subscript represents the standard deviation in the accuracy from running the selection strategy multiple times. The difference to the classic selection baseline (first row) is also included.**
| MNF.TEX_DTD | ProtoNet | MAML | Fine-Tuning |  |  |  |
| --- | --- | --- | --- | --- | --- | --- |
|  | Acc. + Std | Diff. | Acc. + Std | Diff. | Acc. + Std | Diff. |
| Classic | $58.56_{0.42}$ | $+0.00$ | $55.32_{0.33}$ | $+0.00$ | $55.15_{0.24}$ | $+0.00$ |
| Random | $58.38_{0.61}$ | $-0.18$ | $55.00_{0.61}$ | $-0.32$ | $54.90_{0.38}$ | $-0.24$ |
| Similarity | $55.07_{0.86}$ | $-3.49$ | $52.56_{0.76}$ | $-2.77$ | $52.99_{0.56}$ | $-2.16$ |
| Diversity | $56.61_{0.23}$ | $-1.95$ | $51.63_{0.22}$ | $-3.69$ | $53.83_{0.12}$ | $-1.32$ |
| Entropy | $57.64_{0.20}$ | $-0.92$ | $54.50_{0.81}$ | $-0.82$ | $54.57_{0.40}$ | $-0.57$ |
| Margin | $58.37_{0.35}$ | $-0.19$ | $55.11_{0.20}$ | $-0.22$ | $55.34_{0.32}$ | $+0.19$ |
| Least Confidence | $58.02_{0.15}$ | $-0.54$ | $54.91_{0.38}$ | $-0.41$ | $55.07_{0.28}$ | $-0.08$ |
| Loss | $55.70_{0.30}$ | $-2.86$ | $52.79_{0.33}$ | $-2.53$ | $54.20_{0.28}$ | $-0.95$ |
| CAL | $58.62_{0.30}$ | $+0.06$ | $56.05_{0.30}$ | $+0.73$ | $55.73_{0.25}$ | $+0.58$ |
| CRAIG | $58.73_{0.00}$ | $+0.17$ | $55.85_{0.00}$ | $+0.52$ | $55.62_{0.00}$ | $+0.48$ |
| DeepFool | $57.21_{0.43}$ | $-1.36$ | $54.66_{0.09}$ | $-0.66$ | $54.34_{0.16}$ | $-0.81$ |
| Forgetting | $59.28_{0.09}$ | $+0.72$ | $56.21_{0.29}$ | $+0.89$ | $55.83_{0.10}$ | $+0.68$ |
| Glister | $58.73_{0.00}$ | $+0.17$ | $55.85_{0.00}$ | $+0.52$ | $55.62_{0.00}$ | $+0.48$ |
| GraNd | $58.33_{0.52}$ | $-0.23$ | $56.15_{0.37}$ | $+0.83$ | $55.31_{0.27}$ | $+0.16$ |
| Herding | $53.99_{0.50}$ | $-4.57$ | $51.27_{0.49}$ | $-4.06$ | $51.71_{0.19}$ | $-3.44$ |
| KCenter | $54.96_{0.30}$ | $-3.60$ | $53.41_{0.10}$ | $-1.92$ | $54.45_{0.12}$ | $-0.70$ |
| Graph-Cut | $58.73_{0.00}$ | $+0.17$ | $55.85_{0.00}$ | $+0.52$ | $55.62_{0.00}$ | $+0.48$ |
| CartographyEasy | $59.42_{0.30}$ | $+0.86$ | $56.52_{0.25}$ | $+1.20$ | $55.88_{0.28}$ | $+0.74$ |
| CartographyAmbiguous | $58.46_{0.38}$ | $-0.10$ | $55.07_{0.34}$ | $-0.26$ | $55.26_{0.34}$ | $+0.11$ |
| CartographyHard | $48.76_{0.55}$ | $-9.80$ | $48.68_{0.42}$ | $-6.64$ | $50.78_{0.27}$ | $-4.37$ |
| CartographyEasy+Ambig. | $58.97_{0.14}$ | $+0.41$ | $55.71_{0.28}$ | $+0.38$ | $55.47_{0.31}$ | $+0.32$ |
| ACSESSUniform | $59.96_{0.14}$ | $+1.40$ | $57.18_{0.13}$ | $+1.85$ | $56.28_{0.26}$ | $+1.13$ |
| ACSESSWeighted | $60.10_{0.30}$ | $+1.54$ | $57.33_{0.26}$ | $+2.00$ | $56.21_{0.27}$ | $+1.07$ |
| ACSESSWith Random | $59.82_{0.60}$ | $+1.26$ | $56.57_{0.46}$ | $+1.24$ | $56.03_{0.50}$ | $+0.89$ |

Figure: Figure 11: Benefit of the different selection strategies calculated as the difference to the classic few-shot selection strategy for the MCR.PRT dataset. The performance of the classic selection is represented as the zero value.
Refer to caption: extracted/5377787/figures/image/PRT-diff.png

**Table 9: Benefit of the different selection strategies calculated as the difference in accuracy to the classic few-shot selection strategy for the MCR.PRT dataset. The subscript represents the standard deviation in the accuracy from running the selection strategy multiple times. The difference to the classic selection baseline (first row) is also included.**
| MCR.PRT | ProtoNet | MAML | Fine-Tuning |  |  |  |
| --- | --- | --- | --- | --- | --- | --- |
|  | Acc. + Std | Diff. | Acc. + Std | Diff. | Acc. + Std | Diff. |
| Classic | $39.85_{0.39}$ | $+0.00$ | $28.44_{0.29}$ | $+0.00$ | $37.57_{0.26}$ | $+0.00$ |
| Random | $39.86_{0.25}$ | $+0.01$ | $28.34_{1.41}$ | $-0.09$ | $37.24_{0.59}$ | $-0.33$ |
| Similarity | $39.79_{0.22}$ | $-0.06$ | $26.50_{2.06}$ | $-1.93$ | $36.51_{0.42}$ | $-1.06$ |
| Diversity | $39.61_{0.22}$ | $-0.24$ | $27.09_{0.48}$ | $-1.35$ | $37.48_{0.19}$ | $-0.09$ |
| Entropy | $39.92_{0.06}$ | $+0.07$ | $28.46_{1.05}$ | $+0.03$ | $37.14_{0.33}$ | $-0.43$ |
| Margin | $39.70_{0.29}$ | $-0.15$ | $28.86_{0.51}$ | $+0.42$ | $37.27_{0.28}$ | $-0.29$ |
| Least Confidence | $39.64_{0.10}$ | $-0.21$ | $27.91_{0.49}$ | $-0.53$ | $37.50_{0.15}$ | $-0.07$ |
| Loss | $39.94_{0.29}$ | $+0.09$ | $25.78_{1.39}$ | $-2.66$ | $37.15_{0.46}$ | $-0.42$ |
| CAL | $40.08_{0.38}$ | $+0.22$ | $30.12_{0.69}$ | $+1.68$ | $37.15_{0.13}$ | $-0.42$ |
| CRAIG | $40.02_{0.00}$ | $+0.17$ | $27.48_{0.00}$ | $-0.95$ | $36.47_{0.00}$ | $-1.10$ |
| DeepFool | $39.42_{0.00}$ | $-0.43$ | $24.95_{0.00}$ | $-3.48$ | $37.29_{0.00}$ | $-0.28$ |
| Forgetting | $39.63_{0.00}$ | $-0.22$ | $28.81_{0.00}$ | $+0.37$ | $37.76_{0.00}$ | $+0.19$ |
| Glister | $40.02_{0.00}$ | $+0.17$ | $27.48_{0.00}$ | $-0.95$ | $36.47_{0.00}$ | $-1.10$ |
| GraNd | $39.89_{0.14}$ | $+0.04$ | $30.90_{0.75}$ | $+2.47$ | $37.66_{0.30}$ | $+0.09$ |
| Herding | $39.50_{0.00}$ | $-0.36$ | $30.80_{0.00}$ | $+2.37$ | $37.82_{0.00}$ | $+0.25$ |
| KCenter | $40.04_{0.00}$ | $+0.18$ | $27.82_{0.00}$ | $-0.61$ | $37.24_{0.00}$ | $-0.33$ |
| Graph-Cut | $40.02_{0.00}$ | $+0.17$ | $27.48_{0.00}$ | $-0.95$ | $36.47_{0.00}$ | $-1.10$ |
| CartographyEasy | $40.42_{0.20}$ | $+0.57$ | $31.04_{0.62}$ | $+2.60$ | $37.54_{0.20}$ | $-0.03$ |
| CartographyAmbiguous | $39.82_{0.37}$ | $-0.04$ | $26.91_{0.84}$ | $-1.52$ | $37.56_{0.43}$ | $-0.00$ |
| CartographyHard | $39.52_{0.16}$ | $-0.34$ | $25.46_{1.18}$ | $-2.97$ | $36.93_{0.17}$ | $-0.63$ |
| CartographyEasy+Ambig. | $39.80_{0.24}$ | $-0.05$ | $29.04_{0.57}$ | $+0.60$ | $37.35_{0.36}$ | $-0.22$ |
| ACSESSUniform | $41.03_{0.19}$ | $+1.18$ | $31.31_{0.26}$ | $+2.88$ | $37.97_{0.26}$ | $+0.40$ |
| ACSESSWeighted | $41.25_{0.24}$ | $+1.40$ | $31.41_{0.39}$ | $+2.97$ | $37.68_{0.36}$ | $+0.11$ |
| ACSESSWith Random | $40.84_{0.47}$ | $+0.99$ | $31.09_{0.69}$ | $+2.65$ | $36.63_{0.86}$ | $-0.93$ |

