We lifted 13 functions out of this paper's own repositories and ran 9 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 |
|---|---|---|
| yale-lily/loft | canonical | 9 of 13 |
| Function | Status | Where it lives |
|---|---|---|
| distinct | Ran | yale-lily/loft/LoFT_evaluation/diversity_metrics.py code served (permissive licence) · get_code("9a8250b6202d0aaf") |
| evaluate | Ran | yale-lily/loft/LoFT_framework/tapex/model_eval.py code served (permissive licence) · get_code("1b98d89684c0bb11") |
| extract_orig_inference_dict | Ran | yale-lily/loft/LoFT_framework/prepare_verifier_input.py code served (permissive licence) · get_code("e579ce3de5f6f366") |
| extract_structure_data | Ran | yale-lily/loft/LoFT_framework/tapex/model_eval.py code served (permissive licence) · get_code("665139839a94ca8f") |
| func_compute_bleu | Ran | yale-lily/loft/LoFT_evaluation/diversity_metrics.py code served (permissive licence) · get_code("0771747dc8451c0b") |
| logicnlg_evaluate | Ran | yale-lily/loft/LoFT_framework/tapex/model_eval.py code served (permissive licence) · get_code("8d1d37d893efe52b") |
| read_seqs | Ran | yale-lily/loft/LoFT_evaluation/diversity_metrics.py code served (permissive licence) · get_code("56e06b573ddd7ba3") |
| read_verifier_output | Ran | yale-lily/loft/LoFT_framework/prepare_evaluation_data.py code served (permissive licence) · get_code("aeef32be08be0770") |
| sample_output_for_logicnlg_evaluation | Ran | yale-lily/loft/LoFT_framework/prepare_evaluation_data.py code served (permissive licence) · get_code("ea85ef1c2133c936") |
| build_abstract_tree | Not yet run | yale-lily/loft/LoFT_data_processing/inference/modules/templatize.py code served (permissive licence) · get_code("9e349ec94a8be51d") |
| create_templates_single | Not yet run | yale-lily/loft/LoFT_data_processing/inference/modules/templatize.py code served (permissive licence) · get_code("ec9b4f078e421bab") |
| extract_structure_data | Not yet run | yale-lily/loft/LoFT_framework/prepare_verifier_input.py code served (permissive licence) · get_code("cae89a129d9d6aa3") |
| prepare_tapex_evaluation_data | Not yet run | yale-lily/loft/LoFT_evaluation/prepare_tapex_evaluation_data.py code served (permissive licence) · get_code("6db7ab7efba8239e") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Logical Table-to-Text (LT2T) generation is tasked with generating logically faithful sentences from tables. There currently exists two challenges in the field: 1) Faithfulness: how to generate sentences that are factually correct given the table content; 2) Diversity: how to generate multiple sentences that offer different perspectives on the table. This work proposes LoFT, which utilizes logic forms as fact verifiers and content planners to control LT2T generation. Experimental results on the LogicNLG dataset demonstrate that LoFT is the first model that addresses unfaithfulness and lack of diversity issues simultaneously. Our code is publicly available at https://github.com/Yale-LILY/LoFT.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2302.02962")
get_code_for_paper("2302.02962")
have("2302.02962")
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