We lifted 6 functions out of this paper's own repositories and ran 4 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 |
|---|---|---|
| nusnlp/complex-tr | canonical | 4 of 6 |
| Function | Status | Where it lives |
|---|---|---|
| get_tokens | Ran | nusnlp/complex-tr/prompt_experiment/sem_metric/evaluate.py pointer only (licence: NONE) · get_code("72ae17e5ad61f6cb") |
| make_qid_to_has_ans | Ran | nusnlp/complex-tr/prompt_experiment/sem_metric/evaluate.py pointer only (licence: NONE) · get_code("41e5ac23be97994b") |
| mean_pooling | Ran | nusnlp/complex-tr/custom_qa/get_local_ranking.py pointer only (licence: NONE) · get_code("fe0e2df4d9cb5edd") |
| normalize_answer | Ran | nusnlp/complex-tr/prompt_experiment/sem_metric/evaluate.py pointer only (licence: NONE) · get_code("2d391d2daa3c57fe") |
| get_argument_value | Not yet run | nusnlp/complex-tr/atlas/finetune_qa.py pointer only (licence: NONE) · get_code("210d1520a7ea9d7f") |
| set_parser_options | Not yet run | nusnlp/complex-tr/atlas/finetune_qa.py pointer only (licence: NONE) · get_code("3d952765274002ec") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Knowledge in the real world is being updated constantly. However, it is costly to frequently update large language models (LLMs). Therefore, it is crucial for LLMs to understand the concept of temporal knowledge. However, prior works on temporal question answering (TQA) did not emphasize multi-answer and multi-hop types of temporal reasoning. In this paper, we propose a complex temporal question-answering dataset Complex-TR that focuses on multi-answer and multi-hop temporal reasoning. Besides, we also propose a novel data augmentation strategy to improve the complex temporal reasoning capability and robustness of LLMs. We conducted experiments on multiple temporal QA datasets. Experimental results show that our method is able to improve LLMs' performance on temporal QA benchmarks by significant margins. Our code and data are released at: https://github.com/nusnlp/complex-tr.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2311.09821")
get_code_for_paper("2311.09821")
have("2311.09821")
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