Figure: Figure 12: Benefit of the different selection strategies calculated as the difference to the classic few-shot selection strategy for the MCR.PNU dataset. The performance of the classic selection is represented as the zero value.
Refer to caption: extracted/5377787/figures/image/PNU-diff.png

**Table 10: Benefit of the different selection strategies calculated as the difference in accuracy to the classic few-shot selection strategy for the MCR.PNU dataset. The subscript represents the standard deviation in the accuracy from running the selection strategy multiple times. The difference to the classic selection baseline (first row) is also included.**
| MCR.PNU | ProtoNet | MAML | Fine-Tuning |  |  |  |
| --- | --- | --- | --- | --- | --- | --- |
|  | Acc. + Std | Diff. | Acc. + Std | Diff. | Acc. + Std | Diff. |
| Classic | $37.34_{0.31}$ | $+0.00$ | $27.32_{0.34}$ | $+0.00$ | $51.25_{0.26}$ | $+0.00$ |
| Random | $37.27_{0.41}$ | $-0.07$ | $27.43_{0.82}$ | $+0.11$ | $51.15_{0.55}$ | $-0.10$ |
| Similarity | $36.52_{1.17}$ | $-0.82$ | $24.78_{1.33}$ | $-2.54$ | $47.48_{0.87}$ | $-3.77$ |
| Diversity | $36.86_{0.17}$ | $-0.47$ | $25.82_{0.28}$ | $-1.51$ | $50.05_{0.31}$ | $-1.20$ |
| Entropy | $37.42_{0.22}$ | $+0.08$ | $26.99_{1.29}$ | $-0.33$ | $51.17_{0.43}$ | $-0.08$ |
| Margin | $37.66_{0.46}$ | $+0.33$ | $27.38_{0.48}$ | $+0.05$ | $51.81_{0.79}$ | $+0.56$ |
| Least Confidence | $37.80_{0.09}$ | $+0.46$ | $26.20_{0.63}$ | $-1.12$ | $51.43_{0.38}$ | $+0.18$ |
| Loss | $36.73_{0.22}$ | $-0.60$ | $25.67_{0.42}$ | $-1.65$ | $49.82_{0.63}$ | $-1.44$ |
| CAL | $37.97_{0.20}$ | $+0.63$ | $28.65_{1.12}$ | $+1.32$ | $49.29_{0.51}$ | $-1.96$ |
| CRAIG | $37.40_{0.00}$ | $+0.06$ | $28.27_{0.00}$ | $+0.95$ | $51.15_{0.00}$ | $-0.10$ |
| DeepFool | $37.47_{0.00}$ | $+0.13$ | $26.78_{0.00}$ | $-0.55$ | $50.79_{0.00}$ | $-0.46$ |
| Forgetting | $37.86_{0.00}$ | $+0.52$ | $29.35_{0.00}$ | $+2.03$ | $50.96_{0.00}$ | $-0.29$ |
| Glister | $37.83_{0.00}$ | $+0.49$ | $28.86_{0.00}$ | $+1.53$ | $51.12_{0.00}$ | $-0.14$ |
| GraNd | $37.07_{0.24}$ | $-0.27$ | $29.50_{0.39}$ | $+2.17$ | $47.25_{0.44}$ | $-4.00$ |
| Herding | $35.35_{0.00}$ | $-1.98$ | $23.21_{0.00}$ | $-4.11$ | $46.32_{0.00}$ | $-4.93$ |
| KCenter | $37.40_{0.00}$ | $+0.06$ | $24.93_{0.00}$ | $-2.39$ | $50.68_{0.00}$ | $-0.57$ |
| Graph-Cut | $38.13_{0.00}$ | $+0.79$ | $29.01_{0.00}$ | $+1.68$ | $51.51_{0.00}$ | $+0.26$ |
| CartographyEasy | $38.32_{0.30}$ | $+0.98$ | $30.18_{0.60}$ | $+2.86$ | $51.65_{0.34}$ | $+0.40$ |
| CartographyAmbiguous | $36.73_{0.23}$ | $-0.60$ | $26.94_{0.24}$ | $-0.38$ | $50.77_{0.37}$ | $-0.48$ |
| CartographyHard | $31.34_{0.64}$ | $-5.99$ | $20.87_{0.53}$ | $-6.45$ | $47.36_{0.73}$ | $-3.89$ |
| CartographyEasy+Ambig. | $37.35_{0.12}$ | $+0.02$ | $27.87_{0.26}$ | $+0.54$ | $51.44_{0.26}$ | $+0.19$ |
| ACSESSUniform | $39.86_{0.20}$ | $+2.52$ | $30.20_{0.17}$ | $+2.88$ | $52.56_{0.20}$ | $+1.31$ |
| ACSESSWeighted | $39.95_{0.21}$ | $+2.61$ | $30.35_{0.26}$ | $+3.03$ | $52.05_{0.40}$ | $+0.80$ |
| ACSESSWith Random | $38.75_{0.40}$ | $+1.41$ | $29.86_{0.58}$ | $+2.54$ | $51.02_{0.92}$ | $-0.23$ |

Figure: Figure 13: Benefit of the different selection strategies calculated as the difference to the classic few-shot selection strategy for the PLT.FLW dataset. The performance of the classic selection is represented as the zero value.
Refer to caption: extracted/5377787/figures/image/FLW-diff.png

**Table 11: Benefit of the different selection strategies calculated as the difference in accuracy to the classic few-shot selection strategy for the PLT.FLW dataset. The subscript represents the standard deviation in the accuracy from running the selection strategy multiple times. The difference to the classic selection baseline (first row) is also included.**
| PLT.FLW | ProtoNet | MAML | Fine-Tuning |  |  |  |
| --- | --- | --- | --- | --- | --- | --- |
|  | Acc. + Std | Diff. | Acc. + Std | Diff. | Acc. + Std | Diff. |
| Classic | $91.41_{0.25}$ | $+0.00$ | $85.47_{0.31}$ | $+0.00$ | $91.72_{0.22}$ | $+0.00$ |
| Random | $91.29_{0.12}$ | $-0.12$ | $85.45_{0.16}$ | $-0.01$ | $91.82_{0.12}$ | $+0.10$ |
| Similarity | $91.07_{0.13}$ | $-0.34$ | $84.86_{0.29}$ | $-0.61$ | $91.72_{0.18}$ | $-0.00$ |
| Diversity | $91.25_{0.07}$ | $-0.16$ | $85.37_{0.15}$ | $-0.10$ | $91.54_{0.09}$ | $-0.18$ |
| Entropy | $90.80_{0.26}$ | $-0.61$ | $85.09_{0.19}$ | $-0.38$ | $91.66_{0.10}$ | $-0.07$ |
| Margin | $91.33_{0.22}$ | $-0.08$ | $85.33_{0.14}$ | $-0.14$ | $91.71_{0.07}$ | $-0.01$ |
| Least Confidence | $90.99_{0.08}$ | $-0.42$ | $84.96_{0.33}$ | $-0.50$ | $91.71_{0.06}$ | $-0.01$ |
| Loss | $90.91_{0.16}$ | $-0.50$ | $85.26_{0.16}$ | $-0.20$ | $91.87_{0.04}$ | $+0.14$ |
| CAL | $90.85_{0.02}$ | $-0.56$ | $84.98_{0.33}$ | $-0.49$ | $91.30_{0.10}$ | $-0.43$ |
| CRAIG | $91.37_{0.01}$ | $-0.04$ | $85.67_{0.00}$ | $+0.20$ | $91.78_{0.00}$ | $+0.06$ |
| DeepFool | $91.06_{0.20}$ | $-0.35$ | $84.81_{0.15}$ | $-0.65$ | $91.92_{0.12}$ | $+0.19$ |
| Forgetting | $91.24_{0.15}$ | $-0.18$ | $85.59_{0.14}$ | $+0.12$ | $91.49_{0.08}$ | $-0.24$ |
| Glister | $91.42_{0.01}$ | $+0.01$ | $85.67_{0.00}$ | $+0.20$ | $91.78_{0.00}$ | $+0.06$ |
| GraNd | $90.92_{0.19}$ | $-0.49$ | $85.06_{0.08}$ | $-0.40$ | $91.26_{0.08}$ | $-0.46$ |
| Herding | $89.76_{0.08}$ | $-1.65$ | $81.64_{0.34}$ | $-3.82$ | $90.12_{0.10}$ | $-1.60$ |
| KCenter | $90.96_{0.01}$ | $-0.45$ | $84.91_{0.13}$ | $-0.56$ | $91.75_{0.14}$ | $+0.02$ |
| Graph-Cut | $91.38_{0.00}$ | $-0.03$ | $85.64_{0.04}$ | $+0.17$ | $91.75_{0.01}$ | $+0.02$ |
| CartographyEasy | $91.35_{0.08}$ | $-0.06$ | $85.25_{0.15}$ | $-0.22$ | $91.55_{0.10}$ | $-0.18$ |
| CartographyAmbiguous | $91.27_{0.27}$ | $-0.14$ | $85.45_{0.35}$ | $-0.02$ | $91.45_{0.16}$ | $-0.28$ |
| CartographyHard | $90.59_{0.16}$ | $-0.82$ | $83.43_{0.20}$ | $-2.04$ | $91.46_{0.11}$ | $-0.26$ |
| CartographyEasy+Ambig. | $91.35_{0.21}$ | $-0.06$ | $85.72_{0.15}$ | $+0.25$ | $91.59_{0.18}$ | $-0.13$ |
| ACSESSUniform | $91.63_{0.10}$ | $+0.22$ | $87.34_{0.29}$ | $+1.87$ | $92.47_{0.13}$ | $+0.75$ |
| ACSESSWeighted | $91.66_{0.16}$ | $+0.25$ | $87.53_{0.37}$ | $+2.06$ | $92.17_{0.22}$ | $+0.44$ |
| ACSESSWith Random | $91.20_{0.69}$ | $-0.21$ | $86.56_{0.46}$ | $+1.09$ | $91.22_{0.64}$ | $-0.50$ |

