Explaining Answers with Entailment Trees

Abstract: Our goal, in the context of open-domain textual question-answering (QA), is to explain answers by showing the line of reasoning from what is known to the answer, rather than simply showing a fragment of textual evidence (a "rationale'"). If this could be done, new opportunities for understanding and debugging the system's reasoning become possible. Our approach is to generate explanations in the form of entailment trees, namely a tree of multipremise entailment steps from facts that are known, through intermediate conclusions, to the hypothesis of interest (namely the question + answer). To train a model with this skill, we created ENTAILMENTBANK, the first dataset to contain multistep entailment trees. Given a hypothesis (question + answer), we define three increasingly difficult explanation tasks: generate a valid entailment tree given (a) all relevant sentences (b) all relevant and some irrelevant sentences, or (c) a corpus. We show that a strong LLM can partially solve these tasks, in particular when the relevant sentences are included in the input (e.g., 35% of trees for (a) are perfect), and with indications of generalization to other domains. This work is significant as it provides a new type of dataset (multistep entailments) and baselines, offering a new avenue for the community to generate richer, more systematic explanations.

• The paper introduces entailment trees as a method to systematically structure multi-step reasoning in open-domain QA.
• It presents the EntailmentBank dataset that enables training models to generate logical chains connecting premises to answers.
• Empirical results show 35% perfect tree generation for simpler cases, demonstrating promising advances in explanation quality.

Explaining Answers with Entailment Trees

The paper "Explaining Answers with Entailment Trees" presents a framework intended to advance the field of open-domain textual question-answering (QA) by offering more robust explanation mechanisms. The authors introduce "entailment trees" as a novel method for delineating the reasoning process that leads to an answer, emphasizing the systematic construction of explanations as opposed to merely displaying a fragment of text as evidence.

Overview of the Approach

The primary objective of this work is to develop a method that goes beyond the provision of isolated justifications for answers. Current methods typically offer excerpted rationale or a supporting fragment without demonstrating the logical steps that bridge the known facts and the resultant answer. To address this, the paper introduces entailment trees—a structured representation that maps multistep entailment processes involving known premises, intermediate conclusions, and the target hypothesis (i.e., question and answer).

To enable models to generate such entailment trees effectively, the paper introduces a dataset called EntailmentBank. This dataset is noteworthy as it provides a collection of multistep entailment trees that can be utilized for training QA systems. EntailmentBank is built around three tasks of increasing complexity: generating entailment trees given (a) relevant sentences, (b) relevant and non-relevant sentences, or (c) a full corpus without explicit relevance indications.

Strong Numerical Results and Experimental Findings

The paper provides empirical evidence of its claims through experiments demonstrating that LLMs can partially solve these tasks. Notably, when the relevant sentences are part of the input, about 35% of the generated trees for Task (a) are perfect, demonstrating the feasibility of the approach. The authors also show some degree of generalization beyond the domain from which the dataset is constructed, which supports the potential application of this technique across different domains.

Despite these successes, complete success remained elusive, particularly for the most complex task (c), where the model must operate over a full corpus. However, the preliminary results underline the viability of entailment trees as a framework for deeper and more systematic explanations in QA systems.

Implications and Future Directions

The implications of this research are multifaceted:

1. Practical Implications: By outlining the chain of reasoning, the approach could significantly improve debugging processes for AI systems, allowing developers and end-users to identify sources of errors.
2. Theoretical Implications: It provides an additional layer of evaluability in model interpretability and accountability. The entailment trees create a structured mechanism by which system reasoning can be inspected, representing a step towards more interpretable AI systems.
3. Future AI Development: There's potential for extending this approach towards building interactive QA systems that can not only provide answers but also engage users in meaningful dialogues about the answer's derivation.

The dataset and experimental results offer the QA community a pathway to explore richer explanation techniques, which is a crucial aspect of human-AI interaction. Future research may concentrate on enhancing retrieval accuracy for relevant facts and optimizing entailment tree generation under limited supervision or in cross-domain contexts.

The introduction of EntailmentBank and the multidimensional experimentation performed in this study provide a groundwork upon which more refined, reflective, and understandable AI reasoning processes might be constructed. This aligns with a broader movement towards AI systems that not only make decisions but also explain them comprehensively, paving the way for more transparent and trustworthy AI technologies.