SlowGuess/NeurIPS_2023_OCR huggingface dataset:
1: Given: training set  $S = \{(x_i, y_i)\}_i^N$ , pretrained LLM  $\mathcal{M}$ , transformation template  $\Gamma(\cdot)$ , and context-free input  $\eta$
2: Initial prompt  $\rho$
3: for  $(x_{i},y_{i})$  in  $S$  do
4: Inference  $\hat{P} \gets \{\hat{p}(y|\Gamma(x_i, y_i)) \oplus \eta\}|y \in \mathcal{Y}\}$  via  $\mathcal{M}$
5: Calculate the fair  $(\Gamma (x_i,y_i))$  according to Eq. 2
6: end for
7: Sort  $\text{fair}_{i=1,\dots,N}(\Gamma(x_i, y_i))$  in descending order
8: for  $d$  in  $1, \dots, k$  do
9: Insert the most  $d$  fair demonstration at the head of  $\rho$
10: end for
11: return  $\rho$
Algorithm 2 G-fair-Prompting
1: Given: training set  $S = \{(x_{i},y_{i})\}_{i}^{N}$ , pretrained LLM  $\mathcal{M}$ , transformation template  $\Gamma (\cdot)$ , and context-free input  $\eta$
2: Initial prompt  $\rho$
3: while  $S$  is not null do
4: for  $(x_{i},y_{i})$  in  $S$  do
5:  $\rho_{\mathrm{tmp}}\gets \Gamma (x_i,y_i)\oplus \rho$
6: Inference  $\hat{P} \gets \{\hat{p}(y|\rho_{\mathrm{tmp}} \oplus \eta)|y \in \mathcal{Y}\}$  via  $\mathcal{M}$
7: Calculate the fair  $(\rho_{\mathrm{tmp}})$  according to Eq. 2
8: end for
9: Insert the demonstration that can improve fairness best and remove it from  $S$
10: Stop searching when fairness can't be improved
11: end while
12: return  $\rho$
Models. There are a large number of available LLMs (Appendix A.2) including open-source models and black-box cloud API. Recently, Meta has released their powerful pretrained LLMs, LLaMA. LLaMA models with 13B parameters can achieve comparable performance in contrast to BLOOM and GPT-3 with much larger model size. In this paper, we evaluate the effectiveness of our method on BLOOM (176B) and LLaMA models of different sizes. We have opted to employ LLaMA (65B) as a substitute for GPT-3 in our experiments, since oepnai strictly restricts the API access to certain areas.
Datasets. We conducted experiments on various text classification datasets [21], namely SST-2, AGNews, CoLA, TREC, and RTE. Furthermore, the maximum input length of LLaMA is 512, and the sentences in RTE are too long for LLaMA. The task descriptions and statistics are available in Table 6 in Appendix.
We conducted experiments on different settings and reported the results of five runs. We compared our method with the diversity-guided searching strategy proposed by Zhang et al.[12] (Global view) and the similarity-guided searching strategy proposed by Liu et al.[15] (Local view). Note that methods
Table 1: Accuracy for different prompting strategies (averaged on  $5_{(0,\dots,4)}$  different seeds, where Top-  $k$  and Greedy indicate T-fair-Prompting with  $k$  demonstrations and G-fair-Prompting respectively).