Figure: Figure 14: Benefit of the different selection strategies calculated as the difference to the classic few-shot selection strategy for the 20 News Group dataset. The performance of the classic selection is represented as the zero value.
Refer to caption: extracted/5377787/figures/text/20News-diff.png

**Table 12: Benefit of the different selection strategies calculated as the difference in accuracy to the classic few-shot selection strategy for the 20 News Group dataset. The subscript represents the standard deviation in the accuracy from running the selection strategy multiple times. The difference to the classic selection baseline (first row) is also included.**
| 20 News Group | ProtoNet | MAML | Fine-Tuning | Mistral | Zephyr |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
|  | Acc. + Std | Diff. | Acc. + Std | Diff. | Acc. + Std | Diff. | Acc. + Std | Diff. | Acc. + Std | Diff. |
| Classic | $59.41_{0.26}$ | $+0.00$ | $56.03_{0.29}$ | $+0.00$ | $43.88_{0.26}$ | $+0.00$ | $75.81_{1.24}$ | $+0.00$ | $74.58_{1.24}$ | $+0.00$ |
| Random | $59.47_{0.98}$ | $+0.06$ | $56.23_{0.84}$ | $+0.21$ | $43.62_{1.04}$ | $-0.26$ | $76.02_{0.93}$ | $+0.21$ | $75.66_{0.66}$ | $+1.08$ |
| LENS | N/A | N/A | N/A | N/A | N/A | N/A | $77.30_{0.43}$ | $+1.49$ | $76.42_{0.33}$ | $+1.84$ |
| Similarity | $53.40_{1.56}$ | $-6.01$ | $49.69_{1.35}$ | $-6.34$ | $39.33_{1.42}$ | $-4.55$ | $75.86_{0.91}$ | $+0.05$ | $75.70_{0.46}$ | $+1.11$ |
| Diversity | $58.41_{0.31}$ | $-1.00$ | $55.33_{0.27}$ | $-0.70$ | $43.34_{0.33}$ | $-0.54$ | $76.38_{0.34}$ | $+0.57$ | $74.46_{0.44}$ | $-0.12$ |
| Entropy | $59.85_{0.21}$ | $+0.44$ | $54.57_{0.54}$ | $-1.46$ | $46.09_{0.83}$ | $+2.21$ | $76.97_{1.02}$ | $+1.16$ | $75.89_{0.49}$ | $+1.31$ |
| Margin | $59.68_{1.34}$ | $+0.27$ | $56.02_{0.35}$ | $-0.01$ | $44.70_{0.36}$ | $+0.82$ | $76.99_{0.62}$ | $+1.18$ | $75.52_{0.34}$ | $+0.94$ |
| Least Confidence | $59.86_{0.65}$ | $+0.45$ | $54.28_{0.72}$ | $-1.74$ | $44.76_{2.45}$ | $+0.88$ | $76.93_{0.59}$ | $+1.12$ | $75.55_{0.56}$ | $+0.96$ |
| Loss | $57.23_{0.95}$ | $-2.18$ | $54.12_{1.72}$ | $-1.91$ | $42.62_{1.53}$ | $-1.27$ | $75.63_{1.18}$ | $-0.18$ | $75.25_{1.00}$ | $+0.67$ |
| CAL | $59.28_{0.00}$ | $-0.13$ | $54.98_{0.00}$ | $-1.05$ | $42.61_{0.00}$ | $-1.27$ | $76.56_{0.00}$ | $+0.75$ | $76.24_{0.00}$ | $+1.66$ |
| CRAIG | $60.45_{0.01}$ | $+1.04$ | $57.16_{0.00}$ | $+1.13$ | $45.04_{0.17}$ | $+1.16$ | $76.13_{0.04}$ | $+0.32$ | $75.25_{0.30}$ | $+0.67$ |
| DeepFool | $59.28_{0.00}$ | $-0.13$ | $54.98_{0.00}$ | $-1.05$ | $42.61_{0.00}$ | $-1.27$ | $76.56_{0.00}$ | $+0.75$ | $76.24_{0.00}$ | $+1.66$ |
| Forgetting | $60.06_{0.00}$ | $+0.65$ | $56.38_{0.00}$ | $+0.35$ | $44.78_{0.00}$ | $+0.90$ | $76.60_{0.00}$ | $+0.79$ | $75.72_{0.00}$ | $+1.14$ |
| Glister | $60.32_{0.00}$ | $+0.91$ | $56.71_{0.00}$ | $+0.68$ | $44.60_{0.00}$ | $+0.72$ | $75.92_{0.00}$ | $+0.11$ | $75.28_{0.00}$ | $+0.70$ |
| GraNd | $58.70_{0.74}$ | $-0.71$ | $55.28_{1.03}$ | $-0.74$ | $44.55_{0.73}$ | $+0.67$ | $75.73_{0.87}$ | $-0.08$ | $74.37_{0.77}$ | $-0.21$ |
| Herding | $60.44_{0.00}$ | $+1.03$ | $56.19_{0.00}$ | $+0.17$ | $44.35_{0.00}$ | $+0.47$ | $75.36_{0.00}$ | $-0.45$ | $76.00_{0.00}$ | $+1.42$ |
| KCenter | $60.42_{0.00}$ | $+1.01$ | $56.25_{0.00}$ | $+0.22$ | $44.38_{0.00}$ | $+0.50$ | $74.48_{0.00}$ | $-1.33$ | $75.36_{0.00}$ | $+0.78$ |
| Graph-Cut | $60.82_{0.00}$ | $+1.41$ | $57.13_{0.00}$ | $+1.11$ | $44.67_{0.00}$ | $+0.79$ | $76.24_{0.00}$ | $+0.43$ | $75.28_{0.00}$ | $+0.70$ |
| CartographyEasy | $59.65_{0.50}$ | $+0.24$ | $56.06_{0.36}$ | $+0.03$ | $43.65_{0.61}$ | $-0.24$ | $76.04_{0.86}$ | $+0.23$ | $75.90_{0.76}$ | $+1.32$ |
| CartographyAmbiguous | $59.51_{0.31}$ | $+0.10$ | $56.26_{0.31}$ | $+0.24$ | $43.42_{0.30}$ | $-0.46$ | $75.65_{1.90}$ | $-0.16$ | $74.28_{1.52}$ | $-0.30$ |
| CartographyHard | $60.20_{0.47}$ | $+0.79$ | $56.46_{0.35}$ | $+0.43$ | $43.96_{0.57}$ | $+0.08$ | $76.80_{0.85}$ | $+0.99$ | $76.53_{0.67}$ | $+1.94$ |
| CartographyEasy+Ambig. | $60.51_{0.31}$ | $+1.10$ | $57.26_{0.31}$ | $+1.24$ | $44.42_{0.30}$ | $+0.54$ | $75.65_{1.90}$ | $-0.16$ | $74.28_{1.52}$ | $-0.30$ |
| ACSESSUniform | $61.09_{0.23}$ | $+1.68$ | $57.68_{0.24}$ | $+1.66$ | $46.82_{0.28}$ | $+2.94$ | $77.33_{0.33}$ | $+1.52$ | $77.24_{0.22}$ | $+2.65$ |
| ACSESSWeighted | $61.24_{0.30}$ | $+1.83$ | $57.82_{0.35}$ | $+1.79$ | $46.46_{0.30}$ | $+2.58$ | $77.66_{0.56}$ | $+1.86$ | $77.57_{0.33}$ | $+2.98$ |
| ACSESSWith Random | $60.72_{0.37}$ | $+1.31$ | $57.34_{0.46}$ | $+1.31$ | $45.80_{0.89}$ | $+1.92$ | $76.99_{1.68}$ | $+1.18$ | $76.63_{1.25}$ | $+2.04$ |

Figure: Figure 15: Benefit of the different selection strategies calculated as the difference to the classic few-shot selection strategy for the News Category dataset. The performance of the classic selection is represented as the zero value.
Refer to caption: extracted/5377787/figures/text/NewsCategory-diff.png

