Answer-level Calibration for Free-form Multiple Choice Question Answering (2022)

Abstract: Pre-trained language models have recently shown that training on large corpora using the language modeling objective enables few-shot and zero-shot capabilities on a variety of NLP tasks, including commonsense reasoning tasks. This is achieved using text interactions with the model, usually by posing the task as a natural language text completion problem. While using language model probabilities to obtain task specific scores has been generally useful, it often requires task-specific heuristics such as length normalization, or probability calibration. In this work, we consider the question answering format, where we need to choose from a set of (free-form) textual choices of unspecified lengths given a context. We present ALC (Answer-Level Calibration), where our main suggestion is to model context-independent biases in terms of the probability of a choice without the associated context and to subsequently remove it using an unsupervised estimate of similarity with the full context. We show that our unsupervised answer-level calibration consistently improves over or is competitive with baselines using standard evaluation metrics on a variety of tasks including commonsense reasoning tasks. Further, we show that popular datasets potentially favor models biased towards easy cues which are available independent of the context. We analyze such biases using an associated F1-score. Our analysis indicates that answer-level calibration is able to remove such biases and leads to a more robust measure of model capability.

## introduction language models (lm), trained on large corpora, have been shown to exhibit few-shot and zero-shot learning capability @xcite using only text interactions, as opposed to finetuning the model parameters using task specific training examples. relying purely on text interactions for few-shot ability shifts the fo-cus to designing and utilizing suitable task-specific natural language templates. in this work, we focus on free-form multiple choice question answering (and commonsense reasoning tasks in particular), where given a context and a set of choices of unspecified lengths, a model is required to select the most suitable choice. to enable zero-shot learning, the typical approach is to form textual sequences by concatenating the context independently with each choice and then scoring the concatenated strings using a pre-trained lm. while lm probabilities have been shown to provide useful estimates of choice probabilities given a context, there is no incentive to treat the choices as equal in the absence of the associated context. for example, the lm probabilities in a neutral context are likely to be determined by frequency. in this work, we explore the role of biases that are likely to be associated with the choices naturally due to the language modeling objective. we propose alc 1 (answer-level calibration), where we use a neutral context to model such biases and remove them using a scaling factor determined by how similarly a model handles the question context as compared to a neutral context. further, we show that popular datasets favor models which rely on easy cues which are context independent. we use a bias-specific f1 score to analyze such biases. our results indicate the need for answer-level calibration for more accurate estimates of model capabilities, or equivalently the design of better datasets. we hope our work will be useful for further research in both those directions. specifically, we analyze context-independent biases related to length, part-of-speech (pos) and neutral context probabilities of the choices. in summary, we make the following contributions: calibration probabilities output by neural networks are known to suffer from lack of calibration @xcite , including lm output probabilities @xcite . @xcite use token-level calibration to improve on few-shot classification and generation tasks. in contrast, we show that answer-level calibration is more suitable for the multiple choice setting that we consider. while we focus on free-form multiple choice questions in this work, when the choices are single tokens, for example in a classification task where the choices are true and false, answer-level calibration would behave similar to token-level calibration. as a result, answer-level calibration can be seen to have a more general scope as also illustrated empirically through our experiments. further, our analysis (section 3.4) shows that answer-level calibration provides a more reliable measure of model performance on datasets with potential biases. finally, @xcite explore supervised methods, including finetuning as well as post-hoc methods, to improve calibration using training examples. in this work, we focus mainly on unsupervised calibration. answer-level calibration brown et al. (2020) generally perform length normalization over the token probabilities for a choice, while observing that for a select few tasks they obtain performance gains when using an answer-level calibration scheme (which corresponds to the unscaled version in equation 3 of alc). they use task specific development sets to choose between length normalization and answer-level calibration which is undesirable for few-shot learning @xcite , and specifically for zero-shot learning. in this work, we show that unscaled calibration (as in equation 3 ) is suboptimal, compared to our proposed scaled version. more recently, @xcite also arrive at a formulation equivalent to the unscaled version of alc but are motivated differently. specifically, they hypothesize that the possibility of different surface forms of the same concept causes a competition between surface forms when scored by the lm. in contrast, we are motivated by calibration concerns and the presence of context-independent biases. we justify this motivation through bias associated evaluation (section 5.2) for both the unscaled and scaled versions of alc. alternative approaches using enhanced context one way to make the probability estimates of the choices more accurate is to enhance the context using more task-specific cues. for example, @xcite show that with just a few in-context examples, significant gains in performance can be obtained. at the same time, it has been shown that the order of examples as well as token-level calibration in such prompts can be critical for getting good performance @xcite . while the gains from enhanced context through additional examples may be complementary to answer-level calibration, we focus on the zeroshot setting in this work. in the zero-shot setting, @xcite , working on the question answering format, propose generating textual clarifications using the pre-trained lm itself, to enhance the context and improve zero-shot performance of pre-trained lm on commonsense reasoning tasks. while their method has a much higher computational cost, we use it as an unsupervised baseline and show improvement over it on most tasks we consider. ## alc: proposed method we introduce the problem setting and notation in section 3.1. we briefly describe our motivation in section 3.2 and discuss the core idea of removing context-independent biases in section 3.3. we provide the natural language formatting used in our experiments in section a.3. we discuss bias associated measures in section 3.4. ## experimental results we aim to answer the following questions: 4 https://github.com/vered1986/self_ talk q1 how does alc compare with baselines using standard evaluation (accuracy) on free-form multiple choice question answering tasks? (section 5.1) q2 does the aforementioned evaluation reflect true model capability? to answer this question, we perform a series of bias associated evaluations (see section 3.4) and also evaluate whether alc helps overcome such biases. specifically, we evaluate on biases related to answer length, pos tag and context-ignorant lm probability. (section 5.2) q3 does alc improve expected calibration error @xcite ? (section 5.3) ## conclusion we propose alc (answer-level calibration), an unsupervised method to improve performance of pretrained language models. we show that, when compared to existing baselines, alc is more suitable for free-form multiple choice question answering, including commonsense reasoning tasks. we also show that popular datasets favor models which rely on easy cues for predictions, and that alc provides more reliable estimates of model capabilities by getting rid of some of these biases. data size copa @xcite 100 copa-test @xcite 499 commonsenseqa @xcite 1221 mctaco @xcite 454 socialiqa @xcite 1954 piqa @xcite 1838 winogrande @xcite 1267 alc (easy) @xcite 570 alc (challenge) @xcite 299 swag @xcite 20000 dream @xcite 2040 hendrycks @xcite 14042