XufengDuan commited on
Commit
842fa95
1 Parent(s): 616e12e

update scripts

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  1. src/display/about.py +2 -2
src/display/about.py CHANGED
@@ -36,12 +36,12 @@ class Tasks(Enum):
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  # Your leaderboard name
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- TITLE = """<h1 align="center" id="space-title">Humanlike Evaluation Model (HEM) leaderboard</h1>"""
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  # What does your leaderboard evaluate?
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  INTRODUCTION_TEXT = """
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  Welcome to the Humanlikeness Leaderboard, curated by [Xufeng Duan](https://xufengduan.github.io/). This platform rigorously evaluates the alignment between human and model responses across five key aspects of language: sounds, words, syntax, meaning, and discourse, to quantify a model's humanlikeness.<br><br>
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- Data from 2000 human participants have been collected, creating response distributions that reflect natural human behavior. Identical stimuli are then presented to advanced language models, generating response distributions for comparison.<br><br>
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  The congruence between human and model responses provides a precise measure of the model's humanlikeness, offering critical insights into how closely these models mirror human cognitive processes.<br>
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  """
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  # Your leaderboard name
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+ TITLE = """<h1 align="center" id="space-title">Humanlikeness(HL) leaderboard</h1>"""
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  # What does your leaderboard evaluate?
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  INTRODUCTION_TEXT = """
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  Welcome to the Humanlikeness Leaderboard, curated by [Xufeng Duan](https://xufengduan.github.io/). This platform rigorously evaluates the alignment between human and model responses across five key aspects of language: sounds, words, syntax, meaning, and discourse, to quantify a model's humanlikeness.<br><br>
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+ Leveraging a large-scale dataset comprising responses from 2000 human participants, we have created detailed response distributions that capture natural human behavior. These extensive datasets are then compared against response distributions generated by LLMs when presented with identical stimuli.<br><br>
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  The congruence between human and model responses provides a precise measure of the model's humanlikeness, offering critical insights into how closely these models mirror human cognitive processes.<br>
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  """
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