Fei Huang, Min Zhang, Bo Zhang, Ji Zhang, Chen Li, Zhenghua Li, Shaopeng Lai, Houquan Zhou
We lifted 4 functions out of this paper's own repositories and ran 1 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 |
|---|---|---|
| mozillazg/python-pinyin | canonical | 1 of 3 |
| gingasan/lemon | — | 0 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| remove_dup_items | Ran | mozillazg/python-pinyin/gen_phrases_dict.py code served (permissive licence) · get_code("5fdd12a2133be42f") |
| AutoCSCReLM | Not yet run | gingasan/lemon/autocsc.py pointer only (licence: NONE) · get_code("72d91cedf67b4f34") |
| get_pinyins_via_pinyin_dict | Not yet run | mozillazg/python-pinyin/tidy_phrases_dict.py code served (permissive licence) · get_code("03697a2418a4c9f6") |
| parse | Not yet run | mozillazg/python-pinyin/gen_phrases_dict.py code served (permissive licence) · get_code("d95e439c609fdab6") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
This work proposes a simple training-free prompt-free approach to leverage large language models (LLMs) for the Chinese spelling correction (CSC) task, which is totally different from all previous CSC approaches. The key idea is to use an LLM as a pure language model in a conventional manner. The LLM goes through the input sentence from the beginning, and at each inference step, produces a distribution over its vocabulary for deciding the next token, given a partial sentence. To ensure that the output sentence remains faithful to the input sentence, we design a minimal distortion model that utilizes pronunciation or shape similarities between the original and replaced characters. Furthermore, we propose two useful reward strategies to address practical challenges specific to the CSC task. Experiments on five public datasets demonstrate that our approach significantly improves LLM performance, enabling them to compete with state-of-the-art domain-general CSC models.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2410.04027")
get_code_for_paper("2410.04027")
have("2410.04027")
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