Mike D’Arcy, Alexis Ross, Erin Bransom, Bailey Kuehl, Jonathan Bragg, Tom Hope, Doug Downey. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

When a teacher provides examples for a student to study, these examples must be informative, enabling a student to progress from their current state toward a target concept or skill.Good teachers must therefore simultaneously infer what students already know and adapt their teaching to students' changing state of knowledge.There is increasing interest in using computational models, particularly large language models, as pedagogical tools.As students, language models in particular have shown a remarkable ability to adapt to new tasks given small numbers of examples.But how effectively can these models adapt as teachers to students of different types?To study this question, we introduce a suite of models and evaluation methods we call ADAPT.ADAPT has two components: (1) a collection of simulated Bayesian student models that can be used for evaluation of automated teaching methods; (2) a platform for evaluation with human students, to characterize the real-world effectiveness of these methods.We additionally introduce (3) ATOM, a new probabilistic method for adaptive teaching that jointly infers students' past beliefs and optimizes for the correctness of future beliefs.In evaluations of simulated students across three learning domains (fraction arithmetic, English morphology, function learning), ATOM systematically outperforms LLM-based and standard Bayesian teaching methods.In human experiments, both ATOM and LLMs outperform non-adaptive random example selection.Our results highlight both the difficulty of the adaptive teaching task and the potential of learned adaptive methods for solving it.

Zhaofeng Wu, Linlu Qiu, Alexis Ross, Ekin Akyürek, Boyuan Chen, Bailin Wang, Najoung Kim, Jacob Andreas, Yoon Kim. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

Selective rationales and counterfactual examples have emerged as two effective, complementary classes of interpretability methods for analyzing and training NLP models.However, prior work has not explored how these methods can be integrated to combine their complementary advantages.We overcome this limitation by introducing CREST (ContRastive Edits with Sparse raTionalization), a joint framework for selective rationalization and counterfactual text generation, and show that this framework leads to improvements in counterfactual quality, model robustness, and interpretability.First, CREST generates valid counterfactuals that are more natural than those produced by previous methods, and subsequently can be used for data augmentation at scale, reducing the need for human-generated examples.Second, we introduce a new loss function that leverages CREST counterfactuals to regularize selective rationales and show that this regularization improves both model robustness and rationale quality, compared to methods that do not leverage CREST counterfactuals.Our results demonstrate that CREST successfully bridges the gap between selective rationales and counterfactual examples, addressing the limitations of existing methods and providing a more comprehensive view of a model's predictions.

Controlled text perturbation is useful for evaluating and improving model generalizability.However, current techniques rely on training a model for every target perturbation, which is expensive and hard to generalize.We present Tailor, a semantically-controlled text generation system.Tailor builds on a pretrained seq2seq model and produces textual outputs conditioned on control codes derived from semantic representations.We craft a set of operations to modify the control codes, which in turn steer generation towards targeted attributes.These operations can be further composed into higher-level ones, allowing for flexible perturbation strategies.We demonstrate the effectiveness of these perturbations in multiple applications.First, we use Tailor to automatically create high-quality contrast sets for four distinct natural language processing (NLP) tasks.These contrast sets contain fewer spurious artifacts and are complementary to manually annotated ones in their lexical diversity.Second, we show that Tailor perturbations can improve model generalization through data augmentation.Perturbing just ∼2% of training data leads to a 5.8-point gain on an NLI challenge set measuring reliance on syntactic heuristics.

Rationalization is fundamental to human reasoning and learning.NLP models trained to produce rationales along with predictions, called self-rationalization models, have been investigated for their interpretability and utility to end-users.However, the extent to which training with human-written rationales facilitates learning remains an under-explored question.We ask whether training models to selfrationalize can aid in their learning to solve tasks for the right reasons.Specifically, we evaluate how training self-rationalization models with free-text rationales affects robustness to spurious correlations in fine-tuned encoderdecoder and decoder-only models of six different sizes.We evaluate robustness to spurious correlations by measuring performance on 1) manually annotated challenge datasets and 2) subsets of original test sets where reliance on spurious correlations would fail to produce correct answers.We find that while self-rationalization can improve robustness to spurious correlations in low-resource settings, it tends to hurt robustness in higher-resource settings.Furthermore, these effects depend on model family and size, as well as on rationale content.Together, our results suggest that explainability can come at the cost of robustness; thus, appropriate care should be taken when training self-rationalizing models with the goal of creating more trustworthy models.

Much recent work in NLP has documented dataset artifacts, bias, and spurious correlations between input features and output labels.However, how to tell which features have "spurious" instead of legitimate correlations is typically left unspecified.In this work we argue that for complex language understanding tasks, all simple feature correlations are spurious, and we formalize this notion into a class of problems which we call competency problems.For example, the word "amazing" on its own should not give information about a sentiment label independent of the context in which it appears, which could include negation, metaphor, sarcasm, etc.We theoretically analyze the difficulty of creating data for competency problems when human bias is taken into account, showing that realistic datasets will increasingly deviate from competency problems as dataset size increases.This analysis gives us a simple statistical test for dataset artifacts, which we use to show more subtle biases than were described in prior work, including demonstrating that models are inappropriately affected by these less extreme biases.Our theoretical treatment of this problem also allows us to analyze proposed solutions, such as making local edits to dataset instances, and to give recommendations for future data collection and model design efforts that target competency problems.

As machine learning models are increasingly deployed in high-stakes domains such as legal and financial decision-making, there has been growing interest in post-hoc methods for generating counterfactual explanations. Such explanations provide individuals adversely impacted by predicted outcomes (e.g., an applicant denied a loan) with recourse---i.e., a description of how they can change their features to obtain a positive outcome. We propose a novel algorithm that leverages adversarial training and PAC confidence sets to learn models that theoretically guarantee recourse to affected individuals with high probability without sacrificing accuracy. We demonstrate the efficacy of our approach via extensive experiments on real data.

Alexis Ross, Ellie Pavlick. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.