Model	Dataset	Random	Diversity	Similarity	Top-2	Ours Top-4	Greedy
BLOOM (176B)	SST2	92.72.3	95.00.9	94.00.9	94.60.5	93.82.1	91.24.0
AGNews	73.95.9	70.210.1	74.83.8	75.42.2	74.82.3	79.61.4
TREC	47.914.6	46.08.7	31.43.1	55.413.3	39.219.3	66.82.5
RTE	62.44.2	69.21.9	67.23.5	55.61.0	57.61.9	63.02.1
CoLA	68.44.8	71.03.7	69.82.5	66.48.6	66.83.7	68.26.2
LLaMA (33B)	SST2	82.511.8	90.02.7	72.84.4	82.011.1	80.012.2	85.68.2
AGNews	75.25.0	75.05.1	75.02.4	73.23.9	69.84.4	76.44.6
TREC	68.111.1	68.24.7	60.63.4	71.411.1	57.817.3	80.25.3
CoLA	66.911.0	68.86.8	72.82.0	63.813.3	69.83.9	70.64.2
LLaMA (65B)	SST2	90.07.7	90.89.0	87.43.1	88.28.6	95.81.5	87.89.0
AGNews	76.85.0	78.23.1	78.21.8	77.03.4	76.24.9	76.04.0
TREC	63.614.2	65.210.9	64.05.5	65.813.0	57.419.9	74.012.2
CoLA	66.29.8	62.68.6	59.214.0	67.611.7	62.66.5	72.04.5
based on local view are time-consuming since they require searching different demonstrations for every test example. Table 1 shows the performance of the different strategies, where "Random" indicates the average accuracy for enumerating all situations, "Diversity" and "Similarity" indicate demonstrations are selected according to diversity and similarity (details please refer to Appendix A.6), respectively. For each dataset, we set the size of the training set to 4. "Diversity" and "Similarity" select 4 from 16 demonstrations, as they need more candidates. The baseline is expensive to compute since enumerating all candidates for 4 demonstrations in RTE on BLOOM will take more than 120 NVIDIA A100 GPU hours. We enumerate all candidates for the training set with 4 demonstrations on different models, as shown in Fig. 2. The results on models whose parameters less than 13B are shown in Table 4 (i.e., GPT2-XL (1.5B), LLaMA (7B), and LLaMA (13B)).
- G-fair-Prompting can reach a close approximation of enumeration. To evaluate whether the G-fair-Prompting (Greedy) method can approximate the best performance of enumerating all candidates, we marked the performance of G-fair-Prompting with a green star (representing the closest value to averaged accuracy of G-fair-Prompting on the line). We found that G-fair-Prompting can achieve a very close approximation to enumeration. As shown in Fig. 2, most prompts searched by G-fair-Prompting achieved a top  $20\%$  ranking, and on BLOOM (176B), G-fair-Prompting almost found the most fair prompt.
- G-fair-Prompting outperforms T-fair-Prompting. As shown in Table 1, although T-fair-Prompting achieves better performance compared with random selection, G-fair-Prompting consistently outperforms T-fair-Prompting. Furthermore, Top-2 significantly outperforms Top-4 in most cases (over  $5\%$ ), indicating that the number of demonstrations selected is crucial. Overall, the results demonstrate that G-fair-Prompting achieves satisfactory performance with only a slight additional cost.
- Compared with SOTA methods. We compared our methods with several State-of-the-Art (SOTA) methods, including diversity-guided and similarity-guided techniques. We observed that our greedy approach outperforms most of these SOTA methods in most situations, and the improvements of over  $10\%$  are observed on dataset TREC. The similarity-guided method, on the other hand, achieved the best performance on the topic classification task (AGNews).
Figure 4: BLOOM is not sensitive to CoLA.
This is because it searches for a unique prompt for every different test example based on the dis
tance between the embeddings of the training samples and the test example. This strategy selects demonstrations with labels that are the same as the test samples, and Language Models (LLMs) tend to predict biased predictions toward the labels that always appear in the context. However, the similarity-guided method may prove inadequate when applied to other tasks. Specifically, the similarity-guided strategy exhibits lower performance compared to random selection in QC and acceptability tasks. Furthermore, the G-fair-Prompting approach may occasionally falter when the model's sensitivity to the task is not immediately evident, as observed in the acceptability task on BLOOM (depicted in Fig. 4). Note that the training set size of compared methods is  $4 \times$  larger than ours.
- Comparison with Calibration Method. Post-calibration [18], can enhance the accuracy of a given prompt in most cases. However, when the selected prompt is of poor quality, the performance may remain inadequate even after calibration. We compared the performance of G-fair-Prompting with random selection with calibration (averaged on all candidates), and found that G-fair-Prompting can outperform random selection with calibration in most situations. For example, on the topic classification task, G-fair-Prompting achieves the best performance on most models. Moreover, we find that post calibration can harm the performance of the model and it occurs significantly times, so it is worthwhile to reconsider the influence of manipulating the model's probability directly.