**Table 13: Benefit of the different selection strategies calculated as the difference in accuracy to the classic few-shot selection strategy for the News Category dataset. The subscript represents the standard deviation in the accuracy from running the selection strategy multiple times. The difference to the classic selection baseline (first row) is also included.**
| News Category | ProtoNet | MAML | Fine-Tuning | Mistral | Zephyr |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
|  | Acc. + Std | Diff. | Acc. + Std | Diff. | Acc. + Std | Diff. | Acc. + Std | Diff. | Acc. + Std | Diff. |
| Classic | $58.39_{0.25}$ | $+0.00$ | $55.00_{0.24}$ | $+0.00$ | $45.56_{0.30}$ | $+0.00$ | $70.64_{1.66}$ | $+0.00$ | $70.73_{1.56}$ | $+0.00$ |
| Random | $58.29_{0.53}$ | $-0.10$ | $55.00_{0.43}$ | $+0.00$ | $45.20_{0.35}$ | $-0.37$ | $71.15_{1.02}$ | $+0.51$ | $69.58_{0.52}$ | $-1.14$ |
| LENS | N/A | N/A | N/A | N/A | N/A | N/A | $72.10_{0.44}$ | $+1.46$ | $71.64_{0.48}$ | $+0.91$ |
| Similarity | $56.00_{0.74}$ | $-2.39$ | $52.07_{1.20}$ | $-2.92$ | $43.85_{0.89}$ | $-1.71$ | $69.74_{1.24}$ | $-0.90$ | $68.51_{1.09}$ | $-2.22$ |
| Diversity | $55.09_{0.28}$ | $-3.30$ | $52.43_{0.32}$ | $-2.56$ | $41.46_{0.42}$ | $-4.10$ | $69.18_{0.79}$ | $-1.46$ | $69.14_{0.75}$ | $-1.58$ |
| Entropy | $57.87_{0.34}$ | $-0.51$ | $54.77_{0.09}$ | $-0.22$ | $45.20_{0.30}$ | $-0.36$ | $71.45_{0.89}$ | $+0.81$ | $71.28_{0.64}$ | $+0.55$ |
| Margin | $58.46_{0.68}$ | $+0.08$ | $54.93_{0.66}$ | $-0.06$ | $45.69_{0.61}$ | $+0.12$ | $71.21_{0.15}$ | $+0.57$ | $70.81_{1.10}$ | $+0.08$ |
| Least Confidence | $58.42_{0.48}$ | $+0.03$ | $55.27_{0.59}$ | $+0.28$ | $45.36_{0.45}$ | $-0.20$ | $70.13_{0.19}$ | $-0.51$ | $68.61_{0.31}$ | $-2.12$ |
| Loss | $56.00_{0.61}$ | $-2.39$ | $52.14_{0.39}$ | $-2.85$ | $41.77_{0.30}$ | $-3.79$ | $70.35_{0.51}$ | $-0.29$ | $69.52_{0.23}$ | $-1.21$ |
| CAL | $57.97_{0.00}$ | $-0.42$ | $54.36_{0.00}$ | $-0.64$ | $44.91_{0.00}$ | $-0.66$ | $70.72_{0.00}$ | $+0.08$ | $68.80_{0.00}$ | $-1.93$ |
| CRAIG | $58.36_{0.04}$ | $-0.02$ | $54.87_{0.07}$ | $-0.12$ | $45.80_{0.01}$ | $+0.24$ | $70.45_{0.42}$ | $-0.19$ | $69.81_{0.19}$ | $-0.92$ |
| DeepFool | $57.97_{0.00}$ | $-0.42$ | $54.36_{0.00}$ | $-0.64$ | $44.91_{0.00}$ | $-0.66$ | $70.72_{0.00}$ | $+0.08$ | $68.80_{0.00}$ | $-1.93$ |
| Forgetting | $58.67_{0.00}$ | $+0.29$ | $54.91_{0.00}$ | $-0.09$ | $45.74_{0.00}$ | $+0.18$ | $72.48_{0.00}$ | $+1.84$ | $70.92_{0.00}$ | $+0.19$ |
| Glister | $58.13_{0.00}$ | $-0.25$ | $55.21_{0.00}$ | $+0.22$ | $45.65_{0.00}$ | $+0.09$ | $70.64_{0.00}$ | $+0.00$ | $69.84_{0.00}$ | $-0.89$ |
| GraNd | $58.67_{0.14}$ | $+0.28$ | $55.18_{0.08}$ | $+0.19$ | $45.45_{0.21}$ | $-0.11$ | $71.33_{0.72}$ | $+0.69$ | $68.32_{0.17}$ | $-2.41$ |
| Herding | $58.25_{0.00}$ | $-0.13$ | $54.97_{0.00}$ | $-0.02$ | $45.73_{0.00}$ | $+0.17$ | $71.04_{0.00}$ | $+0.40$ | $70.32_{0.00}$ | $-0.41$ |
| KCenter | $58.60_{0.00}$ | $+0.21$ | $55.25_{0.00}$ | $+0.26$ | $45.96_{0.00}$ | $+0.40$ | $70.88_{0.00}$ | $+0.24$ | $71.28_{0.00}$ | $+0.55$ |
| Graph-Cut | $58.52_{0.00}$ | $+0.14$ | $55.42_{0.00}$ | $+0.43$ | $45.68_{0.00}$ | $+0.12$ | $71.84_{0.00}$ | $+1.20$ | $70.16_{0.00}$ | $-0.57$ |
| CartographyEasy | $58.26_{0.43}$ | $-0.13$ | $54.90_{0.61}$ | $-0.09$ | $45.36_{0.27}$ | $-0.20$ | $70.55_{0.82}$ | $-0.09$ | $69.35_{0.84}$ | $-1.38$ |
| CartographyAmbiguous | $58.24_{0.29}$ | $-0.15$ | $54.82_{0.31}$ | $-0.18$ | $45.63_{0.30}$ | $+0.06$ | $70.16_{1.64}$ | $-0.48$ | $70.94_{1.39}$ | $+0.22$ |
| CartographyHard | $58.32_{0.36}$ | $-0.07$ | $54.89_{0.40}$ | $-0.11$ | $45.34_{0.54}$ | $-0.22$ | $72.20_{1.04}$ | $+1.56$ | $71.66_{0.70}$ | $+0.93$ |
| CartographyEasy+Ambig. | $58.84_{0.29}$ | $+0.45$ | $55.82_{0.31}$ | $+0.82$ | $45.83_{0.30}$ | $+0.26$ | $70.16_{1.64}$ | $-0.48$ | $70.94_{1.39}$ | $+0.22$ |
| ACSESSUniform | $59.06_{0.10}$ | $+0.67$ | $56.14_{0.10}$ | $+1.15$ | $46.67_{0.07}$ | $+1.11$ | $72.62_{0.39}$ | $+1.98$ | $71.98_{0.35}$ | $+1.25$ |
| ACSESSWeighted | $59.19_{0.17}$ | $+0.80$ | $56.24_{0.15}$ | $+1.25$ | $46.35_{0.28}$ | $+0.79$ | $72.97_{0.52}$ | $+2.33$ | $72.09_{0.48}$ | $+1.36$ |
| ACSESSWith Random | $58.95_{0.34}$ | $+0.56$ | $56.00_{0.34}$ | $+1.00$ | $45.99_{0.73}$ | $+0.42$ | $72.22_{0.87}$ | $+1.58$ | $71.55_{0.86}$ | $+0.82$ |

Figure: Figure 16: Benefit of the different selection strategies calculated as the difference to the classic few-shot selection strategy for the ATIS dataset. The performance of the classic selection is represented as the zero value.
Refer to caption: extracted/5377787/figures/text/atis-diff.png

**Table 14: Benefit of the different selection strategies calculated as the difference in accuracy to the classic few-shot selection strategy for the ATIS dataset. The subscript represents the standard deviation in the accuracy from running the selection strategy multiple times. The difference to the classic selection baseline (first row) is also included.**
| ATIS | ProtoNet | MAML | Fine-Tuning | Mistral | Zephyr |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
|  | Acc. + Std | Diff. | Acc. + Std | Diff. | Acc. + Std | Diff. | Acc. + Std | Diff. | Acc. + Std | Diff. |
| Classic | $89.90_{0.18}$ | $+0.00$ | $73.29_{0.26}$ | $+0.00$ | $73.09_{0.42}$ | $+0.00$ | $91.19_{1.04}$ | $+0.00$ | $86.09_{1.39}$ | $+0.00$ |
| Random | $89.85_{0.31}$ | $-0.05$ | $73.68_{0.44}$ | $+0.39$ | $72.93_{0.48}$ | $-0.16$ | $92.66_{0.58}$ | $+1.47$ | $86.58_{0.50}$ | $+0.50$ |
| LENS | N/A | N/A | N/A | N/A | N/A | N/A | $94.36_{0.45}$ | $+3.17$ | $87.84_{0.32}$ | $+1.75$ |
| Similarity | $89.08_{0.92}$ | $-0.82$ | $70.69_{1.83}$ | $-2.60$ | $72.52_{0.85}$ | $-0.57$ | $92.36_{1.07}$ | $+1.17$ | $84.12_{1.28}$ | $-1.97$ |
| Diversity | $89.50_{0.14}$ | $-0.40$ | $70.44_{0.29}$ | $-2.85$ | $71.65_{0.23}$ | $-1.44$ | $92.07_{0.37}$ | $+0.88$ | $86.98_{0.76}$ | $+0.89$ |
| Entropy | $89.70_{0.03}$ | $-0.20$ | $73.76_{0.41}$ | $+0.47$ | $73.21_{0.48}$ | $+0.12$ | $93.64_{0.77}$ | $+2.45$ | $86.48_{0.99}$ | $+0.39$ |
| Margin | $89.99_{0.21}$ | $+0.09$ | $73.67_{0.35}$ | $+0.38$ | $72.98_{0.58}$ | $-0.11$ | $93.43_{0.42}$ | $+2.24$ | $86.68_{0.75}$ | $+0.59$ |
| Least Confidence | $89.69_{0.30}$ | $-0.21$ | $73.32_{0.50}$ | $+0.04$ | $73.13_{0.48}$ | $+0.04$ | $93.17_{0.20}$ | $+1.98$ | $86.43_{0.44}$ | $+0.34$ |
| Loss | $89.54_{0.23}$ | $-0.36$ | $71.05_{0.66}$ | $-2.24$ | $70.61_{0.31}$ | $-2.48$ | $92.11_{0.27}$ | $+0.92$ | $85.63_{0.78}$ | $-0.46$ |
| CAL | $89.72_{0.00}$ | $-0.18$ | $73.58_{0.00}$ | $+0.29$ | $73.13_{0.00}$ | $+0.05$ | $93.68_{0.00}$ | $+2.49$ | $88.24_{0.00}$ | $+2.15$ |
| CRAIG | $89.94_{0.00}$ | $+0.04$ | $73.06_{0.00}$ | $-0.22$ | $72.74_{0.00}$ | $-0.34$ | $94.08_{0.00}$ | $+2.89$ | $87.36_{0.00}$ | $+1.27$ |
| DeepFool | $89.72_{0.00}$ | $-0.18$ | $73.58_{0.00}$ | $+0.29$ | $73.13_{0.00}$ | $+0.05$ | $93.68_{0.00}$ | $+2.49$ | $88.24_{0.00}$ | $+2.15$ |
| Forgetting | $90.45_{0.00}$ | $+0.55$ | $74.71_{0.00}$ | $+1.42$ | $73.90_{0.00}$ | $+0.81$ | $93.64_{0.00}$ | $+2.45$ | $87.04_{0.00}$ | $+0.95$ |
| Glister | $89.99_{0.00}$ | $+0.09$ | $73.03_{0.00}$ | $-0.26$ | $72.99_{0.00}$ | $-0.10$ | $93.60_{0.00}$ | $+2.41$ | $87.36_{0.00}$ | $+1.27$ |
| GraNd | $90.12_{0.57}$ | $+0.22$ | $73.26_{2.17}$ | $-0.03$ | $72.63_{1.33}$ | $-0.46$ | $91.97_{0.72}$ | $+0.78$ | $84.88_{1.57}$ | $-1.21$ |
| Herding | $89.78_{0.00}$ | $-0.12$ | $72.92_{0.00}$ | $-0.37$ | $73.12_{0.00}$ | $+0.03$ | $92.56_{0.00}$ | $+1.37$ | $86.08_{0.00}$ | $-0.01$ |
| KCenter | $89.77_{0.00}$ | $-0.13$ | $73.92_{0.00}$ | $+0.63$ | $73.46_{0.00}$ | $+0.38$ | $93.28_{0.00}$ | $+2.09$ | $86.56_{0.00}$ | $+0.47$ |
| Graph-Cut | $89.95_{0.00}$ | $+0.05$ | $73.49_{0.00}$ | $+0.20$ | $72.94_{0.00}$ | $-0.14$ | $93.52_{0.00}$ | $+2.33$ | $87.20_{0.00}$ | $+1.11$ |
| CartographyEasy | $89.76_{0.29}$ | $-0.14$ | $73.37_{0.67}$ | $+0.08$ | $72.98_{0.58}$ | $-0.11$ | $92.33_{0.59}$ | $+1.14$ | $86.60_{0.82}$ | $+0.51$ |
| CartographyAmbiguous | $89.66_{0.33}$ | $-0.24$ | $73.29_{0.40}$ | $+0.00$ | $72.95_{0.44}$ | $-0.13$ | $92.74_{1.20}$ | $+1.55$ | $87.38_{1.02}$ | $+1.30$ |
| CartographyHard | $89.64_{0.34}$ | $-0.26$ | $73.16_{0.69}$ | $-0.12$ | $72.96_{0.60}$ | $-0.12$ | $94.34_{0.74}$ | $+3.15$ | $87.54_{0.95}$ | $+1.45$ |
| CartographyEasy+Ambig. | $90.26_{0.33}$ | $+0.36$ | $74.36_{0.40}$ | $+1.07$ | $73.35_{0.44}$ | $+0.27$ | $92.74_{1.20}$ | $+1.55$ | $87.38_{1.02}$ | $+1.30$ |
| ACSESSUniform | $90.88_{0.25}$ | $+0.98$ | $75.29_{0.31}$ | $+2.00$ | $74.57_{0.22}$ | $+1.48$ | $95.84_{0.25}$ | $+4.65$ | $88.43_{0.22}$ | $+2.34$ |
| ACSESSWeighted | $90.92_{0.30}$ | $+1.02$ | $75.53_{0.39}$ | $+2.24$ | $74.04_{0.36}$ | $+0.95$ | $96.00_{0.44}$ | $+4.81$ | $88.70_{0.37}$ | $+2.61$ |
| ACSESSWith Random | $90.54_{0.33}$ | $+0.64$ | $74.97_{0.66}$ | $+1.68$ | $74.00_{0.69}$ | $+0.91$ | $94.98_{1.59}$ | $+3.79$ | $87.91_{1.13}$ | $+1.83$ |

