Linearity of Relation Decoding in Transformer Language Models
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866916127370641408 |
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| author | Hernandez, Evan Sharma, Arnab Sen Haklay, Tal Meng, Kevin Wattenberg, Martin Andreas, Jacob Belinkov, Yonatan Bau, David |
| author_facet | Hernandez, Evan Sharma, Arnab Sen Haklay, Tal Meng, Kevin Wattenberg, Martin Andreas, Jacob Belinkov, Yonatan Bau, David |
| contents | Much of the knowledge encoded in transformer language models (LMs) may be expressed in terms of relations: relations between words and their synonyms, entities and their attributes, etc. We show that, for a subset of relations, this computation is well-approximated by a single linear transformation on the subject representation. Linear relation representations may be obtained by constructing a first-order approximation to the LM from a single prompt, and they exist for a variety of factual, commonsense, and linguistic relations. However, we also identify many cases in which LM predictions capture relational knowledge accurately, but this knowledge is not linearly encoded in their representations. Our results thus reveal a simple, interpretable, but heterogeneously deployed knowledge representation strategy in transformer LMs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_09124 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Linearity of Relation Decoding in Transformer Language Models Hernandez, Evan Sharma, Arnab Sen Haklay, Tal Meng, Kevin Wattenberg, Martin Andreas, Jacob Belinkov, Yonatan Bau, David Computation and Language Much of the knowledge encoded in transformer language models (LMs) may be expressed in terms of relations: relations between words and their synonyms, entities and their attributes, etc. We show that, for a subset of relations, this computation is well-approximated by a single linear transformation on the subject representation. Linear relation representations may be obtained by constructing a first-order approximation to the LM from a single prompt, and they exist for a variety of factual, commonsense, and linguistic relations. However, we also identify many cases in which LM predictions capture relational knowledge accurately, but this knowledge is not linearly encoded in their representations. Our results thus reveal a simple, interpretable, but heterogeneously deployed knowledge representation strategy in transformer LMs. |
| title | Linearity of Relation Decoding in Transformer Language Models |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2308.09124 |