Table 2: Accuracy comparison after post calibration.
Dataset	Method	BLOOM (176B)	LLaMA (33B)	LLaMA (65B)
Average	Worst	Average	Worst	Average	Worst
TREC	Random (cal)	66.89.0	57.2	69.26.2	59.4	74.69.7	66.2
Ours	66.82.5	64.0	80.25.3	75.0	74.012.2	50.0
Ours (cal)	77.01.1	75.0	76.65.1	70.0	72.812.6	48.0
AGNews	Random (cal)	73.06.6	61.8	71.95.0	64.0	78.24.7	71.6
Ours	79.61.4	77.0	76.44.6	69.0	76.04.0	71.0
Ours (cal)	77.41.4	76.0	76.04.4	68.0	76.43.6	70.0
CoLA	Random (cal)	68.55.5	61.2	67.85.1	63.6	54.012.4	42.4
Ours	68.26.2	57.0	70.64.2	64.0	72.04.5	66.0
Ours (cal)	68.05.2	58.0	70.43.8	65.0	72.04.5	66.0
Post calibration [18] can improve the accuracy of a certain prompt (in most cases), but when the selected prompt is very poor, the performance is still very poor even after calibration. We conducted experiments (Table 2) to compare the performance of G-fair-Prompting and random selection with calibration ("Average" and "Worst" indicate averaged accuracy and worst performance on all permutations of training examples), and observed that G-fair-Prompting outperforms random selection with calibration in most case. For instance, on the CoLA, G-fair-Prompting exhibited superior performance on most models. Additionally, we find that post-calibration could negatively affect the model's performance in many scenarios while it sometimes can improve the performance significantly even on selected prompts, for example, an improvement by  $10\%$  is observed on BLOOMTREC. For more detailed discussions, please refer to Appendix A.5. Hence, it is crucial to reconsider the impact of directly manipulating the model's probability.
In this paper, we revisit the sensitivity of large language model across prompts, and analyse the issue from a predictive bias perspective. Accordingly, we employ a "content-free" strategy as a metric termed as fairness to evaluate the predictive bias of a fixed prompt and show that model's performance is highly consistency with fairness. Then, we propose two strategies to search the most fair prompt in the original space. We conduct extensive experiments on current famous LLMs, and validate the effectiveness of the proposed strategy. Moreover, in addition to fairness adopted in this paper, there would be more metrics for prompt searching in the future for different scenarios.
This work is supported by the National Natural Science Foundation of China (Grant No. 61976151 and 61925602), and Fu is supported by A*STAR Central Research Fund. The project was conducted during the internship in AI Lab, Tencent.
[1] Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. Language models are few-shot learners. In NeurIPS, volume 33, pages 1877–1901, 2020.
[2] Bloom: A 176b-parameter open-access multilingual language model. https://huggingface.co/bigscience/bloom.
[3] Fabio Petroni, Tim Roktaschel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, Alexander H Miller, and Sebastian Riedel. Language models as knowledge bases? arXiv preprint arXiv:1909.01066, 2019.
[4] Yao Lu, Max Bartolo, Alastair Moore, Sebastian Riedel, and Pontus Stenetorp. Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity. In ACL, 2021.
[5] Feng Nie, Meixi Chen, Zhirui Zhang, and Xu Cheng. Improving few-shot performance of language models via nearest neighbor calibration. arXiv preprint arXiv:2212.02216, 2022.
[6] Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Computing Surveys, 55(9):1-35, 2023.
[7] Xiang Lisa Li and Percy Liang. Prefix-tuning: Optimizing continuous prompts for generation, 2021.
[8] Xiao Liu, Kaixuan Ji, Yicheng Fu, Zhengxiao Du, Zhilin Yang, and Jie Tang. P-tuning v2: Prompt tuning can be comparable to fine-tuning universally across scales and tasks. arXiv preprint arXiv:2110.07602, 2021.