Figure: Figure 17: Benefit of the different selection strategies calculated as the difference to the classic few-shot selection strategy for the Facebook dataset. The performance of the classic selection is represented as the zero value.
Refer to caption: extracted/5377787/figures/text/facebook-diff.png

**Table 15: Benefit of the different selection strategies calculated as the difference in accuracy to the classic few-shot selection strategy for the Facebook dataset. The subscript represents the standard deviation in the accuracy from running the selection strategy multiple times. The difference to the classic selection baseline (first row) is also included.**
| Facebook | ProtoNet | MAML | Fine-Tuning | Mistral | Zephyr |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
|  | Acc. + Std | Diff. | Acc. + Std | Diff. | Acc. + Std | Diff. | Acc. + Std | Diff. | Acc. + Std | Diff. |
| Classic | $86.74_{0.28}$ | $+0.00$ | $69.39_{0.48}$ | $+0.00$ | $68.57_{0.41}$ | $+0.00$ | $81.87_{2.14}$ | $+0.00$ | $78.17_{2.76}$ | $+0.00$ |
| Random | $87.48_{0.41}$ | $+0.74$ | $69.32_{1.23}$ | $-0.06$ | $68.48_{0.66}$ | $-0.09$ | $83.29_{0.94}$ | $+1.42$ | $78.29_{1.02}$ | $+0.12$ |
| LENS | N/A | N/A | N/A | N/A | N/A | N/A | $83.86_{0.37}$ | $+1.99$ | $79.58_{0.39}$ | $+1.41$ |
| Similarity | $85.57_{0.92}$ | $-1.16$ | $64.78_{2.56}$ | $-4.60$ | $66.94_{1.35}$ | $-1.62$ | $82.69_{1.24}$ | $+0.82$ | $78.81_{1.57}$ | $+0.64$ |
| Diversity | $84.51_{0.40}$ | $-2.23$ | $63.52_{0.54}$ | $-5.86$ | $60.19_{0.37}$ | $-8.38$ | $81.38_{0.67}$ | $-0.49$ | $77.82_{0.84}$ | $-0.35$ |
| Entropy | $86.78_{0.20}$ | $+0.05$ | $69.45_{0.23}$ | $+0.06$ | $68.60_{0.45}$ | $+0.03$ | $83.63_{0.54}$ | $+1.76$ | $79.28_{0.73}$ | $+1.11$ |
| Margin | $86.93_{0.47}$ | $+0.20$ | $69.47_{0.48}$ | $+0.08$ | $69.00_{0.32}$ | $+0.43$ | $83.44_{0.88}$ | $+1.57$ | $79.36_{0.54}$ | $+1.19$ |
| Least Confidence | $86.96_{0.46}$ | $+0.22$ | $69.60_{0.47}$ | $+0.21$ | $68.65_{0.49}$ | $+0.08$ | $82.85_{0.64}$ | $+0.98$ | $79.55_{1.56}$ | $+1.38$ |
| Loss | $84.76_{0.93}$ | $-1.97$ | $63.48_{1.06}$ | $-5.91$ | $62.10_{1.06}$ | $-6.46$ | $81.33_{1.21}$ | $-0.54$ | $76.51_{0.76}$ | $-1.66$ |
| CAL | $86.41_{0.11}$ | $-0.32$ | $69.16_{0.43}$ | $-0.23$ | $67.06_{0.25}$ | $-1.50$ | $82.93_{0.87}$ | $+1.06$ | $77.97_{0.04}$ | $-0.20$ |
| CRAIG | $87.06_{0.00}$ | $+0.32$ | $69.37_{0.00}$ | $-0.02$ | $68.61_{0.00}$ | $+0.04$ | $83.92_{0.00}$ | $+2.05$ | $76.72_{0.00}$ | $-1.45$ |
| DeepFool | $86.39_{0.00}$ | $-0.34$ | $68.27_{0.00}$ | $-1.12$ | $66.82_{0.00}$ | $-1.75$ | $83.04_{0.00}$ | $+1.17$ | $77.76_{0.00}$ | $-0.41$ |
| Forgetting | $87.42_{0.00}$ | $+0.68$ | $70.39_{0.00}$ | $+1.00$ | $69.30_{0.00}$ | $+0.73$ | $83.72_{0.00}$ | $+1.85$ | $79.52_{0.00}$ | $+1.35$ |
| Glister | $87.36_{0.00}$ | $+0.62$ | $70.00_{0.00}$ | $+0.62$ | $69.00_{0.00}$ | $+0.43$ | $82.96_{0.00}$ | $+1.09$ | $79.04_{0.00}$ | $+0.87$ |
| GraNd | $87.08_{0.00}$ | $+0.34$ | $69.40_{0.00}$ | $+0.02$ | $68.50_{0.00}$ | $-0.07$ | $82.40_{0.00}$ | $+0.53$ | $78.80_{0.00}$ | $+0.63$ |
| Herding | $86.13_{0.00}$ | $-0.60$ | $68.68_{0.00}$ | $-0.71$ | $68.67_{0.00}$ | $+0.10$ | $83.28_{0.00}$ | $+1.41$ | $78.80_{0.00}$ | $+0.63$ |
| KCenter | $85.78_{0.00}$ | $-0.95$ | $68.02_{0.00}$ | $-1.37$ | $68.48_{0.00}$ | $-0.08$ | $82.48_{0.00}$ | $+0.61$ | $78.96_{0.00}$ | $+0.79$ |
| Graph-Cut | $87.20_{0.00}$ | $+0.46$ | $69.99_{0.00}$ | $+0.60$ | $68.66_{0.00}$ | $+0.09$ | $82.08_{0.00}$ | $+0.21$ | $78.24_{0.00}$ | $+0.07$ |
| CartographyEasy | $86.83_{0.48}$ | $+0.10$ | $69.35_{1.21}$ | $-0.04$ | $68.78_{0.82}$ | $+0.21$ | $82.91_{0.82}$ | $+1.04$ | $78.22_{0.97}$ | $+0.06$ |
| CartographyAmbiguous | $86.62_{0.25}$ | $-0.11$ | $69.31_{0.42}$ | $-0.08$ | $68.47_{0.60}$ | $-0.09$ | $82.06_{1.84}$ | $+0.19$ | $77.66_{1.36}$ | $-0.51$ |
| CartographyHard | $86.90_{0.70}$ | $+0.16$ | $69.54_{0.81}$ | $+0.15$ | $68.74_{0.54}$ | $+0.17$ | $84.25_{0.54}$ | $+2.38$ | $79.59_{1.19}$ | $+1.42$ |
| CartographyEasy+Ambig. | $87.62_{0.25}$ | $+0.89$ | $70.31_{0.42}$ | $+0.92$ | $69.18_{0.60}$ | $+0.61$ | $82.06_{1.84}$ | $+0.19$ | $77.66_{1.36}$ | $-0.51$ |
| ACSESSUniform | $87.90_{0.10}$ | $+1.16$ | $70.83_{0.20}$ | $+1.44$ | $69.60_{0.38}$ | $+1.03$ | $84.42_{0.29}$ | $+2.55$ | $80.04_{0.33}$ | $+1.88$ |
| ACSESSWeighted | $88.01_{0.10}$ | $+1.27$ | $71.00_{0.31}$ | $+1.61$ | $69.50_{0.48}$ | $+0.93$ | $84.70_{0.36}$ | $+2.82$ | $80.13_{0.42}$ | $+1.96$ |
| ACSESSWith Random | $87.72_{0.37}$ | $+0.99$ | $70.56_{0.58}$ | $+1.17$ | $69.32_{0.83}$ | $+0.75$ | $84.00_{1.30}$ | $+2.13$ | $79.76_{1.00}$ | $+1.59$ |

