{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T06:52:04Z","timestamp":1763621524226},"reference-count":68,"publisher":"MIT Press","issue":"1","license":[{"start":{"date-parts":[[2024,4,24]],"date-time":"2024-04-24T00:00:00Z","timestamp":1713916800000},"content-version":"vor","delay-in-days":114,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["direct.mit.edu"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,3,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Transformer-based language models have been shown to be highly effective for several NLP tasks. In this article, we consider three transformer models, BERT, RoBERTa, and XLNet, in both small and large versions, and investigate how faithful their representations are with respect to the semantic content of texts. We formalize a notion of semantic faithfulness, in which the semantic content of a text should causally figure in a model\u2019s inferences in question answering. We then test this notion by observing a model\u2019s behavior on answering questions about a story after performing two novel semantic interventions\u2014deletion intervention and negation intervention. While transformer models achieve high performance on standard question answering tasks, we show that they fail to be semantically faithful once we perform these interventions for a significant number of cases (\u223c 50% for deletion intervention, and \u223c 20% drop in accuracy for negation intervention). We then propose an intervention-based training regime that can mitigate the undesirable effects for deletion intervention by a significant margin (from \u223c 50% to \u223c 6%). We analyze the inner-workings of the models to better understand the effectiveness of intervention-based training for deletion intervention. But we show that this training does not attenuate other aspects of semantic unfaithfulness such as the models\u2019 inability to deal with negation intervention or to capture the predicate\u2013argument structure of texts. We also test InstructGPT, via prompting, for its ability to handle the two interventions and to capture predicate\u2013argument structure. While InstructGPT models do achieve very high performance on predicate\u2013argument structure task, they fail to respond adequately to our deletion and negation interventions.<\/jats:p>","DOI":"10.1162\/coli_a_00493","type":"journal-article","created":{"date-parts":[[2023,11,15]],"date-time":"2023-11-15T19:26:01Z","timestamp":1700076361000},"page":"119-155","update-policy":"http:\/\/dx.doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":3,"title":["Analyzing Semantic Faithfulness of Language Models via Input Intervention on Question Answering"],"prefix":"10.1162","volume":"50","author":[{"given":"Akshay","family":"Chaturvedi","sequence":"first","affiliation":[{"name":"IRIT, Universit\u00e9 Paul Sabatier Toulouse, France akshay91.isi@gmail.com"}]},{"given":"Swarnadeep","family":"Bhar","sequence":"additional","affiliation":[{"name":"IRIT, Universit\u00e9 Paul Sabatier Toulouse, France swarnadeep.bhar@irit.fr"}]},{"given":"Soumadeep","family":"Saha","sequence":"additional","affiliation":[{"name":"Indian Statistical Institute Kolkata, India soumadeep.saha97@gmail.com"}]},{"given":"Utpal","family":"Garain","sequence":"additional","affiliation":[{"name":"Indian Statistical Institute Kolkata, India utpal@isical.ac.in"}]},{"given":"Nicholas","family":"Asher","sequence":"additional","affiliation":[{"name":"IRIT, Universit\u00e9 Paul Sabatier Toulouse, France nicholas.asher@irit.fr"}]}],"member":"281","published-online":{"date-parts":[[2024,3,1]]},"reference":[{"key":"2024042419444692500_bib1","doi-asserted-by":"publisher","DOI":"10.1007\/978-94-011-1715-9","volume-title":"Reference to Abstract Objects in Discourse","author":"Asher","year":"1993"},{"key":"2024042419444692500_bib2","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511793936","volume-title":"Lexical Meaning in Context: A Web of Words","author":"Asher","year":"2011"},{"key":"2024042419444692500_bib3","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1007\/978-3-030-84060-0_6","article-title":"Fair and adequate explanations","volume-title":"International Cross-Domain Conference for Machine Learning and Knowledge Extraction","author":"Asher","year":"2021"},{"issue":"4","key":"2024042419444692500_bib4","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1007\/s10992-016-9402-1","article-title":"Message exchange games in strategic contexts","volume":"46","author":"Asher","year":"2017","journal-title":"Journal of Philosophical Logic"},{"key":"2024042419444692500_bib5","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-642-25655-4_2","article-title":"SDRT and continuation semantics","volume-title":"JSAI International Symposium on Artificial Intelligence","author":"Asher","year":"2010"},{"key":"2024042419444692500_bib6","doi-asserted-by":"publisher","first-page":"205","DOI":"10.18653\/v1\/2020.repl4nlp-1.24","article-title":"What\u2019s in a name? 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