Swabha Swayamdipta, Rowan Zellers, Yejin Choi, John Thickstun, Krishna Pillutla, Sean Welleck, Zaid Harchaoui
We lifted 7 functions out of this paper's own repositories and ran 5 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 |
|---|---|---|
| krishnap25/mauve | canonical | 5 of 6 |
| krishnap25/mauve-experiments | canonical | 0 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| cluster_feats | Ran | krishnap25/mauve/src/mauve/compute_mauve.py pointer only (licence: NOASSERTION) · get_code("b0c6f373cfee6834") |
| get_divergence_curve_for_multinomials | Ran | krishnap25/mauve/src/mauve/compute_mauve.py pointer only (licence: NOASSERTION) · get_code("a9f70fe76e261cc1") |
| get_features_from_input | Ran | krishnap25/mauve/src/mauve/compute_mauve.py pointer only (licence: NOASSERTION) · get_code("b8a7725573b67ef3") |
| get_fronter_integral | Ran | krishnap25/mauve/src/mauve/compute_mauve.py pointer only (licence: NOASSERTION) · get_code("b898b214e406536a") |
| kl_multinomial | Ran | krishnap25/mauve/src/mauve/compute_mauve.py pointer only (licence: NOASSERTION) · get_code("98e598ee44b02b1c") |
| cluster_feats | Not yet run | krishnap25/mauve-experiments/src/mauve_metrics.py pointer only (licence: NONE) · get_code("58f653b7d38067cb") |
| compute_mauve | Not yet run | krishnap25/mauve/src/mauve/compute_mauve.py pointer only (licence: NOASSERTION) · get_code("453022cad1085d13") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
As major progress is made in open-ended text generation, measuring how close machine-generated text is to human language remains a critical open problem. We introduce MAUVE, a comparison measure for open-ended text generation, which directly compares the learnt distribution from a text generation model to the distribution of human-written text using divergence frontiers. MAUVE scales up to modern text generation models by computing information divergences in a quantized embedding space. Through an extensive empirical study on three open-ended generation tasks, we find that MAUVE identifies known properties of generated text, scales naturally with model size, and correlates with human judgments, with fewer restrictions than existing distributional evaluation metrics.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2102.01454")
get_code_for_paper("2102.01454")
have("2102.01454")
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