Figure: Figure 18: Benefit of the different selection strategies calculated as the difference to the classic few-shot selection strategy for the HWU-64 dataset. The performance of the classic selection is represented as the zero value.
Refer to caption: extracted/5377787/figures/text/liu-diff.png

**Table 16: Benefit of the different selection strategies calculated as the difference in accuracy to the classic few-shot selection strategy for the HWU-64 dataset. The subscript represents the standard deviation in the accuracy from running the selection strategy multiple times. The difference to the classic selection baseline (first row) is also included.**
| HWU-64 | ProtoNet | MAML | Fine-Tuning | Mistral | Zephyr |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
|  | Acc. + Std | Diff. | Acc. + Std | Diff. | Acc. + Std | Diff. | Acc. + Std | Diff. | Acc. + Std | Diff. |
| Classic | $88.61_{0.31}$ | $+0.00$ | $65.90_{0.46}$ | $+0.00$ | $66.01_{0.78}$ | $+0.00$ | $93.53_{0.68}$ | $+0.00$ | $91.62_{1.21}$ | $+0.00$ |
| Random | $88.43_{0.26}$ | $-0.18$ | $65.64_{0.26}$ | $-0.26$ | $65.59_{0.30}$ | $-0.42$ | $92.43_{0.44}$ | $-1.10$ | $90.58_{0.64}$ | $-1.03$ |
| LENS | N/A | N/A | N/A | N/A | N/A | N/A | $94.66_{0.14}$ | $+1.13$ | $92.80_{0.30}$ | $+1.18$ |
| Similarity | $86.79_{0.43}$ | $-1.82$ | $63.99_{0.62}$ | $-1.91$ | $65.87_{0.45}$ | $-0.14$ | $91.36_{0.45}$ | $-2.17$ | $88.17_{0.54}$ | $-3.45$ |
| Diversity | $86.34_{0.20}$ | $-2.26$ | $63.71_{0.27}$ | $-2.19$ | $60.50_{0.19}$ | $-5.51$ | $92.09_{0.30}$ | $-1.44$ | $90.40_{0.46}$ | $-1.22$ |
| Entropy | $88.11_{0.08}$ | $-0.50$ | $65.36_{0.30}$ | $-0.54$ | $65.06_{0.44}$ | $-0.95$ | $92.84_{0.56}$ | $-0.69$ | $91.85_{0.63}$ | $+0.24$ |
| Margin | $88.72_{0.01}$ | $+0.11$ | $65.87_{0.54}$ | $-0.03$ | $65.78_{0.10}$ | $-0.22$ | $92.73_{0.10}$ | $-0.80$ | $91.32_{0.28}$ | $-0.30$ |
| Least Confidence | $88.34_{0.23}$ | $-0.26$ | $65.35_{0.41}$ | $-0.55$ | $65.39_{0.06}$ | $-0.62$ | $92.45_{0.27}$ | $-1.08$ | $90.83_{0.10}$ | $-0.79$ |
| Loss | $86.71_{0.41}$ | $-1.90$ | $62.35_{0.05}$ | $-3.55$ | $59.98_{0.14}$ | $-6.03$ | $92.03_{0.46}$ | $-1.50$ | $90.27_{0.42}$ | $-1.35$ |
| CAL | $88.75_{0.00}$ | $+0.15$ | $64.84_{0.00}$ | $-1.06$ | $65.74_{0.00}$ | $-0.27$ | $93.12_{0.00}$ | $-0.41$ | $90.00_{0.00}$ | $-1.62$ |
| CRAIG | $88.44_{0.07}$ | $-0.16$ | $64.69_{0.04}$ | $-1.21$ | $65.18_{0.03}$ | $-0.83$ | $92.53_{0.04}$ | $-1.00$ | $90.21_{0.19}$ | $-1.40$ |
| DeepFool | $88.74_{0.00}$ | $+0.14$ | $64.89_{0.00}$ | $-1.01$ | $65.57_{0.00}$ | $-0.44$ | $93.04_{0.00}$ | $-0.49$ | $90.24_{0.00}$ | $-1.38$ |
| Forgetting | $88.82_{0.00}$ | $+0.21$ | $65.93_{0.00}$ | $+0.03$ | $65.80_{0.00}$ | $-0.21$ | $92.98_{0.00}$ | $-0.55$ | $92.00_{0.00}$ | $+0.38$ |
| Glister | $88.39_{0.00}$ | $-0.22$ | $65.26_{0.00}$ | $-0.64$ | $65.28_{0.00}$ | $-0.73$ | $92.88_{0.00}$ | $-0.65$ | $90.40_{0.00}$ | $-1.22$ |
| GraNd | $88.30_{0.18}$ | $-0.31$ | $65.39_{0.12}$ | $-0.52$ | $64.81_{0.20}$ | $-1.20$ | $92.59_{0.39}$ | $-0.94$ | $89.87_{0.25}$ | $-1.75$ |
| Herding | $88.40_{0.00}$ | $-0.21$ | $64.54_{0.00}$ | $-1.36$ | $65.03_{0.00}$ | $-0.98$ | $92.56_{0.00}$ | $-0.97$ | $90.56_{0.00}$ | $-1.06$ |
| KCenter | $88.55_{0.00}$ | $-0.05$ | $64.46_{0.00}$ | $-1.44$ | $65.07_{0.00}$ | $-0.94$ | $92.00_{0.00}$ | $-1.53$ | $90.00_{0.00}$ | $-1.62$ |
| Graph-Cut | $88.57_{0.00}$ | $-0.04$ | $65.65_{0.00}$ | $-0.25$ | $65.99_{0.00}$ | $-0.02$ | $92.64_{0.00}$ | $-0.89$ | $89.92_{0.00}$ | $-1.70$ |
| CartographyEasy | $88.34_{0.26}$ | $-0.27$ | $65.63_{0.37}$ | $-0.27$ | $65.22_{0.44}$ | $-0.80$ | $92.46_{0.38}$ | $-1.07$ | $90.56_{0.42}$ | $-1.06$ |
| CartographyAmbiguous | $88.65_{0.23}$ | $+0.04$ | $65.81_{0.45}$ | $-0.09$ | $66.21_{0.55}$ | $+0.20$ | $93.63_{1.00}$ | $+0.10$ | $90.99_{0.94}$ | $-0.62$ |
| CartographyHard | $88.25_{0.32}$ | $-0.35$ | $65.60_{0.39}$ | $-0.30$ | $65.30_{0.52}$ | $-0.71$ | $93.85_{0.49}$ | $+0.32$ | $92.40_{0.43}$ | $+0.78$ |
| CartographyEasy+Ambig. | $89.05_{0.23}$ | $+0.44$ | $65.91_{0.45}$ | $+0.01$ | $66.41_{0.55}$ | $+0.40$ | $93.63_{1.00}$ | $+0.10$ | $90.99_{0.94}$ | $-0.62$ |
| ACSESSUniform | $89.13_{0.10}$ | $+0.53$ | $66.47_{0.14}$ | $+0.57$ | $66.96_{0.26}$ | $+0.95$ | $95.00_{0.07}$ | $+1.48$ | $93.08_{0.26}$ | $+1.46$ |
| ACSESSWeighted | $89.25_{0.13}$ | $+0.64$ | $66.62_{0.19}$ | $+0.72$ | $66.73_{0.37}$ | $+0.72$ | $95.16_{0.16}$ | $+1.63$ | $93.40_{0.32}$ | $+1.79$ |
| ACSESSWith Random | $89.09_{0.26}$ | $+0.48$ | $66.01_{0.31}$ | $+0.11$ | $66.52_{0.73}$ | $+0.51$ | $94.67_{0.85}$ | $+1.14$ | $92.55_{0.88}$ | $+0.93$ |

Figure: Figure 19: Benefit of the different selection strategies calculated as the difference to the classic few-shot selection strategy for the SNIPS dataset. The performance of the classic selection is represented as the zero value.
Refer to caption: extracted/5377787/figures/text/snips-diff.png

**Table 17: Benefit of the different selection strategies calculated as the difference in accuracy to the classic few-shot selection strategy for the SNIPS dataset. The subscript represents the standard deviation in the accuracy from running the selection strategy multiple times. The difference to the classic selection baseline (first row) is also included.**
| SNIPS | ProtoNet | MAML | Fine-Tuning | Mistral | Zephyr |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
|  | Acc. + Std | Diff. | Acc. + Std | Diff. | Acc. + Std | Diff. | Acc. + Std | Diff. | Acc. + Std | Diff. |
| Classic | $96.98_{0.09}$ | $+0.00$ | $84.10_{0.24}$ | $+0.00$ | $89.86_{0.26}$ | $+0.00$ | $92.02_{1.23}$ | $+0.00$ | $85.65_{1.16}$ | $+0.00$ |
| Random | $97.07_{0.11}$ | $+0.09$ | $84.26_{1.50}$ | $+0.16$ | $89.58_{0.46}$ | $-0.29$ | $92.25_{0.52}$ | $+0.23$ | $86.18_{1.39}$ | $+0.53$ |
| LENS | N/A | N/A | N/A | N/A | N/A | N/A | $93.16_{0.41}$ | $+1.14$ | $87.06_{0.66}$ | $+1.41$ |
| Similarity | $96.27_{0.61}$ | $-0.70$ | $81.40_{2.64}$ | $-2.70$ | $89.12_{1.15}$ | $-0.75$ | $91.63_{1.38}$ | $-0.38$ | $84.19_{2.06}$ | $-1.46$ |
| Diversity | $96.26_{0.17}$ | $-0.72$ | $81.64_{0.57}$ | $-2.46$ | $87.90_{0.16}$ | $-1.96$ | $90.01_{0.85}$ | $-2.01$ | $85.53_{0.78}$ | $-0.12$ |
| Entropy | $96.67_{0.03}$ | $-0.30$ | $84.63_{0.46}$ | $+0.52$ | $89.21_{0.87}$ | $-0.65$ | $92.62_{0.62}$ | $+0.60$ | $86.24_{0.73}$ | $+0.59$ |
| Margin | $96.93_{0.19}$ | $-0.04$ | $84.40_{0.41}$ | $+0.29$ | $89.88_{0.45}$ | $+0.01$ | $92.83_{0.83}$ | $+0.82$ | $86.19_{0.21}$ | $+0.54$ |
| Least Confidence | $96.83_{0.07}$ | $-0.14$ | $84.35_{0.40}$ | $+0.25$ | $88.85_{0.90}$ | $-1.01$ | $92.43_{0.58}$ | $+0.41$ | $86.11_{0.36}$ | $+0.46$ |
| Loss | $96.83_{0.27}$ | $-0.15$ | $82.57_{1.03}$ | $-1.53$ | $87.40_{0.29}$ | $-2.46$ | $90.27_{0.89}$ | $-1.75$ | $85.73_{1.16}$ | $+0.08$ |
| CAL | $96.82_{0.00}$ | $-0.16$ | $82.22_{0.00}$ | $-1.88$ | $89.35_{0.00}$ | $-0.52$ | $90.40_{0.00}$ | $-1.62$ | $86.64_{0.00}$ | $+0.99$ |
| CRAIG | $97.05_{0.00}$ | $+0.08$ | $83.63_{0.00}$ | $-0.47$ | $89.58_{0.00}$ | $-0.28$ | $91.92_{0.00}$ | $-0.10$ | $85.20_{0.00}$ | $-0.45$ |
| DeepFool | $96.82_{0.00}$ | $-0.16$ | $82.22_{0.00}$ | $-1.88$ | $89.35_{0.00}$ | $-0.52$ | $90.40_{0.00}$ | $-1.62$ | $86.64_{0.00}$ | $+0.99$ |
| Forgetting | $96.93_{0.00}$ | $-0.05$ | $84.34_{0.00}$ | $+0.23$ | $89.91_{0.00}$ | $+0.04$ | $93.24_{0.00}$ | $+1.22$ | $86.28_{0.00}$ | $+0.63$ |
| Glister | $96.98_{0.00}$ | $+0.01$ | $83.83_{0.00}$ | $-0.27$ | $89.61_{0.00}$ | $-0.25$ | $92.88_{0.00}$ | $+0.86$ | $86.16_{0.00}$ | $+0.51$ |
| GraNd | $97.08_{0.01}$ | $+0.10$ | $83.59_{0.02}$ | $-0.52$ | $88.63_{0.01}$ | $-1.24$ | $92.75_{0.04}$ | $+0.73$ | $87.36_{0.34}$ | $+1.71$ |
| Herding | $97.17_{0.00}$ | $+0.20$ | $84.93_{0.00}$ | $+0.83$ | $89.84_{0.00}$ | $-0.02$ | $91.68_{0.00}$ | $-0.34$ | $85.76_{0.00}$ | $+0.11$ |
| KCenter | $97.14_{0.00}$ | $+0.17$ | $84.88_{0.00}$ | $+0.78$ | $89.82_{0.00}$ | $-0.04$ | $92.72_{0.00}$ | $+0.70$ | $86.56_{0.00}$ | $+0.91$ |
| Graph-Cut | $97.01_{0.00}$ | $+0.04$ | $84.12_{0.00}$ | $+0.02$ | $89.65_{0.00}$ | $-0.22$ | $91.28_{0.00}$ | $-0.74$ | $85.04_{0.00}$ | $-0.61$ |
| CartographyEasy | $96.94_{0.21}$ | $-0.04$ | $84.54_{0.95}$ | $+0.43$ | $89.86_{0.54}$ | $-0.00$ | $91.74_{1.56}$ | $-0.27$ | $86.54_{1.21}$ | $+0.90$ |
| CartographyAmbiguous | $96.96_{0.10}$ | $-0.01$ | $84.13_{0.25}$ | $+0.03$ | $89.91_{0.20}$ | $+0.04$ | $92.04_{1.02}$ | $+0.02$ | $85.67_{1.51}$ | $+0.02$ |
| CartographyHard | $97.04_{0.18}$ | $+0.06$ | $84.56_{0.64}$ | $+0.46$ | $89.84_{0.55}$ | $-0.02$ | $93.19_{1.01}$ | $+1.18$ | $86.70_{1.29}$ | $+1.05$ |
| CartographyEasy+Ambig. | $97.26_{0.10}$ | $+0.29$ | $84.73_{0.25}$ | $+0.63$ | $90.01_{0.20}$ | $+0.14$ | $92.04_{1.02}$ | $+0.02$ | $85.67_{1.51}$ | $+0.02$ |
| ACSESSUniform | $97.50_{0.02}$ | $+0.52$ | $85.31_{0.22}$ | $+1.20$ | $90.54_{0.08}$ | $+0.67$ | $93.62_{0.36}$ | $+1.60$ | $87.62_{0.58}$ | $+1.98$ |
| ACSESSWeighted | $97.51_{0.09}$ | $+0.53$ | $85.44_{0.27}$ | $+1.33$ | $90.34_{0.13}$ | $+0.47$ | $93.86_{0.42}$ | $+1.85$ | $87.87_{0.68}$ | $+2.22$ |
| ACSESSWith Random | $97.30_{0.27}$ | $+0.33$ | $85.01_{0.59}$ | $+0.90$ | $90.15_{0.39}$ | $+0.29$ | $92.89_{1.02}$ | $+0.88$ | $86.94_{2.25}$ | $+1.30$ |

## Peer Reviews

confidence: 3
contribution: 2
initial_rating: 5
presentation: 2
questions: Please refer to weakness.
soundness: 3
strengths: 1. Performance Improvement through Strategic Selection: The proposed ACSESS method effectively combines various sample selection strategies, leading to consistently better performance in few-shot learning tasks. This combination enhances the overall effectiveness of sample selection, particularly in imbalanced datasets with noisy samples.
2. Emphasis on Learnability: The study reveals that the learnability of samples is more critical than their informativeness or representativeness for the success of few-shot learning. 
3. Cost Reduction and Efficiency: Selecting a subset of samples rather than using full-shot training reduces computational costs while improving performance, especially on imbalanced and noisy datasets. The findings indicate that even in scenarios with limited available samples per class, curated selections can still yield benefits in performance and efficiency.
summary: This paper explores 20 sample selection strategies in few-shot learning, introducing the Automatic Combination of Sample Selection Strategies (ACSESS) method. ACSESS consistently outperforms individual strategies across five approaches using 8 image and 6 text datasets. The findings highlight the importance of modality and dataset characteristics, demonstrating that effective selection enhances performance, particularly in imbalanced and noisy datasets. Notably, the strategies are more beneficial with fewer shots, often reverting to random selection with larger numbers of shots.
weaknesses: 1. Although this paper conducts thorough experiments, it appears to lack significant insights into sample selection strategies and seems to be merely a combination of existing approaches.
2. Many key points in the paper are not clearly articulated, such as how the proposed ACSESS utilizes weighted strategies and active learning, which require further clarification.
3. Table 1 categorizes the comparison strategies into three major groups. However, the classification of strategies presented in Table 2 appears inconsistent with that in Table 1, which may lead to confusion. Furthermore, additional updated comparison strategies are needed to demonstrate the effectiveness of the proposed method.

