Knowledge Infused Decoding

1 INTRODUCTION . Pre-trained language models ( LMs ) have been shown to capture rich semantic and syntactic features , as demonstrated by the state-of-the-art performance on many generation tasks such as abstractive summarization ( Zhang et al. , 2020b ; Chu & Liu , 2019 ) and dialogue generation ( Roller et al. , 2021 ; Zhang et al. , 2020c ) . Their generations , however , can be quite limited in scenarios requiring knowledge—they frequently struggle with issues such as being easily misguided by phrase cooccurrence ( e.g. , “ Talk ? Birds can talk. ” ) , failing to handle negation ( e.g. , “ The theory of relativity was not developed by Einstein. ” ) ( Kassner & Schütze , 2020 ) , and being unable to compare commonsense concepts , such as time ( Qin et al. , 2021 ) and digits ( Talmor et al. , 2020 ) . To enhance the performance of LMs on knowledge-intensive NLG tasks1 , prior studies have proposed to re-train LMs with knowledge-aware objectives ( Zhou et al. , 2020 ; Xiong et al. , 2020 ; Zhang et al. , 2019 ; Khandelwal et al. , 2020 ) or add special architectures to encode knowledge ( Bosselut et al. , 2019 ; Logan et al. , 2019 ; Peters et al. , 2019b ) from external resources ( e.g. , knowledge graphs such as CONCEPTNET ( Speer et al. , 2017 ) and ATOMIC ( Sap et al. , 2019 ) ) . These methods , though yielding impressive results on many downstream tasks , can be computationally expensive . More importantly , knowledge implicitly parameterized in LM architectures is difficult to revise and expand ( Lewis et al. , 2020b ) , and wrong generations are hard to diagnose due to lack of interpretation ( Talmor et al. , 2020 ) , which heavily limits their real-world applications . More recent retrieval-based models try to tackle these problems by augmenting inputs with retrieved knowledge evidence ( Lee et al. , 2019 ; Guu et al. , 2020 ) . For example , RAG ( Lewis et al. , 2020b ) leverages non-parametric memory to access extensive knowledge ( in the form of unstructured documents ) , 1We define knowledge-intensive NLG tasks as those whose input context alone does not provide complete knowledge for a legitimate and plausible generation . and jointly fine-tunes a parametric LM ( i.e. , BART ( Lewis et al. , 2020a ) ) to enable knowledge-aware generation . A key limitation of such methods is that they retrieve documents only once while grounding them in the input static context , and thus can not support the dynamic nature of the context as new tokens are generated . The static knowledge becomes a major problem for tasks where longer and abstractive generation is expected , such as open-ended story generation ( Mostafazadeh et al. , 2016 ) , multi-turn dialogues ( Zhao et al. , 2020 ) , and conversation summarization ( Gliwa et al. , 2019 ) . Moreover , in a recent study , Krishna et al . ( 2021 ) replaced the knowledge retriever in RAG with a random retriever and found little difference in the resulting performance on a long-form QA task named ELI5 ( Fan et al. , 2019b ) , indicating the model may not be actually grounding its text generation to the retrieved documents . To address these limitations , in this work , we present a novel decoding algorithm KID , aiming to better infuse knowledge into generation in a dynamic manner . Instead of solely relying on the static knowledge retrieved at beginning , during each step of LM decoding , KID dynamically searches promising continuation from retrieved knowledge , to guide the current step generation . Specifically , KID maintains a local knowledge memory , interacts it with a knowledge trie dynamically created from retrieved supporting documents , and updates the local memory as a knowledge-aware constraint to guide the generation . The key intuition behind KID is that existing LM pre-training objectives are usually defined at the token level yet do not explicitly model concept-centric knowledge ( Xiong et al. , 2020 ) — thus motivating us to reshape the probability mass at each step decoding towards the distribution of entities in knowledge . The contributions of this work are three-fold : First , we introduce KID as a model and task agnostic decoding method that integrates knowledge on the fly and can be applied to various knowledgeintensive tasks with different generative LMs . Second , from a docoding perspective , on six knowledgeintensive NLG tasks , GPT2 ( Radford et al. , 2019 ) and BART ( Lewis et al. , 2020a ) equipped with KID significantly outperform conventional beam search or sampling decoding by a large margin . Third , from a knowledge infusion perspective , unlike six strong knowledge-infusion baselines which require either additional retraining or special architecture modifications , KID leverages knowledge more effectively as a light-weight knowledge infusion solution . Additionally , in few-shot scenarios KID significantly improves over them , demonstrating its generalization ability in low-resource and domain shifting regimes . 2 RELATED WORK . We briefly review existing work enhancing LMs with external knowledge and representative decoding algorithms for generation . Enhancing Language Model with Knowledge . Large language models implicitly encode knowledge in their parameters but with limits ( Petroni et al. , 2019 ; Kassner & Schütze , 2020 ; Lin et al. , 2020a ) . Several architectures and objective functions have been proposed to explicitly encode external knowledge ( Sun et al. , 2020 ; Logan et al. , 2019 ; Roberts et al. , 2020 ; Levine et al. , 2020 ) , or to augment LM pre-training data with retrieved knowledge ( Lewis et al. , 2020b ; Guu et al. , 2020 ; Lee et al. , 2019 ) . However , Talmor et al . ( 2020 ) notes that the reasoning ability of such LMs is strongly tied to the context seen during pre-training and is thus hard to generalize to new domains . Built on LMs , KID incorporates