Probing GPT-3’s Linguistic Knowledge on Semantic Tasks
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Bibliographic record
Abstract
GPT-3 has attracted much attention from both academia and industry.However, it is still unclear what GPT-3 has understood or learned especially in linguistic knowledge.Some studies have shown linguistic phenomena including negation and tense are hard to be recognized by language models such as BERT.In this study, we conduct probing tasks focusing on semantic information.Specifically, we investigate GPT-3's linguistic knowledge on semantic tasks to identify tense, the number of subjects, and the number of objects for a given sentence.We also experiment with different prompt designs and temperatures of the decoding method.Our experiment results suggest that GPT-3 has acquired linguistic knowledge to identify certain semantic information in most cases, but still fails when there are some types of disturbance happening in the sentence.We also perform error analysis to summarize some common types of mistakes that GPT-3 has made when dealing with certain semantic information.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it