SlowGuess/NAACL_2022_OCR huggingface dataset:
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A Prompt Construction
Following Perez et al. (2021), we avoid prompt tuning on the full training and development sets of the datasets studied, in order to ensure that our methods represent a true few-shot setting. To develop the initial prompt design, we experimented with no more than 10 different layouts in the GPT-3 Sandbox platform using 15 training examples on the CoS-E and e-SNLI datasets. For subsequent prompt design, we again used these 15 training examples for each dataset from which we sampled 6 prompts,
question: When remembering a tragedy in the past, what do many people feel?
pain, depression, knowing, knowledge, or nostalgia?
why? Remembering a past tradedy can resurface feelings that arose in response to that tragedy. Because tragedies are not positive events, it's possible that sadness and depression could arise from remembering it.
question: What do people do sometimes when they find a good deal?
race cars, murder each other, believe in god, fight each other, or fear death?
fight each other
why? Malls sometimes have sales, e.g., on black friday, when they offer good deals; however, the items are sometimes in limited supply, which can cause altercations between folks, each trying to buy the same item.
question: What does someone who has a greed for energy do?
buy food, lie, get, cause businesses to grow, or win? buy food
why? When consumed, food provides energy and satisfies the greed for it.
question: Immediately after peeing, a person's bladder is what?
full, empty, filled, stretchable, or collapsed?
Table 11: Example of a prompt with 3 training examples for CommonsenseQA: presented are the question and answer choices, the gold labels, and the explanations (written by us) that act as input to GPT-3 (in practice, we use 8-24 examples per prompt). The text generated by the model acts as the free-text explanation. In this case, the model greedily auto-completes (given 8 examples): "After peeing, the bladder is empty."
along with a fixed 100-example "development set" randomly drawn from the training set. We preserve the "few-shot" approach by using a maximum of these same 115 instances to develop our prompting methods. For these 115 examples, the authors of this paper manually wrote high-quality explanations to be used as prompt examples (Table 12). As presented in Table 2, we found that structuring SNLI as a question-answering task achieved the best performance, similarly to Zhao et al. (2021). We provide an example of our SNLI prompt in Table 2 and CommonsenseQA in Table 11.
In-context learning methods have been shown to have high variance based on hyperparameters including example order, number of examples given, and which examples are given (Jiang et al., 2020; Zhao et al., 2021; Lu et al., 2022). While these
values have not been standardized, two prominent papers, Schick and Schütze (2021b) and Brown et al. (2020), use 32 and 64 prompt examples, respectively. Due to the 2049-token limit of the OpenAI GPT-3 API and the fact that the addition of explanations elongates each prompt instance, we find the maximum number of examples the API can accommodate is 24 for CoS-E, e-SNLI, and our handwritten explanations and 16 for ECQA.
The focus of this work is not on finding the optimal prompt, but on developing a general strategy for few-shot explanation generation that could be successful when no additional (large) validation set for tuning is available. Therefore, to provide as robust of an expected performance estimate as possible, we do not tune the additional hyperparameters, instead sampling them to approximate performance. $^{14}$  Namely, while prior work uses one fixed prompt for all instances and varies the random seed, we approximate the same expected performance by sampling a new set of prompts for each instance. We also sample the number of prompts for each instance (and shuffle their order) from the values  $\{8, 16, 24\}$  for CommonsenseQA experiments,  $\{8, 16\}$  for experiments using ECQA explanations, and  $\{12, 18, 24\}$  for SNLI experiments (to maintain label balance). To overcome label bias in prompt ordering, for tasks with distinct answer choices per instance (CommonsenseQA), we shuffle the answer choices. For tasks with fixed answer choices (SNLI), we sample an equal number of prompt instances for each label (so number of prompt instances is a multiple of 3).
Table 12 shows a few non-cherry-picked examples of our handwritten explanations used as prompts relative to the datasets.
B Crowdsourcing Details
We discuss shared details of the study designs in  $\S B.1$ . We discuss the head-to-head interface in  $\S B.2$ , the absolute interface in  $\S B.3$ , and the acceptability interface in  $\S B.4$ . Finally, we present details on quality control and payment in  $\S B.5$  and annotator statistics in  $\S B.6$ .