[9] Karen Hambardzumyan, Hrant Khachatrian, and Jonathan May. Warp: Word-level adversarial reprogramming. arXiv preprint arXiv:2101.00121, 2021.
[10] Guanghui Qin and Jason Eisner. Learning how to ask: Querying lms with mixtures of soft prompts. arXiv preprint arXiv:2104.06599, 2021.
[11] Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang, and Jie Tang. Gpt understands, too. arXiv preprint arXiv:2103.10385, 2021.
[12] Zhuosheng Zhang, Aston Zhang, Mu Li, and Alex Smola. Automatic chain of thought prompting in large language models. arXiv preprint arXiv:2210.03493, 2022.
[13] Claudio Gentile, Zhilei Wang, and Tong Zhang. Fast rates in pool-based batch active learning. arXiv preprint arXiv:2202.05448, 2022.
[14] Shizhe Diao, Pengcheng Wang, Yong Lin, and Tong Zhang. Active prompting with chain-of-thought for large language models. arXiv preprint arXiv:2302.12246, 2023.
[15] Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, and Weizhu Chen. What makes good in-context examples for gpt-3? arXiv preprint arXiv:2101.06804, 2021.
[16] Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales, David Dohan, Ed Chi, Nathanael Schärli, and Denny Zhou. Large language models can be easily distracted by irrelevant context. arXiv preprint arXiv:2302.00093, 2023.
[17] https://openai.com/blog/chatgpt.
[18] Tony Z. Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. Calibrate before use: Improving few-shot performance of language models. In ICML, 2021.
[19] Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Roziere, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971, 2023.
[20] Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. Language models are unsupervised multitask learners. Technical Report, 2018.
[21] Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman. Glue: A multi-task benchmark and analysis platform for natural language understanding. arXiv preprint arXiv:1804.07461, 2018.
[22] Ben Wang and Aran Komatsuzaki. GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model. https://github.com/kingoflolz/mesh-transformer-jax, May 2021.
[23] https://huggingface.co/EleutherAI/gpt-neox-20b.
[24] OpenAI. Gpt-4 technical report, 2023.
A.1 Pretrained Large Language Models
Neural autoregressive language model (LMs) are designed for next token prediction to predict the probability distribution over the next token after a sequence of tokens input, and pre-trained LMs show their superior performance since they are trained on various programming languages and a large-scale curated dataset. Training large natural LMs are very expansive and time-consuming process since they always have billions of parameters, which limits the development of LMs. Fortunately, many pre-trained LMs are open access or limited access, which promotes researchers to pool their time and makes the resources to collectively achieve a higher impact. EleutherAI makes the GPT-J [22] and GPT-Neox [23] public available on Hugging Face. GPT-3 [1] is limited access in OpenAI which can be used by researchers for a fee, and another large open-science open-access multilingual language model named Bloom [2] is provided by BigScience.
A.2 Open Access Models
Table 3: Pretrained language models
Model	Params	Provider	Access
GPT-2	124 M	Hugging Face	OPEN
GPT-Medium	335 M	Hugging Face	OPEN
GPT2-Large	774 M	Hugging Face	OPEN
GPT-XL	1.5 B	Hugging Face	OPEN
GPT-3 (ada)	350 M	OPENAI	LIMITED
GPT-3 (babbage)	1.3 B	OPENAI	LIMITED
GPT-3 (curie)	6.7 B	OPENAI	LIMITED
GPT-3 (davinci)	175 B	OPENAI	LIMITED
GPT-J	6 B	EleutherAI	OPEN
GPT-NeoX	20 B	EleutherAI	OPEN
Bloom	176 B	BigScience	OPEN
LLaMA	7 B	Meta	OPEN
13 B	Meta	OPEN
33 B	Meta	OPEN
65 B	Meta	OPEN
A.3 Additional Figures on Different Settings
In addition to the Fig. 2, we show the performance on different models for enumerating all candidates, note that the shadow indicates the half value of standard deviation for clear presentation since the variance is very high for LLMs.
