Identifying Factual Inconsistencies in Summaries: Grounding LLM Inference via Task Taxonomy (2024)

Abstract: Factual inconsistencies pose a significant hurdle for the faithful summarization by generative models. While a major direction to enhance inconsistency detection is to derive stronger Natural Language Inference (NLI) models, we propose an orthogonal aspect that underscores the importance of incorporating task-specific taxonomy into the inference. To this end, we consolidate key error types of inconsistent facts in summaries, and incorporate them to facilitate both the zero-shot and supervised paradigms of LLMs. Extensive experiments on ten datasets of five distinct domains suggest that, zero-shot LLM inference could benefit from the explicit solution space depicted by the error type taxonomy, and achieves state-of-the-art performance overall, surpassing specialized non-LLM baselines, as well as recent LLM baselines. We further distill models that fuse the taxonomy into parameters through our designed prompt completions and supervised training strategies, efficiently substituting state-of-the-art zero-shot inference with much larger LLMs.

## introduction as abstractive summarization has been advanced significantly via generative models such as bart @xcite and large language models (llms), factual inconsistencies remain one of the key concerns for ensuring high-quality faithful summaries @xcite @xcite , where certain facts from the summary are not aligned with those presented in the original document. previous works have studied extensively that employ various paradigms to reason inconsistencies, ranging from specialized bert-variants @xcite such as dae @xcite , qafacteval @xcite , to recent llms equipped with general comprehension capabilities @xcite @xcite . in particular, one outstanding direction for factual inconsistency detection is to frame it as a natural language inference (nli) problem, assessing the entailment between the document and summary @xcite . intuitively, irrelevant or inconsistent facts in the summary should reflect a low level of entailment through nli models. prior to llms, bert-based nli models have been successfully practiced by approaches such as summac @xcite to identify summary inconsistencies. in this new era of llms, several pioneering works have shown that zero-shot prompting of llms is already effective with nli-style scoring, where llms directly classify the summary consistency or provide a consistency score @xcite @xcite . while it is a promising direction to keep enhancing factual inconsistency recognition by deriving stronger nli models, such as factcc @xcite , docnli @xcite , falsesum @xcite , amrfact @xcite , in this work, we propose approaches from an orthogonal aspect, which examines the incorporation of explicit solution space into the inference, such that either zero-shot llm prompting or trained models are grounded by explicit task-specific cues, i.e. an explicit error type taxonomy. our motivation stems from the distinct nature between summary inconsistencies and nli: summaries are grounded by the original document, thus leaning towards reiteration, whereas nli tackles a broader problem that involves extrapolation. since the scope of summary inconsistency detection is roughly smaller than nli, one can consolidate and leverage its task-specific taxonomy to rationalize a more effective inference with interpretability. as there exist numerous annotation schemas adopted by previously introduced datasets, factual errors have been unified into a fine-grained taxonomy by aggrefact @xcite , which we consolidate upon and identify five common error types that are salient for recognizing summary inconsistencies, including predicate error, entity error, circumstantial error, coreference error and addition error (section 3), covering a wide variety of datasets ( for the zero-shot setting (section 4), we craft the instruction tailored for each error type in the prompt, directing llms to reason specific error types according to the given guidance. to handle long summaries, we additionally propose a window-based prompting scheme, as an effective alternative to the vanilla prompting. for a comprehensive evaluation, our experiments are conducted on 10 datasets across five domains, including summarization on different news sources, daily or professional dialogues, official reports and narrative stories. moreover, we employ models from ope-nai (chatgpt, gpt-4o) along with strong opensource llms including llama-3 @xcite and mistral @xcite towards a robust conclusion. empirical results suggest that our proposed methods surpass all baselines, including 7 non-llm baselines and 4 llm baselines, showing that zeroshot llm inference could benefit from a grounded solution space by depicting the task taxonomy in the instruction. our proposed methods, termed factuality with taxonomy (factax), achieve the best overall performance across five domains; especially, factax with chatgpt outperforms previous state-of-the-art zero-shot baseline g-eval with gpt-4 @xcite on the aggrefact-ftsota benchmark @xcite . our zero-shot factax methods could be seamlessly applied with stronger llms to harness their ongoing development; section 4.4 demonstrates that switching to the larger gpt-4o or llama3-70b can unsurprisingly boost the improvement "for free", significantly surpassing previous trained models specialized for this task. we