Evaluating and Modeling Attribution for Cross-Lingual Question Answering Article Swipe
Benjamin Müller
,
John Wieting
,
Jonathan H. Clark
,
Tom Kwiatkowski
,
Sebastian Ruder
,
Livio Baldini Soares
,
Roee Aharoni
,
Jonathan Herzig
,
Xinyi Wang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2305.14332
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2305.14332
Trustworthy answer content is abundant in many high-resource languages and is instantly accessible through question answering systems, yet this content can be hard to access for those that do not speak these languages. The leap forward in cross-lingual modeling quality offered by generative language models offers much promise, yet their raw generations often fall short in factuality. To improve trustworthiness in these systems, a promising direction is to attribute the answer to a retrieved source, possibly in a content-rich language different from the query. Our work is the first to study attribution for cross-lingual question answering. First, we collect data in 5 languages to assess the attribution level of a state-of-the-art cross-lingual QA system. To our surprise, we find that a substantial portion of the answers is not attributable to any retrieved passages (up to 50% of answers exactly matching a gold reference) despite the system being able to attend directly to the retrieved text. Second, to address this poor attribution level, we experiment with a wide range of attribution detection techniques. We find that Natural Language Inference models and PaLM 2 fine-tuned on a very small amount of attribution data can accurately detect attribution. Based on these models, we improve the attribution level of a cross-lingual question-answering system. Overall, we show that current academic generative cross-lingual QA systems have substantial shortcomings in attribution and we build tooling to mitigate these issues.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2305.14332
- https://arxiv.org/pdf/2305.14332
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4378510295
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4378510295Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2305.14332Digital Object Identifier
- Title
-
Evaluating and Modeling Attribution for Cross-Lingual Question AnsweringWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-05-23Full publication date if available
- Authors
-
Benjamin Müller, John Wieting, Jonathan H. Clark, Tom Kwiatkowski, Sebastian Ruder, Livio Baldini Soares, Roee Aharoni, Jonathan Herzig, Xinyi WangList of authors in order
- Landing page
-
https://arxiv.org/abs/2305.14332Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2305.14332Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2305.14332Direct OA link when available
- Concepts
-
Computer science, Attribution, Question answering, Natural language processing, Generative grammar, Artificial intelligence, Rank (graph theory), Matching (statistics), Inference, Generative model, Information retrieval, Data science, Psychology, Statistics, Mathematics, Social psychology, CombinatoricsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.speak | 30 |
| abstract_inverted_index.study | 90 |
| abstract_inverted_index.text. | 154 |
| abstract_inverted_index.their | 49 |
| abstract_inverted_index.these | 31, 61, 197, 230 |
| abstract_inverted_index.those | 26 |
| abstract_inverted_index.First, | 96 |
| abstract_inverted_index.access | 24 |
| abstract_inverted_index.amount | 187 |
| abstract_inverted_index.answer | 1, 70 |
| abstract_inverted_index.assess | 104 |
| abstract_inverted_index.attend | 149 |
| abstract_inverted_index.detect | 193 |
| abstract_inverted_index.level, | 161 |
| abstract_inverted_index.models | 44, 178 |
| abstract_inverted_index.offers | 45 |
| abstract_inverted_index.query. | 83 |
| abstract_inverted_index.system | 145 |
| abstract_inverted_index.Natural | 175 |
| abstract_inverted_index.Second, | 155 |
| abstract_inverted_index.address | 157 |
| abstract_inverted_index.answers | 125, 137 |
| abstract_inverted_index.collect | 98 |
| abstract_inverted_index.content | 2, 19 |
| abstract_inverted_index.current | 213 |
| abstract_inverted_index.despite | 143 |
| abstract_inverted_index.exactly | 138 |
| abstract_inverted_index.forward | 35 |
| abstract_inverted_index.improve | 58, 200 |
| abstract_inverted_index.issues. | 231 |
| abstract_inverted_index.models, | 198 |
| abstract_inverted_index.offered | 40 |
| abstract_inverted_index.portion | 122 |
| abstract_inverted_index.quality | 39 |
| abstract_inverted_index.source, | 74 |
| abstract_inverted_index.system. | 113, 208 |
| abstract_inverted_index.systems | 218 |
| abstract_inverted_index.through | 13 |
| abstract_inverted_index.tooling | 227 |
| abstract_inverted_index.Language | 176 |
| abstract_inverted_index.Overall, | 209 |
| abstract_inverted_index.abundant | 4 |
| abstract_inverted_index.academic | 214 |
| abstract_inverted_index.directly | 150 |
| abstract_inverted_index.language | 43, 79 |
| abstract_inverted_index.matching | 139 |
| abstract_inverted_index.mitigate | 229 |
| abstract_inverted_index.modeling | 38 |
| abstract_inverted_index.passages | 132 |
| abstract_inverted_index.possibly | 75 |
| abstract_inverted_index.promise, | 47 |
| abstract_inverted_index.question | 14, 94 |
| abstract_inverted_index.systems, | 16, 62 |
| abstract_inverted_index.Inference | 177 |
| abstract_inverted_index.answering | 15 |
| abstract_inverted_index.attribute | 68 |
| abstract_inverted_index.detection | 170 |
| abstract_inverted_index.different | 80 |
| abstract_inverted_index.direction | 65 |
| abstract_inverted_index.instantly | 11 |
| abstract_inverted_index.languages | 8, 102 |
| abstract_inverted_index.promising | 64 |
| abstract_inverted_index.retrieved | 73, 131, 153 |
| abstract_inverted_index.surprise, | 116 |
| abstract_inverted_index.accessible | 12 |
| abstract_inverted_index.accurately | 192 |
| abstract_inverted_index.answering. | 95 |
| abstract_inverted_index.experiment | 163 |
| abstract_inverted_index.fine-tuned | 182 |
| abstract_inverted_index.generative | 42, 215 |
| abstract_inverted_index.languages. | 32 |
| abstract_inverted_index.reference) | 142 |
| abstract_inverted_index.Trustworthy | 0 |
| abstract_inverted_index.attribution | 91, 106, 160, 169, 189, 202, 223 |
| abstract_inverted_index.factuality. | 56 |
| abstract_inverted_index.generations | 51 |
| abstract_inverted_index.substantial | 121, 220 |
| abstract_inverted_index.techniques. | 171 |
| abstract_inverted_index.attributable | 128 |
| abstract_inverted_index.attribution. | 194 |
| abstract_inverted_index.content-rich | 78 |
| abstract_inverted_index.shortcomings | 221 |
| abstract_inverted_index.cross-lingual | 37, 93, 111, 206, 216 |
| abstract_inverted_index.high-resource | 7 |
| abstract_inverted_index.trustworthiness | 59 |
| abstract_inverted_index.state-of-the-art | 110 |
| abstract_inverted_index.question-answering | 207 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 9 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/1 |
| sustainable_development_goals[0].score | 0.47999998927116394 |
| sustainable_development_goals[0].display_name | No poverty |
| citation_normalized_percentile |