Rui Xu, Xintao Wang, Jiangjie Chen, Siyu Yuan, Harry Potter, Deqing Yang, Xinfeng Yuan, Hermione Granger, Ron Weasley, Tianhe Lin, Yuhan Cui
We lifted 4 functions out of this paper's own repositories and ran 2 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 |
|---|---|---|
| joanna0123/character_profiling | canonical | 2 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| divide_str | Ran | joanna0123/character_profiling/code/epub2json.py code served (permissive licence) · get_code("582019a30a2fc5ae") |
| strong_divide | Ran | joanna0123/character_profiling/code/epub2json.py code served (permissive licence) · get_code("91af9b151098eb49") |
| evaluation | Not yet run | joanna0123/character_profiling/code/evaluation_score.py code served (permissive licence) · get_code("72ce9df219e77438") |
| gpt4_evaluator | Not yet run | joanna0123/character_profiling/code/evaluation_score.py code served (permissive licence) · get_code("daad9af96ec66f8f") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Large language models (LLMs) have demonstrated impressive performance and spurred numerous AI applications, in which role-playing agents (RPAs) are particularly popular, especially for fictional characters. The prerequisite for these RPAs lies in the capability of LLMs to understand characters from fictional works. Previous efforts have evaluated this capability via basic classification tasks or characteristic imitation, failing to capture the nuanced character understanding with LLMs. In this paper, we propose evaluating LLMs' character understanding capability via the character profiling task, i.e., summarizing character profiles from corresponding materials, a widely adopted yet understudied practice for RPA development. Specifically, we construct the CROSS dataset from literature experts and assess the generated profiles by comparing them with ground truth references and evaluating their applicability in downstream tasks. Our experiments, which cover various summarization methods and LLMs, have yielded promising results. These results strongly validate the character understanding capability of LLMs. Resources are available at https://github. com/Joanna0123/character_profiling.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2404.12726")
get_code_for_paper("2404.12726")
have("2404.12726")
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