Generating Interpretable Rationales for Open-Book Question Answering Using Markup-and-Mask Techniques

Core Concepts

The authors propose a new style of rationale for open-book question answering, called markup-and-mask, which combines aspects of extractive and free-text explanations. They leverage in-context learning with a pretrained language model to generate silver annotated data for training an "honest student" model that produces these rationales without explicit supervision.

Abstract

The authors present a new approach for generating interpretable rationales for open-book question answering. The key idea is to use a combination of free-text markup and masked spans, called "markup-and-mask" rationales, to provide more context-aware explanations than traditional extractive rationales. To train this system without explicit supervision, the authors leverage the in-context learning capabilities of a large pretrained language model (PaLM). They use a prompt chain to generate silver annotated data, where the model first decontextualizes the passage by adding free-text markup, then generates a chain-of-thought rationale, and finally validates the rationale. The authors then fine-tune a smaller "honest student" model on this silver data. The student model is constrained to follow a pipeline: first generate the decontextualizing markup, then select a rationale from the marked-up passage, and finally produce the answer using only the rationale. Evaluation shows that the markup-and-mask rationales produced by the student model have several favorable properties: They support accurate question answering They help human raters quickly and accurately judge the correctness of the system's answers They quantify predictive uncertainty They are more likely to entail the predicted answers than non-pipeline rationales They accurately match human-written decontextualizations The authors also find that the student models outperform their teacher on key metrics like overall accuracy, entailment rate of rationales, and accuracy of decontextualizing markup, highlighting the benefits of distillation from the pretrained language model.

Stats

The markup-and-mask rationales produced by the student model achieve 92.3% extractiveness on the QuoRef dataset and 90.6% on SQuAD. The student model's rationales yield 7.9x compression on QuoRef and 4.5x compression on SQuAD. On the QuoRef dataset, 74.2% of the student model's answers are rationalizable, with an F1 of 91.5% on this subset. On the SQuAD dataset, 86.8% of the student model's answers are rationalizable, with an F1 of 95.3% on this subset.

Quotes

"The key idea is that discourse context is made explicit in free-text markup and then rationales are extracted from the marked-up passages." "We present a new style of explanation, called markup-and-mask, which preserves the attributability of extractive rationales while overcoming the problems created by extracting propositions from the discourse in which they were written." "We show that it is possible to train models to produce markup-and-mask rationales without explicit supervision, by leveraging the capabilities of a pretrained language model."

Key Insights Distilled From

Honest Students from Untrusted Teachers: Learning an Interpretable Question-Answering Pipeline from a Pretrained Language Model

by Jacob Eisens... at arxiv.org 04-26-2024

https://arxiv.org/pdf/2210.02498.pdf

Deeper Inquiries

How could the markup-and-mask approach be extended to handle more complex reasoning patterns beyond single-sentence rationales?

The markup-and-mask approach can be extended to handle more complex reasoning patterns by incorporating multi-sentence rationales. This extension would involve generating markup that spans multiple sentences to capture the interconnectedness of information necessary for answering complex questions. By allowing the markup to include references and relationships across sentences, the rationale can provide a more comprehensive and coherent explanation for the answer. Additionally, the model can be trained to identify and mark key entities, events, or concepts that are relevant to the question across multiple sentences, enabling a more nuanced and detailed rationale. This extension would require the model to have a deeper understanding of discourse structure and the ability to track information flow across a passage to generate informative and accurate rationales for complex reasoning tasks.

What other techniques could be used to further improve the consistency and faithfulness of the rationales generated by the student model?

To further improve the consistency and faithfulness of the rationales generated by the student model, several techniques can be employed: Adversarial Training: Introducing adversarial training can help the model generate more robust and consistent rationales by exposing it to challenging examples that test its reasoning capabilities and encourage it to produce more accurate and reliable explanations. Ensemble Methods: Utilizing ensemble methods by combining multiple student models trained with different initializations or architectures can help improve the overall consistency and faithfulness of the rationales. By aggregating the outputs of multiple models, the system can reduce errors and enhance the quality of the generated explanations. Fine-tuning with Human Feedback: Incorporating human feedback during the fine-tuning process can provide valuable insights into the quality of the generated rationales. By iteratively refining the model based on human annotations and corrections, the system can learn to produce more consistent and faithful explanations over time. Attention Mechanisms: Leveraging attention mechanisms to highlight important information in the passage that contributes to the rationale can enhance the model's ability to focus on relevant details and generate more coherent and faithful explanations. By attending to key entities and relationships, the model can produce more accurate and consistent rationales.

How might the markup-and-mask framework be applied to other NLP tasks beyond open-book question answering, such as summarization or dialogue systems?

The markup-and-mask framework can be adapted and applied to various NLP tasks beyond open-book question answering, such as summarization or dialogue systems, by modifying the prompt generation and training process: Summarization: In the context of summarization, the framework can be used to generate concise and informative summaries by marking key sentences or phrases in the input text as markup and selecting the most relevant information for the summary. The model can be trained to produce markup-and-mask rationales that capture the essential content of the input document, enabling the generation of coherent and accurate summaries. Dialogue Systems: For dialogue systems, the framework can assist in generating contextually relevant responses by marking important context information in the dialogue history as markup and using it to guide the generation of the system's next utterance. By incorporating markup-and-mask rationales, the model can maintain coherence and consistency in the dialogue flow, leading to more engaging and natural conversations. Information Extraction: In tasks related to information extraction, the framework can help identify and extract key facts or entities from unstructured text by marking relevant information as markup and using it to guide the extraction process. By training the model to produce markup-and-mask rationales, it can effectively capture and extract valuable information from text data, improving the accuracy and reliability of the extraction results.