then further seek to distill a model that fuses the task taxonomy into model parameters through supervised training. by unifying the error types of previous independently introduced datasets, we regard them jointly as training resources. llama3-8b models are trained to learn binary decisions as well as to recognize specific error types on summaries, through our designed completions and training strategies. the resulting model outperforms previous supervised baselines, and is able to match the zero-shot factax performance with chatgpt, effectively acting as an efficient alternative to zero-shot reasoning with much larger llms. overall, our key contributions in this work are: • we underscore the importance of a fine-grained task taxonomy for the inference of summary inconsistencies, leading to enhanced performance and interpretability upon vanilla reasoning. ## related work factual inconsistency evaluation datasets numerous datasets for evaluating factual inconsistencies in summaries have been independently introduced in recent years. among these, many focus on the news domain, primarily addressing cnn/dai-lymail summaries @xcite , such as factcc @xcite , frank @xcite and summeval @xcite ; others addressing xsum summaries @xcite constructed upon bbc news, such as xsum-faith @xcite and defacto @xcite ; some also addressing both, such as cliff @xcite and @xcite . apart from news, several datasets focus on dialogue summaries, especially daily dialogues from samsum @xcite , such as diasum-meval @xcite , facteval @xcite , diasummfactcorr @xcite . dia-summfact @xcite also assesses meeting summaries from qmsum @xcite . recent datasets have also been proposed to address more domains, e.g. @xcite evaluates factual consistency on official reports from govreport @xcite ; longeval (krishna et al., 2023) addresses story summaries from squality @xcite . in this work, we aim for robust evaluation across diverse domains under the same task requirement, especially for zero-shot methods that should generalize across different types of documents. ## task taxonomy establishing what constitutes inconsistent facts in a summary is a fundamental aspect of this task. in this work, we target to consolidate key error types that are salient for inconsistent fact detection in general, instead of building fine-grained complex taxonomy, for two reasons. first, a simple taxonomy is easier to be consumed by models than a complex one, aligning with our goal of practical utilization during inference. second, a more finegrained taxonomy may be of greater noises, as the annotated types from different datasets can vary significantly in their standards. based on the annotation schemas of previously introduced datasets and the aggregation of factual errors by aggrefact @xcite , we identify the following five salient error types: • predicate error: the semantics expressed by a predicate in the summary are not consistent with those in the source document. ## approach: zero-shot paradigm to incorporate the error type taxonomy, we first propose zero-shot prompting methods that leverage the general comprehension capability of llms, aiming to depict the explicit solution space to facilitate the zero-shot inference. as such, the proposed methods could harness the promising development of llms for this task, tying to greater potentials when switching to more capable llms. ## approach: supervised paradigm as section 4 has demonstrated the strengths of grounding zero-shot llm inference by its task taxonomy, we further seek to distill a model that absorbs the taxonomy into llm parameters through supervised training. two advantages could come with such distillation. first, by learning the taxonomy from examples, the model gains real-world distribution of each error type, rather than relying on shallow comprehension through prompt instructions. second, it is more efficient and practical compared to zero-shot methods, avoiding the need for large model sizes and lengthy generation due to the cot reasoning. with above motivations, we utilize previous datasets that were proposed independently, and regard them jointly as training resources. to this end, we prepare our training and test set as follows: training set ii includes two publicly released large-scale datasets constructed via synthetic data generation. the more recent amrfact is excluded, since its data has not been released as of this writing. for efficiency, training set ii retains randomly sampled 50k examples from docnli and falsesum respectively that do not overlap with any source documents in the test set. the resulting training resources thereby have 16k examples in training set i, 100k examples in training set ii, and 1k examples in test set. ## conclusion we highlight the importance of task-specific taxonomy for factual inconsistency detection, where we consolidate salient error types, and incorporate them to facilitate llm inference with both zero-shot and supervised paradigms. extensive experiments on ten datasets of five domains demonstrate the efficacy of depicting task taxonomy to ground the zero-shot inference, achieving state-ofthe-art performance compared with respective baselines. we further distill models that fuse the given error taxonomy into parameters through our designed training completions and strategies, effectively serving as an efficient alternative to state-ofthe-art zero-shot reasoning by much larger llms.