Enhancing Systematic Decompositional Natural Language Inference Using Informal Logic (2024)

Abstract: Recent language models enable new opportunities for structured reasoning with text, such as the construction of intuitive, proof-like textual entailment trees without relying on brittle formal logic. However, progress in this direction has been hampered by a long-standing lack of a clear protocol for determining what _valid decompositional entailment_ is. This absence causes noisy datasets and limited performance gains by modern neuro-symbolic entailment engines. To address these problems, we formulate a consistent and theoretically grounded approach to annotating decompositional entailment and evaluate its impact on LLM-based textual inference. We find that our new dataset, RDTE (Recognizing Decompositional Textual Entailment), has a substantially higher internal consistency than prior decompositional entailment datasets, suggesting that RDTE is a significant step forward in the long-standing problem of forming a clear protocol for discerning entailment. We also find that training an RDTE-oriented entailment classifier via knowledge distillation and employing it in an entailment tree reasoning engine significantly improves both accuracy and proof quality, illustrating the practical benefit of this advance for textual inference.

## introduction textual inference using entailment trees has emerged as a promising avenue for explainable ai in domains requiring complex reasoning @xcite . inspired by classical symbolic reasoners that produce proof trees of a hypothesis, recent entailment tree systems (e.g. nellie @xcite or irgr @xcite ) explain a hypothesis by constructing, via an lm-based generative search algorithm, a tree of recursive natural language (nl) decompositions whose children entail their parents. entailment trees provide a natural and granular medium for explaining complex multi-step decisions to users and relevant? not redundant? acceptable? sufficient? figure 1: (upper) two hypothesis decompositions suggested by an llm. the first makes an argument that is generally acceptable to a human. the second contains a fact that is not always true and another that is irrelevant to the entailment. recognizing such an invalid decomposition is core to recent neuro-symbolic reasoning algorithms, but llms struggle at the task. (lower) ambiguous definitions of entailment have hampered progress in annotating data to improve the models. we find that a faceted definition yields both a clean dataset (rdte) and significant downstream task improvements. work on entailment trees has largely avoided this critical question of reasoning validity. existing evaluations focus on tree reconstruction @xcite and qa performance, avoiding considerations of whether the quality of model-generated trees aligns with the accuracy of the answers. this is largely because reasoning quality is difficult to assess. evidence suggests humans think of deductive validity less stringently than formal symbolic axioms; they accept explanations that are more or less incomplete @xcite . attempts to annotate datasets @xcite consequently suffer from inconsistent labels, as opinions differ between annotators over what constitutes validity. without careful instruction, it is hard for annotators to determine where to set their threshold for acceptability on the spectrum from "strict deductive inferences" to "fallible common-sense inferences" @xcite . thus, asking for a single binary decompositional entailment judgment yields a difficult and noisy annotation task (figure 1 , (a) ). this leads us to pursue a more consistent protocol for measuring the goodness of one decomposition over another. the existing discourse surrounding nli (e.g., @xcite ) has not addressed the sort of complex decompositional textual entailments encountered in a recursive tree search algorithm for current qa datasets. towards addressing this gap, we propose an evaluation centered on the notion that a valid decomposition is a valid argument for why the hypothesis should be believed. we take inspiration from the "relevance, acceptability, and sufficiency" criteria for a logically good argument developed in the field of informal logic @xcite , and design an approach to assessing entailment in a principled manner. we annotate items on ordinal scales targeting different facets of argument analysis, yielding a well-defined and consistent process. we use this protocol to collect over 1000 expert annotations of decompositions generated during entailment tree searches. through experiments, we find that models trained on previous compositional entailment datasets and llms like gpt fall short of human performance on the dataset, which we term rdte @xcite . we also find that our annotation protocol can serve as a powerful lm prompting mechanism: given the same instructions as our human annotators, @xcite can serve as a "teacher" in a knowledge distillation pipeline that annotates reasoning traces extracted from a tree search algorithm. for this purpose, we released a large artifact (24k items per domain) of gpt-4's