A.4 Accuracy Varies with demonstrations
Accuracy Varies with Example Amount Demonstrations play an important role in imparting task-related information to language models through in-context learning. Then, the question arises - does a larger number of demonstrations necessarily equate to better performance? To answer this question, we evaluated performance in terms of accuracy by gradually increasing the number of demonstrations. We set  $\rho = \Gamma(x_1, y_1) \oplus \dots \oplus \Gamma(x_k, y_k)$ , where  $k = 1, \dots, n$ , and demonstrations are erased with  $k$  decreasing from  $n$  to 1. Intuitively, accuracy would vary highly across different numbers of demonstrations, and the phenomenon is observed in Fig. 6a. To our surprise, however, erasing some demonstrations can result in a better performance. Removing some demonstrations can perform better and sometimes GPT-3 achieves best accuracy when there is only a few demonstrations remaining. This highlights the importance of considering the appropriate number of demonstrations.
(a) AGNews (GPT2-XL 1.5B)
(b) TREC (GPT2-XL 1.5B)
(c) RTE (GPT2-XL 1.5B)
(d) AGNews (LLaMA 33B)
(e) TREC (LLaMA 33B)
Figure 5: Accuracy is highly consistency with fairness and greedy search can find a good prompt, where "Random" and "Oracle" indicates the average accuracy of all prompts and the upper-bound performance according to fairness.
(f) SST-2 (LLaMA 33B)
(a) Varying amount of examples
Figure 6: ICL suffers from high instability due to variations in example amount, example order, and example selection.
(b) Permutation
(c) Select different examples
Example Order The performance of a model is sensitive to the order of the demonstrations, as has been discussed in [4]. Even when the demonstrations are the same, different permutations of the demonstrations can result in vastly different outcomes. As there are  $n!$  possible permutations, we introducing a strategy of permuting the demonstrations by circularly shifting the index of the demonstrations. The demonstration can be represented as  $\rho = \Gamma(x_{k+1}, y_{k+1}) \oplus \dots \oplus \Gamma(x_n, y_n) \oplus \Gamma(x_1, y_1) \oplus \dots \oplus \Gamma(x_k, y_k)$ . As shown in Fig. 6b, the accuracy varies highly with permutation which is consistent with the observations in [4].
Example Selection In this paper, we find which demonstrations are selected is influence the model extremely. This scenario can be described as selecting  $k$  demonstrations in  $n$  training samples. In Fig. 6c, we only select one example for demonstration to ablate the impact of demonstrations order, and the accuracy also varies highly with different example selected. In this work, we only detail evaluate the proposed probing method on the erasing demonstrations and permutation, although our method improves by  $20\%$  in the setting of example selection on SST-2 (GPT2-XL), because selecting  $k$  demonstrations on a set with  $n$  training samples can't be regarded as  $k$ -shot learning in the strict sense.
A.5 Relationship between with- and without-calibration
- G-fair-Prompting without post-calibration outperforms random demonstrations after post-calibration. Based on Table 1, it is apparent that G-fair-Prompting outperforms random selection prior to post-calibration. This leads to a natural question: do prompts with better performance before calibration also indicate better performance after calibration proposed by Zhao et al. [18]? To investigate the relationship between performance with- and without-calibration, we calculated the Pearson correlation coefficient between the accuracy with- and without-calibration Pearson  $(acc_{w / o}, acc_{with})$ . A positive coefficient value suggests that a prompt with high accuracy before calibration has a
Table 4: Accuracy for different prompting strategies (averaged on  ${5}_{0},\ldots ,{}_{4}$  different seeds).
