Pre-trained language models (LMs) have recently gained attention for their potential as an alternative to (or proxy for) explicit knowledge bases (KBs). In this position paper, we examine this hypothesis, identify strengths and limitations of both LMs and KBs, and discuss the complementary nature of the two paradigms. In particular, we offer qualitative arguments that latent LMs are not suitable as a substitute for explicit KBs, but could play a major role for augmenting and curating KBs

Kassner, N.

Razniewski, S.

Weikum, G.

Yates, A.

English

MPG.PuRe

Language Models As or For Knowledge BasesSimon Razniewskia, Andrew Yatesa,b, Nora Kassnerc and Gerhard WeikumaaMax Planck Institute for InformaticsbUniversity of AmsterdambLMU MunichAbstractPre-trained language models (LMs) have recently gained attention for their potential as an alternativeto (or proxy for) explicit knowledge bases (KBs). In this position paper, we examine this hypothesis,identify strengths and limitations of both LMs and KBs, and discuss the complementary nature of the twoparadigms. In particular, we offer qualitative arguments that latent LMs are not suitable as a substitutefor explicit KBs, but could play a major role for augmenting and curating KBs.1. IntroductionThe ability of pre-trained contextual language models (LMs) to capture and retrieve factualknowledge has recently stirred discussion as to what extent LMs could be an alternative to, orat least a proxy for, explicit knowledge bases (KBs). LMs, such as BERT [1], GPT [2] or T5 [3]are huge transformer-based neural networks trained in a self-supervised manner on huge textcorpora, in order to predict sentence completions or masked-out text parts. In a setting called(masked) prompting or probing [4], these LMs complete a text sequence intended to elicit arelational assertion for a given subject. For example, GPT-3 correctly completes the phrase “AlanTuring was born in” with “London”, which can be seen as yielding a subject-predicate-objecttriple ⟨ Alan Turing, born in, London ⟩.Starting from the LAMA probe [5], many works have explored whether this LM-as-KBparadigm could provide an alternative to structured knowledge bases such as Wikidata. Ex-emplary analyses investigated the inclusion of entity information [6], how to turn LMs intostructured KBs [7], and how to incrementally add knowledge without side effects [8]. Otherwork studied how accuracy relates to the neural network’s storage capacity [9] and whetherQA performance scales with model size [10]. Another focus area is how LMs-as-KBs can befurther augmented with a text retrieval component, to include informative passages (e.g., fromWikipedia) [11, 12, 13].Although most works make their speculative nature clear (e.g., the title of [5] ends with aquestion mark), there is an implicit suggestion that LMs could replace structured KBs. On theother hand, NLP-centric works have identified various kinds of inconsistencies in LM outputs[14] or questioned their quantitative performance [15].This paper discusses the potential of LMs as KBs and its “softer” variation of LMs for KBs.Deep Learning for Knowledge Graphs (DL4KG)" srazniew@mpi-inf.mpg.de (S. Razniewski); ayates@mpi-inf.mpg.de (A. Yates); kassner@cis.lmu.de (N. Kassner);weikum@mpi-inf.mpg.de (G. Weikum)© 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).CEURWorkshopProceedingshttp://ceur-ws.orgISSN 1613-0073 CEUR Workshop Proceedings (CEUR-WS.org)arXiv:2110.04888v1  [cs.CL]  10 Oct 2021LM-as-KB Structured KBConstruction Self/Unsupervised Manual or semi-automaticSchema Open-ended Typically fixedMaintenance- adding facts Difficult, unpredictable side effects Easy- correcting/deleting Difficult EasyKnows what it knows No, assigns probability to everything Yes, content enumerableEntity disambiguation No/limited CommonProvenance No CommonTable 1Differences of LMs-as-KBs and structured KBs.2. BackgroundLM-as-KB refers to efforts to use an LM as a source of world knowledge, as proposed by[5]. The knowledge representation is inherently latent, given by the entirety of the neuralnetwork’s parameter values (in the billions). LMs in general have greatly advanced tasks liketext classification, machine translation, information retrieval, and question answering (see, e.g.,survey [16]).KBs, on the other hand, have been steadily advanced since the mid 2000s (with early workslike DBpedia, Freebase and Yago) [17]. They represent knowledge in the form of subject-predicate-object (SPO) triples along with qualifiers for non-binary statements. KBs have becomekey assets in major industry applications [18, 19], including search engines. A major issue forongoing KB research is quality assurance as the KB is grown and maintained. This includeshuman-in-the-loop approaches throughout the KB life-cycle [20, 21, 22].All LM-as-KB examples that follow are based on the GPT-3 daVinci model [2], one of thelargest pre-trained LMs as of