---

confidence: 4
contribution: 2
initial_rating: 5
presentation: 3
questions: 1. How does the proposed forward and backward selection in ACSESS differ fundamentally from existing selection mechanisms, and what distinct advantages does it bring?
2. Could the paper provide a clearer step-by-step explanation of how different strategies are combined within ACSESS?
3. What specific methods or frameworks were considered to determine the optimal combination of strategies and datasets, and how was this integration logically structured?
4. What theoretical considerations underpin the strategy selection in ACSESS, and how might the absence of theoretical support affect the method’s generalizability across diverse datasets and tasks?
soundness: 2
strengths: 1.	This paper presents an extensive comparison of 20 sample selection strategies, highlighting each strategy’s strengths and limitations across different few-shot learning contexts. This analysis supports the validation of ACSESS’s effectiveness over traditional single-property strategies.
2.	The approach incorporates a diverse set of datasets, spanning both image and text domains, and evaluates multiple few-shot learning methods, including meta-learning and in-context learning.
summary: This paper introduces ACSESS, an Automatic Combination of Sample Selection Strategies aimed at enhancing few-shot learning by combining diverse sample selection strategies. The paper evaluates 20 sample selection strategies across multiple few-shot learning frameworks, including meta-learning and in-context learning, using datasets from both image and text domains. ACSESS leverages several selection properties (informativeness, representativeness, and learnability) and proposes a scoring mechanism that improves the quality of selected samples by assigning weights to different strategies. This approach demonstrates consistent performance gains, especially in settings with imbalanced or noisy datasets.
weaknesses: 1. **Unclear differentiation from existing mechanisms**: The proposed forward and backward selection mechanisms lack a clear distinction from existing methods. The paper does not sufficiently emphasize how these mechanisms differ from conventional techniques or why they are particularly well-suited to the ACSESS framework. The paper could benefit from providing clear formulas or pseudocode to highlight the differences from recent related work (e.g., L149, L151), which would help readers better understand the distinction.

2. **Lack of detailed methodological steps**: Although the approach resembles an ensemble method that combines multiple strategies, the methodology section does not clearly outline the specific steps required to implement this combination. This lack of detail may challenge readers attempting to reproduce or apply the method. For example, providing clear algorithmic steps or expressing them through formulas would help avoid ambiguities and facilitate verification of the method on recent related works. Additionally, if relevant experiments are available, presenting them would enhance the manuscript’s credibility.

3. **Limited explanation of integration across strategies and datasets**: While the paper examines the impact of combining various strategies across datasets, it does not provide a structured explanation of how these combinations are optimized. A clearer flowchart for integrating diverse strategies with datasets would enhance the presentation. The manuscript could provide a specific example to illustrate the clear operational flowchart and pseudocode, further clarifying the execution process of ACSESS.

4. **Insufficient theoretical support for strategy selection**: Although extensive experiments empirically validate the method, the paper lacks a theoretical basis for favoring certain strategies. Greater theoretical justification could strengthen the feasibility and applicability of ACSESS, as relying solely on experimental results may limit the method’s perceived robustness.

---

confidence: 5
contribution: 3
initial_rating: 6
presentation: 3
questions: see the weaknesses
soundness: 3
strengths: 1. The article systematically evaluates the performance of single-property sample selection strategies and introduces a novel sample selection strategy—ACSESS. This strategy aims to combine multiple single-property strategies, optimizing the selected samples through a balanced weighting scheme to overcome the limitations of single-attribute selection, significantly enhancing the performance of few-shot learning under extreme sample conditions. 
2. The paper provides extensive experimental data, covering nearly all existing sample selection strategies, and evaluates their effectiveness in few-shot learning tasks across text classification, image classification, and context-based text classification. 
3. The ACSESS method innovatively proposes a scheme for autonomously combining strategy sets, building on previous research and addressing prior strategy limitations, with potential to advance future few-shot learning studies.
4. The article’s structure is clear, with numerous bolded sections to enhance readability and summary for researchers. However, the frequent use of bolding may sometimes detract from the clarity of key points, and it would be beneficial to use bolding more selectively.
summary: The article addresses the uncertainty of the impact of sample selection on few-shot learning by:
(1) Broadly investigating the effects of single-property sample selection strategies used in previous work across three approaches: image classification and text classification in few-shot learning, and context-based text classification on extensive datasets.
(2) Proposing Automatic Combination of Sample Selection Strategies (ACSESS) method, which combines multiple complementary attributes in sample selection. The method employs the intersection of Forward Selection, Backward Selection, and Datamodels Selection schemes, assigning scores to samples through a weighted approach. Experimental results show that ACSESS has a sustained impact on performance, indicating that learnability is the highest-priority metric in few-shot learning.
(3) Further exploring the effects of sample quantity, sample quality (including noisy samples), dataset size, and sample selection strategies.
Overall, this paper provides a comprehensive analysis of the influence of sample selection strategies on few-shot learning and creatively introduces the ACSESS method, a strategic combination to overcome the limitations of single-attribute selection. The paper is supported by extensive experimental data and conclusive findings, making it a thorough and innovative contribution to sample selection strategies.
weaknesses: 1.Considering that the paper finalizes the relevant selection strategies by taking the intersection of three selection methods (Forward Selection, Backward Selection, Datamodels Selection), does this combined approach demonstrate a performance advantage over using single-strategy methods?
2.The paper evaluates three independent weighting schemes. Are there other feasible weighting schemes beyond these three? Alternatively, could an adaptive mechanism be introduced to adjust these weights based on the characteristics of the dataset? Further explanation on this aspect would aid in understanding the flexibility and effectiveness of the ACSESS approach.
3.While ACSESS demonstrates excellent performance in text and image classification tasks, could this method also be applied to other few-shot learning scenarios, such as more challenging tasks like object detection, semantic segmentation, or depth estimation?

---

confidence: 4
contribution: 4
initial_rating: 5
presentation: 2
questions: The studied topic is useful and meaningful. But the details can hardly be elaborated in limited pages. Illustrative examples are missing which impedes understanding. I think this work would be a great fit for journals like TPAMI after meticulous reorganization.

For questions, plz refer to the weaknesses.
soundness: 3
strengths: The work is well-motivated, the literature is well reviewed, and a reasonable and promising method for combing different sample selection strategies for learning is proposed.

The idea of learning a regression model to derive weights for each strategy, by learning upon a small number of random combinations of strategies, is interesting.
summary: Samples are important in few-shot learning (FSL), meta-learning and in-context learning (ICL), especially in noisy environment. Sample selection picks informative, easily-learned or representative examples, probably improve performance of FSL or meta-learning or ICL when compared to typical supervised settings. However, the impact of sample selection is not well-studied in few-shot scenario. 

This work managed to justify the impact of different sample selection strategies. It showcases that the effectiveness of each sample selection strategies depends on data modality, dataset characteristics and models. To facilitate sample selection for FSL, in this work authors propose a method to identify a subset of well-performing strategies and combine those chosen ones for learning. 

Three types of  sample selection strategy identification are investigated, including forward selection, backward selection and "datamodels" selection. The obtained three subsets of sample selection strategies are intersected to get the final set of strategies. Then, a weighting scheme is utilized to assign different weight to each strategy for sample selection. 

With the proposed sample selection method by automatically combining different sample selection strategies, this work witnesses promising results on different tasks.
weaknesses: My concerns are as follows:
1) **Novelty**: The work uses off-the-shelf sample selection strategies, proposing a strategy to leverage these sample selection strategies for model learning. The integration of existing strategies into a unified pipeline easily distracts my attention away from its main contribution (Note I know it is the automatic combination of sample selection strategies).
2) **Clarity**: Though I know the overall framework of the proposed ACSESS, it remains ambiguous for me how sample selection works within FSL, for instance in the classic 5w-5s few-shot image classification. And, it is unclear how to efficiently measure the contribution of each strategy, especially when several strategies are combined. How exactly does the sample selection operate in the meta-learning procedure? How do we generalize or use ACSESS in a new task from a new dataset? 
3) **Efficiency**: As said, the optimal combination of sample selection strategies depends on data modality, dataset characteristic (eg. size, #categories) and model. It means we have no idea of which sample selection is better for a new dataset, model, nor do we know which combination of sample selection strategies boost the performance most unless searching for the optimal one. This is computational inefficient.  How can we intuitively and quickly identify a subset of sample selection strategies when encountering a new dataset or model?

---

confidence: 4
contribution: 3
initial_rating: 6
presentation: 3
questions: Please see the weaknesses.
soundness: 3
strengths: The conclusions obtained through a large number of experimental validations provide a valuable reference for research teams with limited resources, which can effectively reduce the cost of trial and error and accelerate the research process.
summary: This thesis focuses on proposing a method that identifies a subset of relevant strategies that can improve the overall success rate of few-shot learning. The identified strategies are then weighted according to their expected contribution, and the identified strategies are combined to identify the most informative, highest quality samples that provide the greatest benefit.
weaknesses: 1. What model is used for the backbone of this article (e.g., whether it is a CNN or a Transformer), and the characteristics of the different architectures can have an impact on the model performance. Most classical methods are usually proposed without pre-training, but if pre-trained models are used, it is necessary to verify whether the differences between the pre-trained and target tasks affect the applicability of the conclusions of this paper. It is recommended that the authors perform specific backbone architecture comparisons, such as testing on ResNet and Vision Transformer, and perform ablation experiments, using pre-trained and randomly initialized models, to directly assess the impact of pre-training on the findings.

2. In few-shot learning tasks, the domain gap exists in data distribution between an upstream task (pre-training task) and a downstream task (fine-tuning or application task). This may result in the knowledge learned by the model in the upstream task not being effectively migrated to the downstream task. In this case, methods need to be evaluated on cross-domain undersampled learning benchmark datasets such as DomainNet or Office-Home. Testing on these datasets enables a more specific assessment of the impact of domain gaps on the performance of its sample selection strategy and a clearer understanding of the applicability of its strategy to cross-domain few-shot learning.

Meta-review: The paper empirically explores the question of the impact of sample selection on the performance of few-shot learning and presents a scheme: ACSESS, that can automatically combine strategies for selecting data samples towards improved performance. The key idea is to identify a subset of strategies that are scored well and which are combined based on their expected performance improvement. The strategies are built over the informativeness, representativeness, and learnability properties of the samples. Experiments are presented on a number of image and text datasets and various conclusions are drawn. A key result is that ACSESS leads to a consistent 1-2% improvement in performance across the datasets.

Decision: Reject