Recourse for Reclamation: Chatting with Generative Language Models Article Swipe
Jennifer Chien
,
Kevin R. McKee
,
Jackie Kay
,
William Isaac
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1145/3613905.3650999
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1145/3613905.3650999
Researchers and developers increasingly rely on toxicity scoring to moderate\ngenerative language model outputs, in settings such as customer service,\ninformation retrieval, and content generation. However, toxicity scoring may\nrender pertinent information inaccessible, rigidify or "value-lock" cultural\nnorms, and prevent language reclamation processes, particularly for\nmarginalized people. In this work, we extend the concept of algorithmic\nrecourse to generative language models: we provide users a novel mechanism to\nachieve their desired prediction by dynamically setting thresholds for toxicity\nfiltering. Users thereby exercise increased agency relative to interactions\nwith the baseline system. A pilot study ($n = 30$) supports the potential of\nour proposed recourse mechanism, indicating improvements in usability compared\nto fixed-threshold toxicity-filtering of model outputs. Future work should\nexplore the intersection of toxicity scoring, model controllability, user\nagency, and language reclamation processes -- particularly with regard to the\nbias that many communities encounter when interacting with generative language\nmodels.\n
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1145/3613905.3650999
- https://dl.acm.org/doi/pdf/10.1145/3613905.3650999
- OA Status
- gold
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- 50
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- 10
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All OpenAlex metadata
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https://openalex.org/W4393119066Canonical identifier for this work in OpenAlex
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https://doi.org/10.1145/3613905.3650999Digital Object Identifier
- Title
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Recourse for Reclamation: Chatting with Generative Language ModelsWork title
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
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2024-05-02Full publication date if available
- Authors
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Jennifer Chien, Kevin R. McKee, Jackie Kay, William IsaacList of authors in order
- Landing page
-
https://doi.org/10.1145/3613905.3650999Publisher landing page
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https://dl.acm.org/doi/pdf/10.1145/3613905.3650999Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://dl.acm.org/doi/pdf/10.1145/3613905.3650999Direct OA link when available
- Concepts
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Land reclamation, Generative grammar, Computer science, Natural language processing, Artificial intelligence, Geography, ArchaeologyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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| abstract_inverted_index.interacting | 131 |
| abstract_inverted_index.may\nrender | 26 |
| abstract_inverted_index.reclamation | 37, 118 |
| abstract_inverted_index.to\nachieve | 61 |
| abstract_inverted_index."value-lock" | 32 |
| abstract_inverted_index.compared\nto | 99 |
| abstract_inverted_index.improvements | 96 |
| abstract_inverted_index.increasingly | 3 |
| abstract_inverted_index.intersection | 109 |
| abstract_inverted_index.particularly | 39, 121 |
| abstract_inverted_index.inaccessible, | 29 |
| abstract_inverted_index.user\nagency, | 115 |
| abstract_inverted_index.fixed-threshold | 100 |
| abstract_inverted_index.should\nexplore | 107 |
| abstract_inverted_index.controllability, | 114 |
| abstract_inverted_index.cultural\nnorms, | 33 |
| abstract_inverted_index.for\nmarginalized | 40 |
| abstract_inverted_index.interactions\nwith | 78 |
| abstract_inverted_index.toxicity-filtering | 101 |
| abstract_inverted_index.language\nmodels.\n | 134 |
| abstract_inverted_index.moderate\ngenerative | 9 |
| abstract_inverted_index.toxicity\nfiltering. | 70 |
| abstract_inverted_index.algorithmic\nrecourse | 50 |
| abstract_inverted_index.service,\ninformation | 18 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.5899999737739563 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.03707433 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |