Enhancing Post-Hoc Attributions in Long Document Comprehension via Coarse Grained Answer Decomposition (2024)

Abstract: Accurately attributing answer text to its source document is crucial for developing a reliable question-answering system. However, attribution for long documents remains largely unexplored. Post-hoc attribution systems are designed to map answer text back to the source document, yet the granularity of this mapping has not been addressed. Furthermore, a critical question arises: What exactly should be attributed? This involves identifying the specific information units within an answer that require grounding. In this paper, we propose and investigate a novel approach to the factual decomposition of generated answers for attribution, employing template-based in-context learning. To accomplish this, we utilize the question and integrate negative sampling during few-shot in-context learning for decomposition. This approach enhances the semantic understanding of both abstractive and extractive answers. We examine the impact of answer decomposition by providing a thorough examination of various attribution approaches, ranging from retrieval-based techniques to LLM-based attributors.

## introduction the rise of large language models (llms) and genai-based technologies has greatly increased their usability across various sectors, notably in grounded question-answering systems. however, to establish trust, it's crucial to attribute information obtained from source documents, especially given the tendency of these models to generate texts from their own knowledge bases @xcite . with opaque llms like chatgpt, there's a need to explore post-hoc attribution methods to enhance reliability. targeted attribution is necessary in chat-based question-answering systems to improve user experience, as not every part of an answer requires attribution. prior research addresses generating attributions alongside responses in open domains @xcite , figure 1 : an example from verifiability dataset. the input to the post-hoc attribution system is the question, document and answer. the output is evidence sentences from the document. text marked in red do not require attribution. with rise of llms, the attribution task is viewed as a retrieval and mapping task @xcite . this shift presents challenges, especially in abstractive question-answering scenarios, where retrieving context is not straightforward, and semantic relatedness must often be inferred from the entire document context. notably, @xcite has underscored the limitations of retriever-based attribution engines, particularly in handling out-ofdistribution knowledge during context processing. despite these efforts, existing methods treat an-swers as singular attributable elements and strive to map them back to long sequence contexts. however, answers can encompass multiple facts and are contextually dependent on the posed question. existing fine-tuning or retriever-based systems face challenges in identifying the specific attributable components within an answer. in this paper, we formulate post-hoc attribution as the task of identifying source sentences from a document that support attributable parts of an answer when our input consists of a question, answer, and the document from which the answer is obtained. firstly, we propose using template-based incontext learning as a method to achieve questioncontextualized decomposition of answers. this technique helps break down answers that aligns with the question, which aids identifying references in the document context. this alignment is crucial as it enhances the identification of the specific information units necessary to answer the question effectively. empirical observations suggest that guiding a language model for specific tasks in fewshot settings can lead to improved adaptability @xcite . building upon this insight, we recognize the significance of guiding a language model for in-context decomposition. additionally, we introduce negative sampling-based in-context learning to enable language models to discern between good and bad decomposition. secondly, we analyze the use of different retrieval methods as attributors and compare their performance using four decomposition methods. we observe that, on average, question-contextualized coarse-grain decomposition results are comparable, and in some settings, better than using nondecomposed sentences. this serves as a regression test for our methodology without degrading retriever performance. moreover, our experimental results on the citation verifiability dataset highlight that contextualized coarse-grain decomposition for retriever-based attributors (bm25, gtr, monot5) achieves, on average, a 3% gain in precision over baseline models, emphasizing the efficacy of decomposition for attributions ( thirdly, we examine the use of llms as posthoc attributors and the advantages they offer by considering the context of the question and the decomposed answer. we observe a significant improvement in performance both empirically and qualitatively when providing question-contextualized coarse-grain decomposition of answers to llms. by leveraging llms as attributors and incorporating question-contextualized coarse-grain decomposition, we achieve state-of-the-art performance on the qasper and verifiability datasets ( ## related work multiple works have established the need for ensuring a trustworthy model and how citations and attributions can help ensure that reliable information is provided or to detect if any information is hallucinated @xcite @xcite . efforts to create truthful ai have been highlighted, emphasizing the importance of fact-checking and claim verification, even in the pre-llm era @xcite . notably, @xcite focuses on improving the identification of claims from wikipedia that lack support from citations, with a specific emphasis on identifying supporting evidence at a paragraph level. in addition to unimodal systems, multimodal systems, with the introduction of generative ai, are becoming increasingly important to attribute contextual parts and build trustworthy ai systems @xcite @xcite . in the context of dialogue systems requiring background knowledge, issues with spurious correlations have been acknowledged, necessitating a more robust method for identifying what can be attributed in an abstractive setting @xcite . @xcite introduces the identification of missing information and providing appropriate attributions to mitigate potential dangers due to misinformation. meanwhile, @xcite delve into the analysis of feature attribution and interpretability of language models. a unique task is undertaken by @xcite , which addresses the creation of inline citations for scientific articles from abstracts of other scientific articles. @xcite introduced fine-grained attribution of answers but did not cover factbased attribution. the task of fact-based attribution-assigning facts from answers to a list of evidence sentences rather than paragraphs-is underexplored in the existing literature. this underscores the need for further exploration in fact-based attribution, particularly concerning sentence-level source attributions and the challenges posed by long document sequences in question-answering systems. ## method we propose a 2-fold process: first, decompose the answer into smaller information units, then employ an attributor to map these units to evidence from the source document (fig. 2 ). we investigate using retrieval methods and various llms for attribution. ## datasets we reformulate the citation verifiability dataset @xcite and qasper dataset @xcite for our task. the statistics of the dataset are present in ## results and discussion in we notice comparable results over the qasper dataset. this dataset majorly contains extractive and short sentences. the average length of extractive answers in the dataset is 14.4 and that of abstractive answers in 15.6 words. due to the short and highly extractive nature of the dataset, it is likely that using answer sentences as information unit suffices. interestingly, we observe that on qasper, gtr and monot5 have comparable performance while using cog and single answer sentence as an information unit. whereas, in bm25 there is a slight reduction in scores. this is likely due to the way the answer is decomposed or augmented when the question is taken into context. this impacts retrieval methods like bm25 where the frequency of words are taken into account to calculate the score. this motivates the use of embedding based retrievers as attributors over term frequency based retrievers. in question answer gt attribution "paint cast iron" "to paint cast iron, you should first coat it with oil-based primer to create a smooth surface and help the paint adhere. you can find cast iron paint on amazon." cog decompositions nil-monot5 cog-monot5 nil-gpt4 cog-gpt4 ["coat the cast iron with oilbased primer.', "priming the metal creates a smooth surface and will help the paint adhere."] ["to paint cast iron, you should first coat it with oil-based primer.", "the oil-based primer helps create a smooth surface and help the paint adhere."] ["if you're working with a smaller piece of cast iron, you can wipe it down with a damp rag, instead.", "apply oil-based paint to the cast iron."] ["coat the cast iron with oilbased primer.", "if you're working with a smaller piece of cast iron, you can wipe it down with a damp rag, instead."] ["priming the metal creates a smooth surface and will help the paint adhere.", "apply oil-based paint to the cast iron.", "coat the cast iron with oil -based primer."] ["coat the cast iron with oil-based primer.", "priming the metal creates a smooth surface and will help the paint adhere."] [] ["you can find cast iron paint on amazon."] ["read on for our complete guide to painting cast iron easily at home."] ["read on for our complete guide to painting cast iron easily at home."] [] [] we observe that identifying relevant information helps monot5 make better attributions compared to using answer sentences as queries. retrievalbased attributors rely on query and evidence scores, which can lead to irrelevant matches, especially since finding an appropriate threshold is challenging and dataset-specific. unlike retrieval methods, llms consider question context, resulting in more accurate attributions. however, it is interesting to observe that when the information unit is given as a complete sentence, llm struggles to find the precise set of evidences. examples for these observations are present in ## conclusion in this paper, we raise research questions regarding the consideration of post-hoc attributions as a grounding task rather than a naive mapping task. on this basis, we propose and explore a novel approach to the factual decomposition of generated answers for attribution, employing templatebased instructional tuning. we empirically establish the fact that our proposed granular-level coarse grained decomposition (cog) helps identify the spans of answers that need decomposition by following the semantic context inferred from the question asked. we also qualitatively and empirically establish that using language models (llms) as attributors provides the breathing space to consider attribution as contextual semantic grounding rather than performing as a retrieval and mapping task. through various ablation studies, we establish that on an extractive dataset, retriever-based algorithms can perform better by incorporating our coarse we present promising results in enhancing posthoc attributions for comprehending lengthy documents. adopting a post-hoc perspective provides research opportunities when dealing with outputs from opaque models. currently, our focus is solely on post-hoc attributions through a unimodal lens, involving textual input and textual output. the transition to multimodal attributions would present additional challenges. an intriguing avenue for research involves exploring attributions from tables, charts, and images and performing reasoning over them. in our post-hoc approach, we identify only those parts with supporting evidence but do not address how to mitigate unsupported claims. this creates opportunities to explore the incorporation of feedback loops for unattributable answer parts, aiming to generate more reliable answers.