app.py

import streamlit as st
st.set_page_config(layout="wide")

st.title("AI Detectability Index (ADI)")

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.")

import streamlit as st

# Create two columns
col1, col2 = st.columns([0.4, 0.6])

# Add text to the left column (40% area)
with col1:
    st.write("Watermarking: Watermarking AI-generated text,
    first proposed by Wiggers (2022), entails the incorporation of an imperceptible signal to establish the
    authorship of a specific text with a high degree of
    certainty. This approach is analogous to encryption
    and decryption. Kirchenbauer et al. (2023a) (wv1)
    were the first to present operational watermarking models for LLMs, but their initial proposal
    faced criticism. Sadasivan et al. (2023) shared
    their initial studies suggesting that paraphrasing
    can efficiently eliminate watermarks. In a subsequent paper (Kirchenbauer et al., 2023b) (wv2), 
    the authors put forth evidently more resilient watermarking techniques, asserting that paraphrasing
    does not significantly disrupt watermark signals
    in this iteration of their research. By conducting
    extensive experiments (detailed in Section 3), our
    study provides a thorough investigation of the dewatermarking techniques wv1 and wv2, 
    demonstrating that the watermarked texts generated by both
    methods can be circumvented, albeit with a slight
    decrease in de-watermarking accuracy observed
    with wv2. These results further strengthen our contention that text watermarking is fragile and lacks
    reliability for real-life applications.
    ")
    
    st.write("You can add more text or components here.")

# Add text to the right column (60% area)
with col2:
    st.write("This is the right column with 60% area.")
    st.write("You can add more text or components here as well.")
    

st.markdown("""
<style>
table th:first-of-type {
    width: 250px;
}
table th:nth-of-type(2) {
    width: 600px;
}
</style>
""", unsafe_allow_html=True)

st.markdown("""
<table>
    <tr>
        <th>Calculate perplexity</th>
        <th>Perplexity</th>
    </tr>
    <tr>
        <td><img src="https://via.placeholder.com/300x200.png?text=Image+1"></td>
        <td><img src="https://via.placeholder.com/400x200.png?text=Image+2"></td>
    </tr>
</table>
""", unsafe_allow_html=True)

st.markdown("""
<table>
    <tr>
        <th>Brustiness</th>
        <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>
    </tr>
</table>
""", unsafe_allow_html=True)

st.markdown("""
<table>
    <tr>
        <th>NLC</th>
        <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>
    </tr>
</table>
""", unsafe_allow_html=True)

st.markdown("""
<table>
    <tr>
        <th>Stylometry</th>
        <th>
            <p>Perplexity</p>
            <img src="https://via.placeholder.com/300x200.png?text=Image+5">
            <img src="https://via.placeholder.com/300x200.png?text=Image+5">
            <p>Human</p>
            <p>AI</p>
            <p>Brustiness</p>
        </th>
    </tr>
</table>
""", unsafe_allow_html=True)