Model	Dataset	Random	Diversity	Similarity	Top-2	Ours Top-4	Greedy
GPT2-XL (1.5B)	SST-2	61.16.1	-	-	60.811.4	65.88.7	74.212.0
AGNews	38.911.4	-	-	45.212.5	37.211.2	46.411.9
TREC	22.15.7	-	-	19.48.9	28.29.2	25.07.4
RTE	53.26.9	-	-	54.07.5	53.65.9	56.42.2
LLaMA (7B)	AGNews	64.510.0	66.49.1	-	66.011.7	69.25.5	63.85.7
TREC	49.510.4	51.49.6	-	48.410.5	38.615.2	61.34.8
CoLA	60.410.6	63.88.7	-	58.27.8	61.66.5	36.43.6
LLaMA (13B)	AGNews	72.27.7	78.43.5	-	73.69.0	74.24.3	75.22.8
TREC	46.416.5	48.016.0	-	51.016.6	39.223.3	61.412.1
CoLA	67.72.9	67.22.4	-	67.02.0	67.21.6	67.02.0
Figure 7: Illustration of accuracy relationship between with- and without calibration when Pearson(·) is positive.
higher likelihood of achieving higher accuracy after calibration than other prompts. We take the topic classification task on LLaMA(65B) for illustration to show the relationship between with- and without calibration when Pearson is positive in Fig.7. Table 5 presents the Pearson correlation coefficient on accuracy of permutation and G-fair-Prompting after calibration. The majority of Pearson correlation coefficients were found to be positive, indicating that prompts with better performance before calibration have more potential to perform well after calibration. Furthermore, our results on the LLaMA family reveal that the larger the model, the stronger the correlation between performance with- and without-calibration. For instance, the value of the Pearson correlation coefficient increases from 0 to 0.7 as the model size increases.
Theorem A.1. Suppose the performance of the model under certain prompts with- and without calibration is positively correlated, i.e.,  $\text{Pearson}(acc_{w/o}, acc_{with}) > 0$ , if we can assure  $\mathbb{E}(acc_{w/o}^{Selected}) > \mathbb{E}(acc_{w/o}^{Random})$ , then we have  $\mathbb{E}(acc_{with}^{Selected}) > \mathbb{E}(acc_{with}^{Random})$ .
Table 5: Pearson's r between the with- and without-calibration.
Dataset	BLOOM
176B	7B	LLaMA
13B	33B	65B
TREC	0.1274	0.1551	0.2959	0.3090	0.5151
AGNews	0.3875	-0.0471	0.3044	0.6953	0.7100
CoLA	0.4050	0.3592	0.5193	0.3611	0.8012
As analysed in Theorem A.1, if we can find a prompt with high accuracy before calibration, we have a higher likelihood of achieving higher accuracy after calibration than random selection. Our approach consistently identifies an appropriate prompt, as evidenced by the results in Table 1. Moreover, the
performance of the model exhibits a positive correlation with and without calibration under certain prompts, as illustrated in Table 5. Therefore, our method is more likely to enhance calibration performance.
A.6 Diversity and Similarity
Diversity indicates the demonstrations are selected according to the diversity of the embeddings of all samples [12]. Specifically, we select the 4 most diverse samples as demonstrations by k-means clustering on training set consists of 16 samples.
Similarity indicates the demonstrations are selected according to the similarity of the embeddings of all samples [15]. Specifically, we select the 4 most similar samples as demonstrations according to Euclidean distance from the training set consists of 16 samples. Note that demonstrations for different test samples are different for best performance. A larger training set may result in a better performance, but we set the size of training set as 16 for a fair comparison.
Similarity
Figure 8: Illustration for Diversity and Similarity.
A.7 Details for experiments
In Fig. 1, we show the high instability due to high variations in demonstrations selection and order for both with- and without-calibration [18]. Specifically, for left two figures, we sample 4 different demonstrations randomly from dataset AGNews, and estimate the influence of demonstration selection. On the other hand, the right two figures show the instable performance due to permutation when the demonstrations are fixed. Specifically, we randomly sample 4 demonstrations and estimate the performance with all possible orders.
A.8 Complexity of different strategies
Figure 9: Computational cost. T-fair and G-fair indicate T-fair-Prompting and G-fair-Prompting respectively, and "w/c" indicates the worst case.
A.9 Limitations.