Leveraging Structured Information for Explainable Multi-hop Question Answering and Reasoning (2023)

Abstract: Neural models, including large language models (LLMs), achieve superior performance on multi-hop question-answering. To elicit reasoning capabilities from LLMs, recent works propose using the chain-of-thought (CoT) mechanism to generate both the reasoning chain and the answer, which enhances the model’s capabilities in conducting multi-hop reasoning. However, several challenges still remain: such as struggling with inaccurate reasoning, hallucinations, and lack of interpretability. On the other hand, information extraction (IE) identifies entities, relations, and events grounded to the text. The extracted structured information can be easily interpreted by humans and machines (Grishman, 2019). In this work, we investigate constructing and leveraging extracted semantic structures (graphs) for multi-hop question answering, especially the reasoning process. Empirical results and human evaluations show that our framework: generates more faithful reasoning chains and substantially improves the QA performance on two benchmark datasets. Moreover, the extracted structures themselves naturally provide grounded explanations that are preferred by humans, as compared to the generated reasoning chains and saliency-based explanations.

## introduction multi-hop question answering (qa) involves answering questions that require reasoning over multiple pieces of information, often spread across different parts of a document or multiple documents. it looks like "hopping" over various facts to arrive at a conclusion or answer. in multi-hop question answering, the system needs to understand the context, maintain the sequence of information, and utilize this combined information to generate a correct and comprehensive answer. traditionally, researchers @xcite @xcite have applied graph neural networks (gnn) to this task. in recent years, with the growing capabilities of large language models (llms), several works propose using prompting to address this task in a few-or zero-shot way @xcite . llms have achieved superior performance in reasoning-based question-answering tasks. these models have the potential to "reason" and connect multiple pieces of information to generate the answers. however, they still struggle with complex multi-hop questions; and the end-to-end generation nature makes their answering process not explainable. to elicit the reasoning capabilities of llms, recent research introduced the chain-of-thought (cot) mechanism and its variants @xcite . the cot mechanism enables models to not only generates better answers but the reasoning chain (process) in natural language, improving the llm's performance in conducting multi-hop reasoning. despite the progress made, several challenges persist under this paradigm, one is the neural models' limitations in tackling compositional problems that require strict multi-hop reasoning to derive correct answers @xcite . cot-based methods still conduct generations in an end-to-end way, which is not a strict "symbolic derivation" and often causes inaccurate reasoning (e.g., generating answers that don't exist or two-hops away). instead, we propose a multi-step approach (figure 1 ) to tackle this problem: firstly, constructing the semantic graph structures (sg) with information extraction (ie) and then leveraging this symbolic information (including entities and semantic relations) for strictly guiding the model's reasoning process. the second challenge is that, although the current model-generated reasoning chain provides explanations, they are only surface-level interpretations, and there is no guarantee that they are fully a: anne was the daughter of jean. jean was the youngest child of robert. robert was the greatgrandson of louis. the document does not say who taught louis. so the answer is: unknown. grounded to context @xcite . for example, the model could make up a reasonablesounding explanation that is not faithfulness for its decision-making (hallucinations). while our extracted graphs from the ie step naturally provide explanations grounded to the given context (figure 4 ), which are overwhelmingly preferred by human evaluators, compared to saliency-and nlgbased explanations. plus, we also find that by leveraging our extracted sgs, the large language model also generates more faithful reasoning chains (section 4.3), to sum up, we make the following contributions in this work: (1) we propose creating and utilizing semantic graphs to enhance models' capability in multi-hop reasoning and achieve better performance on the task; (2) we demonstrate that leveraging semantic graphs in the prompting process help models generate higher-quality and more faithful reasoning chains; (3) we show that semantic graphs-based explanations are grounded to context and help improve the interpretability of the answer-predicting process, which human evaluators prefer. in addition, under our prompting-based qa setting, we sequentialize the graphs by concatenating the triples, which simplifies the process of encoding the semantic graphs. specifically, our approach does not require the fine-tuning