Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference

• URL: http://arxiv.org/abs/2211.11875v1
• Date: Mon, 21 Nov 2022 21:58:30 GMT
• Title: Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference
• Authors: Eric Mitchell, Joseph J. Noh, Siyan Li, William S. Armstrong, Ananth Agarwal, Patrick Liu, Chelsea Finn, Christopher D. Manning
• Abstract summary: Large pre-trained language models often lack logical consistency across test inputs. We propose a framework, ConCoRD, for boosting the consistency and accuracy of pre-trained NLP models. We show that ConCoRD consistently boosts accuracy and consistency of off-the-shelf closed-book QA and VQA models.
• Score: 72.61732440246954
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. See https://ericmitchell.ai/emnlp-2022-concord/ for code and data.

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