Evidence-Focused Fact Summarization for Knowledge-Augmented Zero-Shot Question Answering (2024)

Abstract: Recent studies have investigated utilizing Knowledge Graphs (KGs) to enhance Quesetion Answering (QA) performance of Large Language Models (LLMs), yet structured KG verbalization remains challenging. Existing methods, like concatenation or free-form textual conversion of triples, have limitations, including duplicated entities or relations, reduced evidence density, and failure to highlight crucial evidence. To address these issues, we propose EFSum, an Evidence-focused Fact Summarization framework for enhanced QA with knowledge-augmented LLMs. We optimize an LLM as a fact summarizer through distillation and preference alignment. Our extensive expeirments show that EFSum improves LLM’s zero-shot QA performance with its helpful and faithful summaries, especially when noisy facts are retrieved.

## introduction large language models (llms) have shown remarkable zero-shot abilities but often produce factual errors, known as hallucinations, particularly in knowledge-intensive tasks like question answering (qa). this happens because the static knowledge within llm parameters may be incomplete, incorrect, or outdated, failing to keep pace with evolving real-world knowledge. recent studies remedy this by integrating external knowledge into llms @xcite . as one form of external knowledge, knowledge graphs (kgs) have been considered as the knowledge source to augment llms for enhanced performance in knowledge graph qa (kgqa) @xcite @xcite . the key challenge of utilizing kgs, which consist of a set of (head entity, relation, tail entity) triples, is to bridge the modality gap between graphs and text. efforts on connecting the gap mostly fall into either training additional layers to blend two representations, or verbalizing graphs into texts. while training fusion layers @xcite ) can make models expressive on two different modalities, it takes expensive computaions, and needs to be trained when kgs are updated. on the other hand, recent studies proposed verbalizing kgs into text-form, without training llms for qa. for instance, one strategy is simply concatenating the facts in their triple-form text @xcite , and another entails converting them into semantically-coherent textual description (i.e., freeform text) through the distillation of llm's ability to generate text from kgs @xcite . despite their remarkable efficacy, existing verbalization strategies for providing contextual knowledge exhibit critical limitations. (1) low density of evidence: both concatenation and linearization of the facts @xcite are highly likely to include duplicated entities or relations due to limited flexibility; this eventually degrades the density of useful evidence within the contextual knowledge, hindering the ability to answer questions effectively. (2) low clarity of evidence: while contextual knowledge describes factual information, they often fail to highlight the evidence necessary for answering ques-tions. this lack of focus can lead to noise from irrelevant facts, which can detrimentally impact the llm's ability to provide accurate answers. to address the aforementioned challenges, we introduce a novel efsum framework, evidencefocused fact summarization for enhanced qa with knowledge-augmented llm prompting. the key idea is transforming the set of facts into plausible and coherent summary while highlighting evidence and filtering out noise given a question; this ensures that the summaries maintain a high density and clarity of evidence, facilitating effective qa (figure 1 ). the most straightforward solution to this summarization is prompting llms with the detailed instruction. however, llm-generated summaries often omit crucial evidence (such as answer spans), resulting in information loss, or include information that cannot be inferred from retrieved facts, leading to extrinsic hallucination. for enhanced summary quality, we optimize an open-source llm as a fact summarizer in two steps: llm distillation and preference alignment (figure 3 ). during the first step, we train our fact summarizer by using the reference summaries obtained through llm prompting. subsequently, in the second step, we refine our summarizer to better align with the task-specific preference related to qa. to this end, we introduce two preference criteria for the summary candidates: helpfulness evaluates llms can correctly answer the question based on the summary, and faithfulness assesses the factual consistency of the summary in relation to the provided set of facts. by selecting pairs of preferred and dispreferred summaries based on these criteria, we further fine-tune the summarizer through direct preference optimization (dpo) @xcite . in the end, efsum is capable of generating summaries that are both helpful for qa and faithful to the given facts. extensive experiments on two qa benchmark datasets validate the effectiveness of our evidencefocused fact summarization in improving llm's zero-shot qa performance. efsum outperforms other fact verbalization methods in two key settings: (1) when fixing the token length (density, section 4.2), and (2) when fixing the number of triples (clarity, section 4.3) within contextual knowledge. additionally, our approach enhances the helpfulness and faithfulness of the generated fact summaries. for reproducibility, our codes are publicly available at https://github.com/kk13332488/efsum . ## preliminaries in this section, we introduce a kg-augmented zeroshot qa pipeline, and provide analyses on the verbalized facts obtained by existing methods. ## efsum: proposed method in this section, we present an evidence-focused fact summarization framework, named efsum, which aims to effectively augment the set of relevant facts to llms for zero-shot qa tasks. ## experiments in this section, we design our experiments to answer the following research questions: most question-answer pairs can only be solved by utilizing multi-hop reasoning or the attributes of multiple entities. we use a test set of mintaka which has 4,000 examples for evaluation. llms for zero-shot qa. to measure the efficacy of efsum and other fact verbalization methods, we utilized three different llms, gpt-3.5-turbo, flan-t5-xl (chung et al., 2022), and llama2-7b-chat @xcite , for our zero-shot qa evaluation. note that flan-t5-xl and llama2-7b-chat are publicly available as open-source. we provide more details on the models in appendix a.1. baseline methods. as the main baselines for fact verbalization, we consider various approaches. ## conclusion in this paper, we explore methods to enhance the zero-shot qa performance of llms by augmenting knowledge from kgs. we introduce a novel summarization framework, called efsum, which transforms a set of facts into summary with high density and clarity of evidence for answering the question. to achieve this, we optimize an open-source llm as a fact summarizer, leveraging a teacher llm's summarization capabilities and aligning its outputs with qa-specific preferences. our experiments show that efsum significantly improve qa accuracy across various llms compared to other fact verbalization approaches. furthermore, serving as an independent summarization module, it generates helpful and faithful summaries based on relevant facts and target questions.