process since we apply the prompt-based method over llms, which simplifies the reasoning process. this generative format is closely related and concurrent to the prompting-based method that is recently applied to the multi-hop qa task @xcite ; @xcite ). we introduce the details and their variance in the paragraph below. prompting and reasoning the chain-of-thought (cot) prompting strategy @xcite , as a variation of llm few-shot prompting, significantly improves the reasoning ability of llms. compared to traditional few-shot prompting, it turns complex implicit reasoning chains into explicit long natural language text and adds it to the demonstrations, which helps llms conduct better reasoning. @xcite introduce zero-shot-cot by using a simpler prompt ("let's think step-by-step") to instruct llms. @xcite introduce the self-consistency strategy to improve the performance of the cot strategy. when applying prompting strategy to the multihop qa task, @xcite introduces an iterative information retrieval method to provide more relevant context. @xcite decomposes prompts to simple solvable tasks. in our approach, we leverage semantic graphs extracted from documents, which guide reasoning processes and help accurately find the answer node. those graphs are extracted from context, sequentialized into text form, and added back into context. generating faithful reasoning chains is difficult with the vanilla cot strategy, as generated natural language contents may contain irrelevant information and cause hallucinations. @xcite try to solve it by translating natural languages to symbolic languages and deducing results by deterministic external solvers. but, the process of translation itself remains opaque and cannot guarantee the prevention of hallucinations. instead of directly generating symbolic presentations from contexts, we introduce semantic graphs to prevent hallucinations and improve interpretability. since graphs are extracted from contexts, they are strictly grounded to context. edges in the graphs can be seen as potential reasoning steps in the whole reasoning process. ## methodology in this section, we first give a formal introduction to the multi-hop qa task and two different settings we tackle the problem. we then describe our technique for extracting semantic graphs, which will be used in our framework. third, we introduce our overall and specific prompting for generating the reasoning chains and final answers. ## further qualitative analysis error analysis we further investigate errors made by our sg-based framework. there are four major categories: (1) lack of external or commonsense knowledge (e.g. one's uncle is the brother of father), most of the wrong answers are caused by this. (2) poor quality graph (limitation of input length); (3) other problems such as metaphors/paraphrasing (e.g. relations between "rests" and "death") and mathematical knowledge. (4) unanswerable questions based on the given context, which is a problem of dataset creation. around 5% of questions have this problem. more about explanabiltiy/interpretability our extracted semantic graph naturally provides an explanation of the reasoning process. current work mainly relies on the generated reasoning chain, which is nlg-based and contains hallucinations @xcite . our sg is fully grounded to the input context, as shown in the upper part of figure 4 . the red parts are the extracted nodes, and the green parts denote the relations/edges. apart from comparing to nlgbased (reasoning chains in our setting) explanations @xcite . we also compare to the prevalent saliency-based method, which obtains explanations that are also grounded to the context (bottom of figurer 4). it's mainly done by using attention scores to highlight the most important spans/words in the context @xcite . we conduct rigorous human preference studies. we invite a group of over four volunteers (with at least bachelor's degrees) to make pairwise comparisons of explanations in a blind way. they are only provided with ten questions, the corresponding contexts, and the highlighted words in the context (that models deem as most important for answering questions). volunteers overwhelmingly vote for our sg-based extractive graphs/explanations as most grounded and informative. beyonds, results also show a large preference for grounded explanations as compared to the reasoning chains, mainly because they have to verify the factuality/faithfulness of the generated explanations. ## conclusion we investigate how extracted semantic graphs can contribute to explainable multi-hop question answering. we propose a prompting-based method that leverages the extracted and sequentialized graph into a prompt context. through comprehensive experiments from multiple aspects, we find that leveraging the sgs substantially improves llms' reasoning capabilities in answering multiple-hop questions -with more faithful reasoning chains and better accuracy scores. meanwhile, human studies show that the extracted graph, which itself is an interpretation that is strictly grounded to context, is preferred. as compared to the nlgbased explanations such as the generated reasoning chain and saliency-based explanations.