While large pre-trained language models are powerful, their predictions often lack logical consistency across test inputs. For example, a state-of-the-art Macaw question-answering (QA) model answers Yes to Is a sparrow a bird? and Does a bird have feet? but answers No to Does a sparrow have feet?. To address this failure mode, we propose a framework, Consistency Correction through Relation Detection, or ConCoRD, for boosting the consistency and accuracy of pre-trained NLP models using pre-trained natural language inference (NLI) models without fine-tuning or re-training. Given a batch of test inputs, ConCoRD samples several candidate outputs for each input and instantiates a factor graph that accounts for both the model's belief about the likelihood of each answer choice in isolation and the NLI model's beliefs about pair-wise answer choice compatibility. We show that a weighted MaxSAT solver can efficiently compute high-quality answer choices under this factor graph, improving over the raw model's predictions. Our experiments demonstrate that ConCoRD consistently boosts accuracy and consistency of off-the-shelf closed-book QA and VQA models using off-the-shelf NLI models, notably increasing accuracy of LXMERT on ConVQA by 5% absolute.

TL;DR: We introduce ConCoRD, a method that extracts more internally consistent (and ultimately more accurate) predictions from a pre-trained language model for a batch of test inputs, without fine-tuning!

An Overview of ConCoRD

ConCoRD processes a batch of test inputs in three steps. In step one, ConCoRD samples several candidate outputs for each test input from the base model. In a question-answering setting, we sample several candidate answers for each test question, perhaps using techniques like diverse beam search.

In step two, ConCoRD runs an off-the-shelf Natural Language Inference (NLI) model on pairs of model beliefs, where a model belief corresponds to a pair of (input, candidate output). The NLI model estimates the likelihood that an entailment relation, contradiction relation, or no relation exists between a belief pair. For the pairs where an entailment or contradiction relation is likely, we intuitively need to balance the base language model's original confidence scores for each answer with the need to satisfy the relationships detected by the NLI model.

In the step three, ConCoRD uses a MaxSAT solver to find the approximately optimal choices of model outputs that balances the base model's original confidence and the compatibility between answers, as determined by the NLI model. This optimization problem is equivalent to finding the maximum probability assignment of the variables in a factor graph that contains unary factors reflecting the probability that the base model assigns to each answer and binary factors reflecting the probability that the NLI model assigns to an entailment or contradiction relation existing between a pair of answers.

How does ConCoRD create a factor graph from the predictions of our base model and NLI model? The example below shows a factor graph for a batch of two test questions: What is the capital of Afghanistan? and What is the capital of Georgia? ConCoRD defines a binary variable z_ij representing the truth of each candidate answer a_ij. The factor graph is defined over these binary truth variables. In addition to unary factors for the probability assigned to each answer by the base model and binary factors representing the NLI model's predictions, ConCoRD includes mutual exclusivity (XOR) factors among the set of answers for a given question (to represent the constraint that the binary truth variable must be True for exactly one answer per problem). ConCoRD converts this factor graph into a weighted MaxSAT problem, for which optimized solvers exist.

In general, we don't need to assume that all test questions within a batch are closely related; we use a pair of questions that are closely related here only for illustrative purposes. In practice, the NLI model is responsible for the identifying pairs of model beliefs for which a relationship actually exists.

@inproceedings{mitchell2022enhancing,
    title={Enhancing Self-Consistency and Performance of
           Pretrained Language Models with NLI},
    author={Mitchell, Eric and Noh, Joseph J. and Li, Siyan and
            Armstrong, William S. and Agarwal, Ananth and
            Liu, Patrick and Finn, Chelsea and Manning, Christopher D.},
    booktitle={Proceedings of the 2022 Conference on Empirical
               Methods in Natural Language Processing (EMNLP)},
    url={https://ericmitchell.ai/concord.pdf},
    year={2022},
    publisher={Association for Computational Linguistics}
}

The authors would like to thank the anonymous reviewers for their helpful feedback during the review period, Gabe Mudel, Julie Wang, Cameron Tew, Anthony Tzen, Kevin Yang, and Ian Ng for helpful discussions and assisting with exploratory experiments early on in the project, and Nora Kassner for providing helpful early guidance in configuring the BeliefBank experiments. CF and CM are CIFAR Fellows. EM gratefully acknowledges funding from the Stanford Knight-Hennessy Graduate Fellowship. JN is supported by Stanford University Medical Scientist Training Program grant T32-GM007365. SL acknowledges brownie bites from Target for providing a crucial fuel source for late night experiment-running.

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