On Using Self-Report Studies to Analyze Language Models
We are at a curious point in time where our ability to build language models (LMs) has outpaced our ability to analyze them. We do not really know how to reliably determine their capabilities, biases, dangers, knowledge, and so on. The benchmarks we have are often overly specific, do not generalize...
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| Format: | Article |
| Language: | English |
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Linköping University Electronic Press
2024-09-01
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| Series: | Northern European Journal of Language Technology |
| Online Access: | https://nejlt.ep.liu.se/article/view/5000 |
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| _version_ | 1832591272475361280 |
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| author | Matúš Pikuliak |
| author_facet | Matúš Pikuliak |
| author_sort | Matúš Pikuliak |
| collection | DOAJ |
| description |
We are at a curious point in time where our ability to build language models (LMs) has outpaced our ability to analyze them. We do not really know how to reliably determine their capabilities, biases, dangers, knowledge, and so on. The benchmarks we have are often overly specific, do not generalize well, and are susceptible to data leakage. Recently, I have noticed a trend of using self-report studies, such as various polls and questionnaires originally designed for humans, to analyze the properties of LMs. I think that this approach can easily lead to false results, which can be quite dangerous considering the current discussions on AI safety, governance, and regulation. To illustrate my point, I will delve deeper into several papers that employ self-report methodologies and I will try to highlight some of their weaknesses.
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| format | Article |
| id | doaj-art-e87718772025478e9c1771a65a315028 |
| institution | Kabale University |
| issn | 2000-1533 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Linköping University Electronic Press |
| record_format | Article |
| series | Northern European Journal of Language Technology |
| spelling | doaj-art-e87718772025478e9c1771a65a3150282025-01-22T15:24:16ZengLinköping University Electronic PressNorthern European Journal of Language Technology2000-15332024-09-0110110.3384/nejlt.2000-1533.2024.5000On Using Self-Report Studies to Analyze Language ModelsMatúš Pikuliak0Kempelen Institute of Intelligent Technologies We are at a curious point in time where our ability to build language models (LMs) has outpaced our ability to analyze them. We do not really know how to reliably determine their capabilities, biases, dangers, knowledge, and so on. The benchmarks we have are often overly specific, do not generalize well, and are susceptible to data leakage. Recently, I have noticed a trend of using self-report studies, such as various polls and questionnaires originally designed for humans, to analyze the properties of LMs. I think that this approach can easily lead to false results, which can be quite dangerous considering the current discussions on AI safety, governance, and regulation. To illustrate my point, I will delve deeper into several papers that employ self-report methodologies and I will try to highlight some of their weaknesses. https://nejlt.ep.liu.se/article/view/5000 |
| spellingShingle | Matúš Pikuliak On Using Self-Report Studies to Analyze Language Models Northern European Journal of Language Technology |
| title | On Using Self-Report Studies to Analyze Language Models |
| title_full | On Using Self-Report Studies to Analyze Language Models |
| title_fullStr | On Using Self-Report Studies to Analyze Language Models |
| title_full_unstemmed | On Using Self-Report Studies to Analyze Language Models |
| title_short | On Using Self-Report Studies to Analyze Language Models |
| title_sort | on using self report studies to analyze language models |
| url | https://nejlt.ep.liu.se/article/view/5000 |
| work_keys_str_mv | AT matuspikuliak onusingselfreportstudiestoanalyzelanguagemodels |