extra knowledge from external resources ( Wikipedia ) and thus shows strong performance in knowledge-intensive NLG tasks . Better Decoding Algorithm . Two common strategies dominate the decoding algorithms used by most generative models : beam search which maximizes likelihood in a local horizon ( due to finite beam size ) , and sampling decoding ( e.g. , top-k sampling ( Fan et al. , 2018 ; Holtzman et al. , 2018 ) ) which relies on randomness . Holtzman et al . ( 2020 ) find beam search often produces generic and repetitive generation , while top-k sampling tends to pick unlikely tokens which creates incoherent and unrelated sequences . Existing attempts to mitigate these problems include reranking ( Adiwardana et al. , 2020 ; Shao et al. , 2017 ) , adding control signals ( Zhang et al. , 2018 ; Xing et al. , 2017 ) , and self-adaptive truncation ( Welleck et al. , 2020 ; Peters et al. , 2019a ) . None of these decoding algorithms consider integrating knowledge in the generation process . Reflective decoding ( West et al. , 2021 ) and DeLorean ( Qin et al. , 2020 ) are two recent decoding algorithms that focus on abductive commonsense reasoning . Reflective decoding in particular has the potential to be extended to other knowledge-intensive tasks . We compare it with KID in our experiments . 3 KNOWLEDGE INFUSED DECODING . We detail the implementation of KID in this section . As shown in Figure 1 , KID comprises of three steps : retrieving relevant knowledge ( §3.1 ) , constructing external and local knowledge memory ( §3.2 ) , and guiding current step decoding under the constraint of the knowledge trie ( §3.3 ) . 3.1 RETRIEVING EXTENSIVE KNOWLEDGE FROM WIKIPEDIA . The first step of KID is to retrieve several context-relevant documents to ground the following generation . We use DPR ( Karpukhin et al. , 2020 ) as our general-purpose knowledge retriever , which projects contexts and relevant documents to a 768-dim shared embedding space using a bi-encoder network ( i.e. , two independent BERTs ( Devlin et al. , 2019 ) ) . Here , the documents refer to the 100- word chunks of Wikipedia passages released by RAG ( Lewis et al. , 2020b ) , a total of 21M documents as our knowledge source Z . We pre-load the weights from the latest checkpoint of DPR ( March 2021 ) , as it improves retrieval performance by using mined negative samples and contrastive learning , which is also suggested by Jernite ( 2020 ) . During retrieval , we perform a maximum inner-product search ( MIPS ) with faiss2 accelerated by GPU ( Johnson et al. , 2019 ) . Formally , we retrieve k most relevant document z [ 1 , ... , k ] ∈ Z for context xcontext as : z [ 1 , ... , k ] = { zi ∈ Z|topk { BERT ( xcontext ) ⊤ · BERT ( zi ) } } ( 1 ) where BERT ( · ) means the vectors encoded by BERT . The number of retrieved documents k is a task-specific hyper-parameter—we discuss its impact on performance in §4.3 . 3.2 CONSTRUCTING EXTERNAL AND LOCAL KNOWLEDGE MEMORIES . We convert multi-document input retrieved from previous step into compressed knowledge memories , in order to 1 ) allow relevant knowledge to be easily identified , 2 ) reduce the memory footprint of the knowledge , whose size grows linearly with the number of retrieved documents . Following design choice of previous successful methods ( Bosselut et al. , 2021 ; Huang et al. , 2020 ; Fan et al. , 2019a ) , we adopt co-reference resolution and open information extraction ( OpenIE ) ( Stanovsky et al. , 2018 ) to convert plain text into triplet form3 . For example , knowledge statement like “ Iceland is a Nordic island country in the North Atlantic Ocean and it is the most sparsely populated country in Europe. ” will be converted to subject-relation-object triplets such as ⟨subj : Iceland , rel : is , obj : Nordic island country⟩ , ⟨subj : Iceland , rel : is , obj : most sparsely populated country in Europe⟩ , etc . To account for overlapping elements from these triplets , we use a prefix tree ( namely Knowledge Trie Gext ) to store and organize the extracted triplets , by setting the subject in each triplet as the key in the Gext . Note that unlike common character-level dictionary tries ( Chen et al. , 2020 ; Germann et al. , 2009 ) , in Gext each triplet is stored in token unit as our goal is to efficiently query and traverse the knowledge triplets stored in it . A tree structure encoding knowledge is appealing to knowledge intensive NLG tasks , since 1 ) the non-cyclic structure helps reduce repetitions in generations , and 2 ) querying a prefix tree can be efficiently completed in constant time ( O ( |xcontext| ) ) which does not involve any costly traversal on the graph ( Ji et al. , 2020 ; Zhang et al. , 2020a ) , regardless of the scale of grounding knowledge ( normally |xcontext| ≪ |k Wiki docs| ) . We also maintain a local memory Gloc ( a first-in-first-out list ) that records all the mentioned entities in current context , to focus on concept-centric knowledge ( Zhou et al. , 2020 ; Lin et al. , 2020b ) . More details about how we construct and query these knowledge memories can be found in §A.2 of Appendix .

Summary: This paper presents a text decoding strategy for knowledge-intensive text generation tasks with the goal to infuse external knowledge based information at each time-step of decoding process via efficient document retrieval, creation of local and fast global knowledge access data structure and the interaction guided decoding method. The decoding method uses RL based policy gradient for token selection. The method is evaluated using diverse types of knowledge-intensive tasks such as abstractive question answering, logic centric writing and dialog generation. This method works with existing decoder only or encoder-decoder LMs as a plug and play method and outperforms existing knowledge aware task optimized models and correlates well with human evaluation.