B.1 Shared Interface Details
For all three human subjects study designs designs, we show the user the input instance (e.g.,
$^{14}$ Perez et al. (2021) show that performing cross-validation or tuning via maximum description length over a small validation set does not significantly outperform random sampling of these values.
Premise: A person on a horse jumps over a broken down airplane.
Hypothesis: A person is training his horse for a competition.
Label: neutral
Our Explanation: While it is possible that jumping a horse over an obstacle is part of a training routine for a competition, it is also possible that the horse ride is being done for pleasure, not necessarily for a competition (sp).
e-SNLI Explanation: the person is not necessarily training his horse
Premise: Children smiling and waving at camera
Hypothesis: There are children present
Our Explanation: Since the children are part of the event of smiling at the camera, they are present at the event under discussion.
e-SNLI Explanation: The children must be present to see them smiling and waving.
Question: A cat can't talk, but a cat can what?
Answer choices: sleep all day, meow, shed fur, see king, live many years
Our Explanation: A cat can meow as a way to vocalize.
CoS-E Explanation: the cat is a small carnivorous mammal
ECQA Explanation: A cat can meow but cannot see the king. Meowing is how a cat communicates and not by sleeping all day, shedding fur or by living many years.
Question: "There are 10 apples on an apple tree. Three fall off. Now there are X apples." What is this an example of?
Answer choices: park, coloring book, garden center, math problem, gravity
Our Explanation: A math problem is usually posed as a question that requires some operation such as subtraction or addition to answer.
CoS-E Explanation: webmath is designed to help you solve
ECQA Explanation: Math problem is an arithmetical problem of addition, subtraction, multiplication or division. So "There are 10 apples on an apple tree. Three fall off. Now there are X apples." is a math problem. All the other options aren't problems to be examples of the given question.
Table 12: Examples of explanations used as prompts from various sources, including our handwritten explanations. Correct answers for CommonsenseQA are underlined.
premise + hypothesis) and the gold label in addition to the explanation(s). We explain our motivation for using the gold label as a methodological control in §2.
For a similar reason, we do not show the other incorrect label choices to the user, which is particu
larly of note for the CommonsenseQA task which has different answer choices per instance. Some instances in CommonsenseQA have multiple correct or very similar answer choices, due to noise in the dataset and the fact that the wrong answer choices were deliberately collected to make the task challenging. We (the authors) again found we struggled to accurately judge explanation quality when we disagreed with the selected answer choice or found multiple answer choices to be correct. To remove this possible confounder, we instruct participants to pretend the gold label is correct even if they disagree with it, and make this easier by hiding the other answer choices. This may result in a slight bias in judgements against the ECQA dataset due to its unique counterfactual nature, though our goal was not to study the benefits and downsides of counterfactual explanations in this work.
B.2 Head-to-Head Interface Details
We show the user the task input and gold label, and ask them to select which of two explanations best explains the answer. We instruct workers to consider the gold label to be correct even if they disagree with it (CommonsenseQA instances can be subjective) and to ignore minor grammar and spelling mistakes such as improper upper-casing. Figures 5 and 6 show the evaluation interface.
B.3 Absolute Interface Details
Figures 7 and 8 show the absolute evaluation interface (minus the acceptability attribute, which is collected in a separate run of the study). Our interface is inspired by prior work from psychology and the social sciences (Leake, 1991; Gopnik, 1998; Lombrozo, 2007; Zemla et al., 2017; Chiyah Garcia et al., 2018; Clinciu et al., 2021; Sulik et al., 2021). We iterated over 3-4 versions of the questions and UI design until we had optimized agreement rates as much as possible. Our resulting two-part evaluation consists of 7 questions:
Part 1: Context-Independent Evaluation We first assess the explanation in isolation, i.e., these questions are presented to the user without revealing the question/context that the explanation is attempting to address:
1. How factual is this statement? (generally false, sometimes or partially true, generally true, or need more information to judge). This question is designed to test both generality (can the explanation's truthfulness be ascertained or is more in-
formation needed?) and factuality, which aligns with "compatibility with receiver's existing beliefs" and that the best explanation is the "most likely" explanation from the receiver/user's perspective (Lombrozo, 2007; Zemla et al., 2017; Sulik et al., 2021). Generality is coded based on whether a truthfulness answer is selected (considered to be general) or whether the "need more information to judge" choice is selected (considered not to be general).