annotations. we illustrate the effectiveness of student models trained via this pipeline as the linchpin of tree-wise, a novel entailment tree engine that performs hypothesis proving via decompositional entailment search. inspired by the recent nellie engine @xcite , treewise leverages in-context learning, forward chaining, branch consolidation, and most importantly, improved decompositional entailment recognition. it surpasses tree-producing approaches on established benchmarks like entail-mentbankqa but also adapts to complex tasks such as hotpotqa that require reasoning over less structured knowledge sources like wikipedia. using the knowledge-distilled models as reasoning verifiers improves treewise's qa accuracy and raises the quality of the trees it produces. our contributions are thus: ## data collection we seek to construct a dataset of decompositional entailment judgments that a reasoning system might encounter while performing qa. following @xcite , we found it apt to annotate decompositions generated during a model's reasoning search traces over training questions. we use the backward chaining process of the tree search algorithm treewise, which is introduced in §6. each item is a hypothesis and a set of 2-3 premises that the model proposes might conjunctively entail the hypothesis in the context of a given question. to collect a representative sample of hypotheses, we pull from three classes: we collect a mixture of gpt-4 and chatgptgenerated decompositions using a suite of different styles of prompts described in §a.2. we generated decompositions for hypotheses foot_3 in two task domains: multiple choice science qa in arc @xcite and multi-hop qa over wikipedia in hotpotqa @xcite . while hotpot questions are constructed over public world knowledge, the esoteric individual facts (e.g. the birth year of some actor) are not common knowledge among humans, and it would be a stretch for the audience of an argument made by a hotpot decomposition to be expected to know each premise's factuality. we thus do not annotate hotpot decompositions for factuality, but annotate the other facets as normal. ## rdte evaluation we elicit sufficiency judgments over the 1000 goldannotated rdte items using a series of methods based on the rdte protocol and existing methods for judging decompositional entailment. we test them on the gold sufficiency scores converted to binary labels by assigning 4 and 5s to positive entailment. factuality, relevance, and redundancy predictions were not directly evaluated. ## treewise to illustrate the impact that an rdte-based entailment model can have on systematic reason10 ## treewise experiments the distillation framework suggests a way forward for improving entailment engines in new domains: first extract non-optimized reasoning traces from the engine, annotate them under the rdte protocol with gpt-4 (which is itself too slow and expensive to use in the engine), train student models on the silver data, and then substitute the student as an entailment classifier in the engine. the experiments below emulate this scenario and show that the resulting engines improve on both qa and on generated tree quality, highlighting the effectiveness of applying rdte-trained distillation student models to entailment tree creation algorithms. entailment tree qa task definition we pursue a version of evidence-grounded, explainable qa in which models are right for the right reasons. given corpus c, multiple-choice question q and hypotheses h 1 , . . . , h m corresponding to answer options a 1 , . . . , a m , we task models to predict correct answer a * i and generate entailment tree t h i rooted at h i such that (1) the t h i 's leaves are in c ∪ q and (2) the tree's steps explain how each parent follows compositionally from its children. ## conclusion the trustworthy application of llms to complex reasoning tasks critically depends not only on accurate responses, but also accurate justifications. to date, research in entailment tree reasoning has largely focused on measuring the former. this work develops a protocol based on works in informal logic for the assessment of compositional entailment, the core reasoning task undergirding entailment trees. we employed this rubric-based protocol to annotate rdte, a high-quality dataset of judged compositional entailments, along with tens of thousands of automatically scored items by gpt-4 under the same instructions in multiple domains. we demonstrated that rdte supports state-of-the-art results with a novel system for evidence-grounded entailment tree generation. this pipeline, which involves knowledge distillation of student models using careful entailment prompting, can be readily applied to new task domains to make entailment-based reasoners more precise task-specific problem solvers. the combination of our rubric, manual annotations, gpt-4 derived data, and this new system that we call treewise represents a significant advance in building trustworthy ai systems capable of not only solving complex reasoning tasks but providing correct justifications along with their answers.