October 2021.3. LM-as-KB3.1. Intrinsic ConsiderationsThe following are principal differences between LMs-as-KBs and structured KBs.Predictions vs. lookups: While content of structured KBs can be explicitly looked up, LMshave a latent representation and output probabilities at probing time. This has the advantage ofnot requiring any schema design upfront. However, it implies that it is not possible to enumeratethe knowledge stored in an LM, nor can we look up whether a certain fact is contained or not.For predictions with very high confidence scores, this is still viable. However, even top-rankedpredictions often have low scores and near-ties. Properly calibrating scores and thresholding isa black art.Example: GPT-3 does not have tangible knowledge that Alan Turing was born in London; itmerely assigns this a high confidence of 83%. Yann LeCun, on the other hand, is given mediumconfidence in being a citizen of France and Canada (67% and 26%), but he actually has French andUSA citizenship, not Canadian. The LM assigns USA a very low score. The Wikidata KB, on theother hand, only states his French citizenship, not USA. Wikidata is incomplete, but it does notcontain any errors.Statistical correlations vs. explicit knowledge: Errors made by LMs-as-KBs are not random,but exhibit systematic biases [23, 15] due to frequent values and co-occurrences (includingindirect co-occurrences captured latently).Example: When prompting GPT-3 for awards won by Alan Turing, its top-confidence predictionis the Turing Award, and lower-ranked outputs include “Nobel Prize” and “the war” (none of themcorrect).Awareness of limits: In KBs, absence of facts is explicit and easy to assert. Wikidata evensupports a way of stating non-existence (no-value statements) to impose a local-closed-worldview while following a general open-world assumption [24]. LM’s latent representationsinherently lack awareness of cases where no object exist, and so they easily produce non-zeroor even high scores for incorrect assertions.Example: Alan Turing was homosexual and never married. When prompting GPT-3 with thephrase “Alan Turing married”, the top prediction is “Sara Lavington” with score 21%, and for theprompt “Alan Turing and his wife” it is “Sara Turing” (his mother’s name). This is a case of LMhallucination [25, 26]. In contrast, Wikidata has an explicit statement ⟨ Alan Turing, spouse,no value ⟩ denoting that he was unmarried.Coverage: The scope of KBs is usually limited by the fixed set of predicates specified in the KBschema. These can be hundreds (or even a few thousands) of interesting relations, but will hardlyever be complete. In particular, “non-standard relations”, such as worked with colleague,song is about person (or event), movie based on person’s biography, are missing in allof the major KBs. LMs, on the other hand, latently tap into the full text of Wikipedia, books,news, and more, and are thus able to capture some of these predicates.Example: Creatively prompting GPT-3 can yield impressive nuggets of knowledge: the inputphrases “Turing’s colleague” and “Turing worked with” result in outputs like John Womersley, HughAlexander, Gordon Welchman (all correct). Likewise, the prompt “The Imitation Game film is aboutthe life of” is completed with the high-confidence output Alan Turing. These anecdotes indicate thegreat power of LMs to go beyond the current scope and coverage of explicit KBs.Curatability: In structured KBs, a knowledge curator can correct, add or remove assertions. ForLMs, this is an open challenge, as these operations require major (non-monotonic) re-training,or the addition of explicit exceptions, which means reverting to a KB [27, 28].Example: For the prompt “Alan Turing died in the town of”, GPT-3 returns the top prediction“Warrington”, which is wrong (he died in Wilmslow). The LM does not provide any hint on how tofix this (e.g., by changing the training corpus or parameters), and a knowledge curator has no wayto tackle such errors.Provenance: LMs have no ability to trace their outputs back to specific source documents(and passages) in the training data. KBs, on the other hand, consider reference sources as anindispensable pillar of scrutable veracity. Provenance is crucial for giving explanations to users,including knowledge engineers who maintain the KB and end-users in downstream applications.Also, without provenance, LMs have no way of pinpointing an incorrect prediction’s root causeand correcting the underlying corpus (e.g., removing misleading documents).Example: Reconsider the previous example of predicting “Warrington” as Turing’s death place.The LM itself does not give any cue where this comes from. A diligent and smart Google user coulddetect a possible origin, namely, news and other reports about a memorial plaque at 2 WarringtonCrescent in Maida Vale, London, which is near Turing’s birth place. However, the knowledgeengineer cannot be