2. Is this statement grammatical? (yes or no) This question is designed to test for clarity, aligning with characteristics such as coherence (Lei et al., 2016) and human-likeness and understandability (Ehsan et al., 2019).
Part 2: Context-Dependent Evaluation We next show the user the question (premise and hypothesis for SNLI) and gold answer that the explanation was conditioned on. We then ask:
1. Does the explanation provide new facts, information or reasoning not stated in the question and answer? (yes or no) In our preliminary experiments, we found some explanations simply restate the question declaratively with the answer filled in. This question addresses the distinction between "validity" and "utility" (Leake, 1991): an explanation can be valid (i.e., a restatement of the question with the answer filled-in might be correct), but not useful; utility is defined by whether an explanation "satisfies an explainer's need for information". And while utility is best understood in the context of real-world applications (Lai et al., 2020), we nonetheless aim to identify vacuous explanations that do not provide new information.
2. Is the new information relevant to justifying the answer? (yes or no) New information, if provided, “should be compatible with our existing beliefs, and consistent with the evidence and with itself” (Zemla et al., 2017). This question is designed to test whether the information provided supports the label. The specific interpretation of “relevance” is purposefully left to the annotator.[15]
3. How much information does the explanation have to justify the answer? (not enough, enough, or too much) This question is designed to test
the extent to which the provided novel information is adequate or sufficient (Kim et al., 2016; Lei et al., 2016; Ehsan et al., 2019).<sup>16</sup>
4. Is the explanation acceptable? (yes or no) The final question is designed to assess annotators' overall judgement of the explanation as a whole.
We only ask Question 2 if the answer to Question 1 is "yes" and Question 3 if the answer to Question 2 is yes, because they regard the new facts, information, or reasoning. We found that most prior work tends to lump added-value, relevance, and adequacy judgements into one "informativeness" judgement (Clinciu et al., 2021), which we felt was too course to allow for meaningful error analysis.
B.4 Acceptability Interface Details
Figures 9 and 10 show the binary acceptability interface used to collect training and test data for the overgeneration filter model.
Spearman's rank-order correlation coefficients (Table 6) are computed using scipy (Virtanen et al., 2020) on the 250 test explanations from the 5 data sources in Figure 2. Each instance is annotated by 3 annotators for a total of 3750 datapoints (some criteria are only evaluated conditionally, resulting in less total annotations- see Appendix B.3). Statistical significance is computed using the built-in two-sided non-parametric test.
B.5 Quality Control and Payment
We use Amazon Mechanical Turk (AMT), and calculate pay on a rate of  $15/hour. Every few batches, we check to ensure that the median time taken per-annotator amounts to approximately this pay rate. While annotators do tend to speed up the more HITs we released, first-round median times were approximately 30 seconds per head-to-head evaluation HIT (thus paid at$ 0.12 each), 1 minute per absolute evaluation HIT (thus paid at $0.25 each), and 35-39 seconds per acceptability HIT (5 explanations; paid at $0.20 each).
We require annotators to be located in either Australia, Canada, New Zealand, the United Kingdom, or the United States, as a proxy for English competency.[17] We require a past HIT approval rate of  $>$
16 In practice, we do not find Turkers use the "too much information" option often, except in the case of ECQA dataset explanations. We included the option because succinctness is an oft-cited explanatory virtue (Lombrozo, 2007; Zemla et al., 2017; Chiyah Garcia et al., 2018).
17We realize this is a broad assumption and likely suboptimal. However, colleagues have found that broadening
This decision is inspired by prior work in psychology, which finds that explanations are only good "to the extent that people find [them] satisfying" (Gopnik, 1998; Sulik et al., 2021).
98% and  $>5000$  HITs approved. We do not allow annotators to participate who were previously on a block list from our past AMT studies.