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Update app.py
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app.py
CHANGED
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@@ -5,14 +5,13 @@ st.title("AI Detectability Index (ADI)")
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st.write("As new LLMs continue to emerge at an accelerated pace, the usability of prevailing AGTD techniques might not endure indefinitely. To align with the ever-changing landscape of LLMs, we introduce the AI Detectability Index (ADI), which identifies the discernable range for LLMs based on SoTA AGTD techniques. The hypothesis behind this proposal is that both LLMs and AGTD techniques' SoTA benchmarks can be regularly updated to adapt to the evolving landscape. Additionally, ADI serves as a litmus test to gauge whether contemporary LLMs have surpassed the ADI benchmark and are thereby rendering themselves impervious to detection, or whether new methods for AI-generated text detection will require the ADI standard to be reset and re-calibrated.")
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import streamlit as st
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# Create two columns
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col1, col2 = st.columns([0.4, 0.6])
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# Add text to the left column (40% area)
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with col1:
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st.write("Watermarking: Watermarking AI-generated text,
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first proposed by Wiggers (2022), entails the incorporation of an imperceptible signal to establish the
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authorship of a specific text with a high degree of
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certainty. This approach is analogous to encryption
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@@ -30,8 +29,7 @@ with col1:
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methods can be circumvented, albeit with a slight
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decrease in de-watermarking accuracy observed
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with wv2. These results further strengthen our contention that text watermarking is fragile and lacks
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reliability for real-life applications.
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")
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st.write("You can add more text or components here.")
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@@ -39,62 +37,4 @@ with col1:
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with col2:
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st.write("This is the right column with 60% area.")
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st.write("You can add more text or components here as well.")
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st.markdown("""
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<style>
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table th:first-of-type {
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width: 250px;
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}
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table th:nth-of-type(2) {
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width: 600px;
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}
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</style>
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""", unsafe_allow_html=True)
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st.markdown("""
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<table>
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<tr>
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<th>Calculate perplexity</th>
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<th>Perplexity</th>
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</tr>
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<tr>
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<td><img src="https://via.placeholder.com/300x200.png?text=Image+1"></td>
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<td><img src="https://via.placeholder.com/400x200.png?text=Image+2"></td>
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</tr>
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</table>
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""", unsafe_allow_html=True)
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st.markdown("""
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<table>
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<tr>
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<th>Brustiness</th>
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<th><img src="https://via.placeholder.com/300x200.png?text=Image+1"><br><img src="https://via.placeholder.com/400x200.png?text=Image+2"></th>
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</tr>
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</table>
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""", unsafe_allow_html=True)
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st.markdown("""
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<table>
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<tr>
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<th>NLC</th>
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<th><img src="https://via.placeholder.com/420x150.png?text=Image+3"><br><img src="https://via.placeholder.com/500x150.png?text=Image+4"></th>
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</tr>
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</table>
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""", unsafe_allow_html=True)
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st.markdown("""
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<table>
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<tr>
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<th>Stylometry</th>
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<th>
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<p>Perplexity</p>
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<img src="https://via.placeholder.com/300x200.png?text=Image+5">
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<img src="https://via.placeholder.com/300x200.png?text=Image+5">
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<p>Human</p>
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<p>AI</p>
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<p>Brustiness</p>
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</th>
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</tr>
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</table>
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""", unsafe_allow_html=True)
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st.write("As new LLMs continue to emerge at an accelerated pace, the usability of prevailing AGTD techniques might not endure indefinitely. To align with the ever-changing landscape of LLMs, we introduce the AI Detectability Index (ADI), which identifies the discernable range for LLMs based on SoTA AGTD techniques. The hypothesis behind this proposal is that both LLMs and AGTD techniques' SoTA benchmarks can be regularly updated to adapt to the evolving landscape. Additionally, ADI serves as a litmus test to gauge whether contemporary LLMs have surpassed the ADI benchmark and are thereby rendering themselves impervious to detection, or whether new methods for AI-generated text detection will require the ADI standard to be reset and re-calibrated.")
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# Create two columns
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col1, col2 = st.columns([0.4, 0.6])
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# Add text to the left column (40% area)
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with col1:
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st.write("""Watermarking: Watermarking AI-generated text,
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first proposed by Wiggers (2022), entails the incorporation of an imperceptible signal to establish the
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authorship of a specific text with a high degree of
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certainty. This approach is analogous to encryption
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methods can be circumvented, albeit with a slight
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decrease in de-watermarking accuracy observed
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with wv2. These results further strengthen our contention that text watermarking is fragile and lacks
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reliability for real-life applications.""")
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st.write("You can add more text or components here.")
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with col2:
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st.write("This is the right column with 60% area.")
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st.write("You can add more text or components here as well.")
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