certain that this is indeed the culprit.Correctly predicted facts need explanations, too. For example, the assertion that Turing was engagedwith Joan Clarke may appear puzzling given his homosexuality. Pointing to explicit provenance iscrucial evidence.3.2. Pragmatic ConsiderationsEntity disambiguation: Although LMs are lauded for their ability to disambiguate wordsbased on context, this happens latently, and there is no easy way to explicitly build this intoprobing procedures [9, 6]. Consequently, LMs mix up facts from distinct entities that sharesurface forms. Although structured KBs cannot perform disambiguation on their own either,they can correctly separate assertions.Example: GPT-3 completes “Turing was a famous” with “mathematician”, “computer”, “code”etc., stemming from very different entities (including the Turing Machine).Numbers and singletons: LMs are good at latently capturing knowledge about predicateswith few possible object values, such as nationality or language-spoken. However, when theobject values are rarely occurring values or even singletons (i.e., occurring only with a singlesubject), the latent representation is bound to produce errors, and explicit KB storage is superior.The same applies to many cases of numeric values, where the value distribution exhibits highentropy.Example: For the input “The Turing Institute’s address in London is”, GPT-3 returns “Dilly’s Den”or “the street called Dilly’s Den” (possibly derived from the famous Piccadilly Circus; the correctvalue is British Library, 96 Euston Road, London NW1 2DB). Rephrasing the prompt does not leadto success either.Subjects with zero or many objects: An important case where the brittleness of LM predic-tions becomes a significant problem is when a subject entity has no object value for a givenpredicate or has many distinct true values. The zero-value case often leads to the pitfall that theLM must predict some value. In the many-values case, we could go deep in the ranking of theLM output, but this would usually result in a wild mix of valid and spurious objects, and thereis no guideline for how deep we should go into the ranking.Example: To obtain a list of Turing Award winners, we could prompt GPT-3 with the phrase “theTuring Award was won by” and receive various predictions like “Stuart Shieber”, “John Hopcroft”and “Andrew Yao” (1 false, 2 correct). There are currently 73 winners, all captured in Wikidata. Byprobing LMs, we would have to go very deep in the prediction ranking to see all of them, but onlyin a confusing mix of true and false positives.As for zero-objects, the prompt for “the first woman on the moon was” returns Sally Ride, EileenCollins and others. These are astronauts, but unfortunately, none of them ever landed on the moon.The ground-truth for this example is empty.We summarize the main differences in Table 2.4. LM-for-KBOur view of how to harness the great potential of LMs is to leverage them for KB curation:maintaining high quality as the KB grows throughout its life-cycle. This is a major pain point inKB practice [20, 21, 22]. For example, when adding new entities, one needs to ensure that theyare not duplicates (with slightly different alias names) of existing entities. Likewise, keepingthe type system (aka ontology) clean while gradually extending it and ensuring the correctnessof new facts are never-ending challenges.The envisioned role of LMs is to scrutinize SPO assertions considered for augmenting the KB.For example, a new fact such as ⟨ Leonardo da Vinci, has won, Turing Award ⟩ couldbe “double-checked” by prompting the LM as to whether it yields high-confidence predictionsfor this candidate assertion. This is akin to the way knowledge graph embeddings [29] havebeen considered for KB completion. However, the key difference is that KG embeddings drawfrom the KG itself, and thus do not provide complementary evidence. LMs, on the other hand,bring in a new and largely independent perspective, by tapping into text corpora (includingWikipedia, but also news, books etc.). If the LM does not yield sufficiently confident support forthe candidate fact, it should be refuted.The converse direction, using LMs to predict assertions and thus generate candidates for newfacts, is conceivable too. However, this needs major research to advance prediction accuracy.5. ConclusionIn this paper we discussed the strengths and limitations of LMs as KBs in comparison tostructured KBs. We believe that LMs cannot broadly replace KBs as explicit repositories ofstructured knowledge. While the probabilistic nature of LM-based predictions is suitablefor task-specific end-to-end learning, the inherent uncertainty of outputs does not meet thequality standards of KBs. LMs cannot separate facts from correlations, and this entails majorimpediments for KB maintenance. We advocate, on the other hand, that LMs can be valuableassets for KB curation, by providing a “second opinion” on new fact candidates or, in the absenceof corroborated evidence, signal that the candidate should be refuted. Other ways of combiningthe strengths of latent knowledge (LMs) and structured knowledge (KBs) could be promising aswell, such as “KB-for-LM” approaches that allow a LM to look up facts from an external memory(e.g., [12, 30, 31, 32]) and thus have the potential to combine the strengths of both approaches.References[1] J. Devlin, M.