Elaboration-Generating Commonsense Question Answering at Scale (2023)

Abstract: In question answering requiring common sense, language models (e.g., GPT-3) have been used to generate text expressing background knowledge that helps improve performance. Yet the cost of working with such models is very high; in this work, we finetune smaller language models to generate useful intermediate context, referred to here as elaborations. Our framework alternates between updating two language models—an elaboration generator and an answer predictor—allowing each to influence the other. Using less than 0.5% of the parameters of GPT-3, our model outperforms alternatives with similar sizes and closes the gap with GPT-3 on four commonsense question answering benchmarks. Human evaluations show that the quality of the generated elaborations is high.

## introduction commonsense question answering (qa; @xcite provides benchmarks used to evaluate the extent to which nlp models-increasingly based on language models-can "understand" questions and reason about their answers. for example, consider the question in figure 1 : gases released during the use of fossil fuels cause a what? a reasonably informed human could give the answer global warming, by reasoning that: fossil fuel emissions are the main source of greenhouse gases. they cause global warming. it is common to use lms to predict answers directly for qa tasks @xcite @xcite . on challenging datasets whose questions rely on unstated background knowledge @xcite @xcite , some recent works rely on external knowledge, e.g., wikipedia or structured knowledge bases @xcite frank, 2018; @xcite for additional information that helps to answer the question. such attempts are limited by the availability and coverage of the knowledge sources. another line of study @xcite @xcite ) reveals that generating text that expresses additional background knowledge relevant to a question is beneficial for answer prediction. the ability to express such knowledge may promote model explainability by explicitly showing the reasoning process. however, expressing high-quality knowledge relies on massive (and thus, expensive) pretrained lms, e.g., gpt-3 with 175b parameters @xcite . in this work, we focus on a more practical setting and ask: can smaller lms, e.g., bart which is about 400× smaller than gpt-3, support reasoning and inference in an end-to-end manner? to this end, we propose a scalable framework, alternating elaboration and answer predictor (elabor), consisting of two interacting modules: an elaboration generator and an answer predictor. here an elaboration refers to additional context describing some background knowledge about the question. instead of generating elaborations independently, we propose a probabilistic framework that treats the elaboration as a latent variable and iteratively optimizes the elaboration generator after receiving feedback from the answer prediction. specifically, for each question-answer pair (q, a), we decompose the distribution of the answer conditioned on the question p (a | q) into a distribution p (e | q) over a latent elaboration, modeled by the elaboration generator, and a likelihood distribution p (a | e, q) over the answer, modeled by the answer predictor. we alternately train the elaboration generator and the answer predictor so that each can benefit the other. earlier work either pre-constructs elaborations e from external knowledge @xcite or learns p (e | q) solely based on annotations @xcite ; we learn the elaboration generator by distilling high-quality knowledge from gpt-3. we do this using a procedure inspired by hard expectation-maximization @xcite . this involves refining and filtering elaborations informed by the answer predictor, as shown in figure 1 . elabor is thus capable of propagating information in both directions: from elaboration generator to answer predictor and vice versa. we conduct experiments on four commonsense qa datasets: commonsenseqa @xcite , commonsenseqa 2.0 @xcite , scientific commonsense @xcite , and openbookqa @xcite . our experiments reveal that (1) alternating training with smaller lms (e.g., bart, and gpt-2) narrows the gap between small models and gpt-3; (2) the ability to generate and reason with background elaborations indeed brings larger performance gains than direct inference on more challenging commonsense qa datasets; (3) the alternating framework helps to filter irrelevant elaborations generated from gpt-3 and the learned elaboration generator can express information that helps to answer the question, as shown through human evaluations. ## modeling answers and elaborations we focus on the task of commonsense question answering in the multiple-choice setting: we seek to identify the answer to a commonsense question among provided candidate choices. importantly, we are not provided with additional elaboration that may be needed to do so. we formalize the setting and define the model in this section, and section 3 details the training procedure. ## alternating elaboration and answer predictor (elabor) many existing retrieval or knowledge-based qa methods only optimize p (a | e, q), assuming e is given and fixed. explanation-based methods, on the other hand, train p (e | q) separately using human-annotated explanations. doing so poses two problems: (1) we need an annotated explanation corpus, and (2) the elaboration generator cannot be calibrated towards the answer. in this work, we propose an approach that tackles both problems by jointly training the elaboration generator and the answer predictor in an alternating framework. figure 2 illustrates the overall architecture for training. in each iteration, the elaboration generator f e learns to produce high-quality elaborations using feedback from the answer predictor (section 3.1). the answer predictor f a then takes the generated elaborations as input to produce more reliable answers (section 3.2). this strategy allows mutual interaction between the two components, propagating information in both directions. to reduce the search space of possible elaborations, we propose to distill knowledge from the pretrained gpt-3 model in a selective way to learn a lightweight elaboration generator (section 3.3). ## experiments in this section, we examine the question: does jointly optimizing the elaboration generator with the answer predictor outperform approaches that merely retrieve knowledge from trained models, if at all? as a secondary objective, we also investigate the impact of the design choices in our approach, including the choice of the language model, ## conclusion we propose a framework for commonsense qa problems that alternates between learning a meaningful, relatively lightweight elaboration generator and producing an answer from the question and automatically generated elaboration. these two steps are trained interactively, propagating signals to each other. we narrow the performance gap between small lms and gpt-3, with the elaboration generator producing elaborations judged useful by humans, and matching the performance of the much more expensive gpt-3 model as an elaboration generator. one limitation of elabor is lack of exploration beyond gpt-3. we consider investigating this problem as our future work.