We lifted 14 functions out of this paper's own repositories and ran 11 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| eric11eca/inference-information-probing | canonical | 11 of 14 |
| Function | Status | Where it lives |
|---|---|---|
| batched_index_select | Ran | eric11eca/inference-information-probing/jiant/ext/allennlp.py code served (permissive licence) · get_code("e445a3b7d6a58154") |
| count_freq | Ran | eric11eca/inference-information-probing/analyze.py code served (permissive licence) · get_code("df8b734fdb77932a") |
| evaluate | Ran | eric11eca/inference-information-probing/inform_prob/trainer.py code served (permissive licence) · get_code("349a73ed4ded5853") |
| file_len | Ran | eric11eca/inference-information-probing/inform_prob/util.py code served (permissive licence) · get_code("e6bd0ba803f299b8") |
| flat_collate_fn | Ran | eric11eca/inference-information-probing/jiant/tasks/core.py code served (permissive licence) · get_code("8f7619b2828926ea") |
| get_fasttext | Ran | eric11eca/inference-information-probing/inform_prob/process.py code served (permissive licence) · get_code("59cfe607c72c35ba") |
| masked_softmax | Ran | eric11eca/inference-information-probing/jiant/ext/allennlp.py code served (permissive licence) · get_code("16cde5ff8ae0a586") |
| metadata_collate_fn | Ran | eric11eca/inference-information-probing/jiant/tasks/core.py code served (permissive licence) · get_code("61ffc5549a75fe3c") |
| read_data | Ran | eric11eca/inference-information-probing/inform_prob/util.py code served (permissive licence) · get_code("0b2cc230b2f4d63a") |
| resolve_is_lower_case | Ran | eric11eca/inference-information-probing/jiant/shared/model_resolution.py code served (permissive licence) · get_code("e17e83a5650fd73c") |
| resolve_warmup_steps | Ran | eric11eca/inference-information-probing/jiant/shared/model_setup.py code served (permissive licence) · get_code("36c8a80131f2e20a") |
| eval_all | Not yet run | eric11eca/inference-information-probing/inform_prob/trainer.py code served (permissive licence) · get_code("ad20a9d93a38e9cd") |
| get_type_token_ratio | Not yet run | eric11eca/inference-information-probing/analyze.py code served (permissive licence) · get_code("fa314e7bf8386566") |
| weighted_sum | Not yet run | eric11eca/inference-information-probing/jiant/ext/allennlp.py code served (permissive licence) · get_code("2f417d486bcad523") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Progress in pre-trained language models has led to a surge of impressive results on downstream tasks for natural language understanding. Recent work on probing pre-trained language models uncovered a wide range of linguistic properties encoded in their contextualized representations. However, it is unclear whether they encode semantic knowledge that is crucial to symbolic inference methods. We propose a methodology for probing linguistic information for logical inference in pre-trained language model representations. Our probing datasets cover a list of linguistic phenomena required by major symbolic inference systems. We find that (i) pre-trained language models do encode several types of linguistic information for inference, but there are also some types of information that are weakly encoded, (ii) language models can effectively learn missing linguistic information through fine-tuning. Overall, our findings provide insights into which aspects of linguistic information for logical inference do language models and their pre-training procedures capture. Moreover, we have demonstrated language models' potential as semantic and background knowledge bases for supporting symbolic inference methods.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2112.01753")
get_code_for_paper("2112.01753")
have("2112.01753")
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