-W. Chang, K. Lee, K. Toutanova, BERT: Pre-training of deep bidirectionaltransformers for language understanding, in: NAACL, 2019.[2] A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever, Language models areunsupervised multitask learners, 2019. OpenAI technical report.[3] C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, P. J. Liu,Exploring the limits of transfer learning with a unified text-to-text transformer, arXiv(2019).[4] P. Liu, W. Yuan, J. Fu, Z. Jiang, H. Hayashi, G. Neubig, Pre-train, prompt, and predict: Asystematic survey of prompting methods in natural language processing, arXiv (2021).[5] F. Petroni, T. Rocktäschel, S. Riedel, P. Lewis, A. Bakhtin, Y. Wu, A. Miller, Languagemodels as knowledge bases?, in: EMNLP, 2019.[6] N. Poerner, U. Waltinger, H. Schütze, E-BERT: Efficient-yet-effective entity embeddingsfor BERT, in: Findings of EMNLP, 2020.[7] C. Wang, X. Liu, D. Song, Language models are open knowledge graphs, arXiv (2021).[8] R. Wang, et al., K-adapter: Infusing knowledge into pre-trained models with adapters,arXiv (2021).[9] B. Heinzerling, K. Inui, Language models as knowledge bases: On entity representations,storage capacity, and paraphrased queries, in: EACL, 2021.[10] A. Roberts, C. Raffel, N. Shazeer, How much knowledge can you pack into the parametersof a language model?, in: EMNLP, 2020.[11] F. Petroni, P. Lewis, A. Piktus, T. Rocktäschel, Y. Wu, A. H. Miller, S. Riedel, How contextaffects language models’ factual predictions, in: AKBC, 2020.[12] K. Guu, K. Lee, Z. Tung, P. Pasupat, M.-W. Chang, Realm: Retrieval-augmented languagemodel pre-training, in: ICML, 2020.[13] P. Lewis, et al., Retrieval-augmented generation for knowledge-intensive NLP tasks, in:NeurIPS, 2021.[14] Y. Elazar, N. Kassner, S. Ravfogel, A. Ravichander, E. Hovy, H. Schütze, Y. Goldberg,Measuring and improving consistency in pretrained language models, arXiv (2021).[15] B. Cao, H. Lin, X. Han, L. Sun, L. Yan, M. Liao, T. Xue, J. Xu, Knowledgeable or educatedguess? revisiting language models as knowledge bases, in: ACL, 2021.[16] T. Young, D. Hazarika, S. Poria, E. Cambria, Recent trends in deep learning based naturallanguage processing, Computational intelligence magazine (2018).[17] S. Razniewski, P. Das, Structured knowledge: Have we made progress? an extrinsic studyof KB coverage over 19 years, in: CIKM, 2020.[18] N. Noy, et al., Industry-scale knowledge graphs: lessons and challenges, CACM (2019).[19] G. Weikum, L. Dong, S. Razniewski, F. Suchanek, Machine knowledge: Creation andcuration of comprehensive knowledge bases, in: Foundations and Trends in Databases,2021.[20] J. Taylor, Automated knowledge base construction, AKBC invited talk, 2020. https://youtu.be/JsB4T35We0w?t=12032.[21] A. Piscopo, E. Simperl, What we talk about when we talk about Wikidata quality: aliterature survey, in: Symposium on Open Collaboration, 2019.[22] K. Shenoy, F. Ilievski, D. Garijo, D. Schwabe, P. Szekely, A study of the quality of Wikidata,arXiv (2021).[23] E. M. Bender, T. Gebru, A. McMillan-Major, S. Shmitchell, On the dangers of stochasticparrots: Can language models be too big?, in: FAccT, 2021.[24] H. Arnaout, S. Razniewski, G. Weikum, J. Z. Pan, Negative knowledge for open-worldWikidata, in: Companion Proceedings of the Web Conference, 2021.[25] A. Rohrbach, et al., Object hallucination in image captioning, in: EMNLP, 2018.[26] C. Wang, R. Sennrich, On exposure bias, hallucination and domain shift in neural machinetranslation, in: ACL, 2020.[27] C. Zhu, A. S. Rawat, M. Zaheer, S. Bhojanapalli, D. Li, F. Yu, S. Kumar, Modifying memoriesin transformer models, in: arXiv, 2020.[28] N. D. Cao, W. Aziz, I. Titov, Editing factual knowledge in language models, in: EMNLP,2021.[29] Q. Wang, Z. Mao, B. Wang, L. Guo, Knowledge graph embedding: A survey of approachesand applications, TKDE (2017).[30] T. Févry, L. B. Soares, N. FitzGerald, E. Choi, T. Kwiatkowski, Entities as experts: Sparsememory access with entity supervision, in: EMNLP, 2020.[31] H. Sun, L. B. Soares, P. Verga, W. W. Cohen, Adaptable and interpretable neural memoryover symbolic knowledge, in: NAACL, 2021.[32] N. Kassner, O. Tafjord, H. Schutze, P. Clark, Enriching a model’s notion of belief using apersistent memory, in: arXiv, 2021.

Language Models As or For Knowledge Bases

http://hdl.handle.net/21.11116/0000-0009-6512-1

Language Models As or For Knowledge Bases

Abstract

Similar works

Full text

Available Versions

MPG.PuRe