We lifted 9 functions out of this paper's own repositories and ran 8 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 |
|---|---|---|
| facebookresearch/ParlAI | canonical | 8 of 9 |
| Function | Status | Where it lives |
|---|---|---|
| escape | Ran | facebookresearch/ParlAI/parlai/core/dict.py pointer only (licence: MIT) · get_code("758e9948df81569f") |
| find_ngrams | Ran | facebookresearch/ParlAI/parlai/core/dict.py pointer only (licence: MIT) · get_code("761ab53c3cc0c21a") |
| get_metric_display_data | Ran | facebookresearch/ParlAI/parlai/core/metrics.py pointer only (licence: MIT) · get_code("b6eb8b2b6abaf6ba") |
| register_agent | Ran | facebookresearch/ParlAI/parlai/core/loader.py pointer only (licence: MIT) · get_code("60339a7726e86b18") |
| register_mutator | Ran | facebookresearch/ParlAI/parlai/core/mutators.py pointer only (licence: MIT) · get_code("f9ccfd237025f9b0") |
| register_script | Ran | facebookresearch/ParlAI/parlai/core/loader.py pointer only (licence: MIT) · get_code("eba5f047c4021087") |
| register_teacher | Ran | facebookresearch/ParlAI/parlai/core/loader.py pointer only (licence: MIT) · get_code("b965ad8e264b80f1") |
| unescape | Ran | facebookresearch/ParlAI/parlai/core/dict.py pointer only (licence: MIT) · get_code("7e5138ac467c5625") |
| normalize_answer | Not yet run | facebookresearch/ParlAI/parlai/core/metrics.py pointer only (licence: MIT) · get_code("da3bc8c42eec18d1") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Large language models show improved downstream task performance when prompted to generate step-by-step reasoning to justify their final answers. These reasoning steps greatly improve model interpretability and verification, but objectively studying their correctness (independent of the final answer) is difficult without reliable methods for automatic evaluation. We simply do not know how often the stated reasoning steps actually support the final end task predictions. In this work, we present ROSCOE, a suite of interpretable, unsupervised automatic scores that improve and extend previous text generation evaluation metrics. To evaluate ROSCOE against baseline metrics, we design a typology of reasoning errors and collect synthetic and human evaluation scores on commonly used reasoning datasets. In contrast with existing metrics, ROSCOE can measure semantic consistency, logicality, informativeness, fluency, and factuality - among other traits - by leveraging properties of step-by-step rationales. We empirically verify the strength of our metrics on five human annotated and six programmatically perturbed diagnostics datasets - covering a diverse set of tasks that require reasoning skills and show that ROSCOE can consistently outperform baseline metrics.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2212.07919")
get_code_for_paper("2212.07919")
have("2212.07919")
Connect an agent — have() is free.