Numeric Magnitude Comparison Effects in Large Language Models Article Swipe
Raj C. Shah
,
Vijay Marupudi
,
Reba Koenen
,
Khushi Bhardwaj
,
Sashank Varma
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.18653/v1/2023.findings-acl.383
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.18653/v1/2023.findings-acl.383
Large Language Models (LLMs) do not differentially represent numbers, which are pervasive in text. In contrast, neuroscience research has identified distinct neural representations for numbers and words. In this work, we investigate how well popular LLMs capture the magnitudes of numbers (e.g., that 4<5) from a behavioral lens. Prior research on the representational capabilities of LLMs evaluates whether they show human-level performance, for instance, high overall accuracy on standard benchmarks. Here, we ask a different question, one inspired by cognitive science: How closely do the number representations of LLMscorrespond to those of human language users, who typically demonstrate the distance, size, and ratio effects? We depend on a linking hypothesis to map the similarities among the model embeddings of number words and digits to human response times. The results reveal surprisingly human-like representations across language models of different architectures, despite the absence of the neural circuitry that directly supports these representations in the human brain. This research shows the utility of understanding LLMs using behavioral benchmarks and points the way to future work on the number of representations of LLMs and their cognitive plausibility.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.18653/v1/2023.findings-acl.383
- https://aclanthology.org/2023.findings-acl.383.pdf
- OA Status
- gold
- Cited By
- 3
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385571000
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https://openalex.org/W4385571000Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.18653/v1/2023.findings-acl.383Digital Object Identifier
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Numeric Magnitude Comparison Effects in Large Language ModelsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-01-01Full publication date if available
- Authors
-
Raj C. Shah, Vijay Marupudi, Reba Koenen, Khushi Bhardwaj, Sashank VarmaList of authors in order
- Landing page
-
https://doi.org/10.18653/v1/2023.findings-acl.383Publisher landing page
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https://aclanthology.org/2023.findings-acl.383.pdfDirect 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://aclanthology.org/2023.findings-acl.383.pdfDirect OA link when available
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Cognition, Cognitive psychology, Computer science, Ask price, Contrast (vision), Psychology, Artificial intelligence, Cognitive science, Neuroscience, Economy, EconomicsTop concepts (fields/topics) attached by OpenAlex
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3Total citation count in OpenAlex
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2025: 1, 2024: 1, 2023: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.numbers | 24, 40 |
| abstract_inverted_index.overall | 65 |
| abstract_inverted_index.popular | 34 |
| abstract_inverted_index.results | 128 |
| abstract_inverted_index.utility | 159 |
| abstract_inverted_index.whether | 57 |
| abstract_inverted_index.Language | 1 |
| abstract_inverted_index.accuracy | 66 |
| abstract_inverted_index.directly | 147 |
| abstract_inverted_index.distinct | 20 |
| abstract_inverted_index.effects? | 103 |
| abstract_inverted_index.inspired | 77 |
| abstract_inverted_index.language | 93, 134 |
| abstract_inverted_index.numbers, | 8 |
| abstract_inverted_index.research | 17, 49, 156 |
| abstract_inverted_index.response | 125 |
| abstract_inverted_index.science: | 80 |
| abstract_inverted_index.standard | 68 |
| abstract_inverted_index.supports | 148 |
| abstract_inverted_index.circuitry | 145 |
| abstract_inverted_index.cognitive | 79, 182 |
| abstract_inverted_index.contrast, | 15 |
| abstract_inverted_index.different | 74, 137 |
| abstract_inverted_index.distance, | 99 |
| abstract_inverted_index.evaluates | 56 |
| abstract_inverted_index.instance, | 63 |
| abstract_inverted_index.pervasive | 11 |
| abstract_inverted_index.question, | 75 |
| abstract_inverted_index.represent | 7 |
| abstract_inverted_index.typically | 96 |
| abstract_inverted_index.behavioral | 46, 164 |
| abstract_inverted_index.benchmarks | 165 |
| abstract_inverted_index.embeddings | 117 |
| abstract_inverted_index.human-like | 131 |
| abstract_inverted_index.hypothesis | 109 |
| abstract_inverted_index.identified | 19 |
| abstract_inverted_index.magnitudes | 38 |
| abstract_inverted_index.benchmarks. | 69 |
| abstract_inverted_index.demonstrate | 97 |
| abstract_inverted_index.human-level | 60 |
| abstract_inverted_index.investigate | 31 |
| abstract_inverted_index.capabilities | 53 |
| abstract_inverted_index.neuroscience | 16 |
| abstract_inverted_index.performance, | 61 |
| abstract_inverted_index.similarities | 113 |
| abstract_inverted_index.surprisingly | 130 |
| abstract_inverted_index.plausibility. | 183 |
| abstract_inverted_index.understanding | 161 |
| abstract_inverted_index.LLMscorrespond | 88 |
| abstract_inverted_index.architectures, | 138 |
| abstract_inverted_index.differentially | 6 |
| abstract_inverted_index.representations | 22, 86, 132, 150, 177 |
| abstract_inverted_index.representational | 52 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.8600000143051147 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.73547905 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |