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+            alt="HSE"
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+            class="header__participants-logo"
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+        </div>
+        <h1 class="header-projectName">
+          Fact-Checking the Output of Large Language Models via Token-Level
+          Uncertainty Quantification.
+        </h1>
+        <div class="header__link-container">
+          <div class="linkContainer__authors">
+            <a href="">Ekaterina Fadeeva<sup>3,4</sup></a>
+            <a href="">Aleksandr Rubashevskii<sup>1,3</sup></a>
+            <a href="">Artem Shelmanov<sup>1</sup></a>
+            <a href="">Sergey Petrakov<sup>3</sup></a>
+            <a href="">Haonan Li<sup>1</sup></a>
+            <a href="">Hamdy Mubarak<sup>7</sup></a>
+            <a href="">Evgenii Tsymbalov<sup>8</sup></a>
+            <a href="">Gleb Kuzmin<sup>2,5</sup></a>
+            <a href="">Alexander Panchenko<sup>2,3</sup></a>
+            <a href="">Timothy Baldwin<sup>1,6</sup></a>
+            <a href="">Preslav Nakov<sup>1</sup></a>
+            <a href="">Maxim Panov<sup>1</sup></a>
+          </div>
+          <div class="linkContainer__institutes">
+            <a href=""><sup>1</sup>MBZUAI</a>
+            <a href=""><sup>2</sup>AIRI</a>
+            <a href=""
+              ><sup>3</sup>Center for Artificial Intelligence Technology</a
+            >
+            <a href=""><sup>4</sup>HSE University</a>
+            <a href=""><sup>5</sup>FRC CSC RAS</a>
+            <a href=""><sup>6</sup>The University of Melbourne</a>
+            <a href=""><sup>7</sup>QCRI</a>
+            <a href=""><sup>8</sup>Independent Researcher</a>
+          </div>
+        </div>
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+            <img
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+      <aside class="sidebar-section">
+        <div class="sidebar">
+          <ul class="nav flex-column">
+            <li class="nav-item" data-target="#header">
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+            <li class="nav-item" data-target="#diagrams-section">
+              <a class="nav-link" href="#diagrams-section">Diagramms</a>
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+              <span class="indicator"></span>
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+            <li class="nav-item" data-target="#numbers-section">
+              <a class="nav-link" href="#numbers-section">Number of claims</a>
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+          </ul>
+        </div>
+      </aside>
+      <section class="text-section" id="text-section">
+        <div class="text-section__container">
+          <h2>Abstract</h2>
+          <p>Large language models (LLMs) are notorious for hallucinating, i. e., producing erroneous claims in&nbsp;their output. Such hallucinations can be&nbsp;dangerous, as&nbsp;occasional factual inaccuracies in&nbsp;the generated text might be&nbsp;obscured by&nbsp;the rest of&nbsp;the output being generally factually correct, making it&nbsp;extremely hard for the users to&nbsp;spot them. Current services that leverage LLMs usually do&nbsp;not provide any means for detecting unreliable generations. Here, we&nbsp;aim to&nbsp;bridge this gap. In&nbsp;particular, we&nbsp;propose a&nbsp;novel <nobr>fact-checking</nobr> and hallucination detection pipeline based on&nbsp;<nobr>token-level</nobr> uncertainty quantification. Uncertainty scores leverage information encapsulated in&nbsp;the output of&nbsp;a&nbsp;neural network or&nbsp;its layers to&nbsp;detect unreliable predictions, and we&nbsp;show that they can be&nbsp;used to&nbsp;<nobr>fact-check</nobr> the atomic claims in&nbsp;the LLM output. Moreover, we&nbsp;present a&nbsp;novel <nobr>token-level</nobr> uncertainty quantification method that removes the impact of&nbsp;uncertainty about what claim to&nbsp;generate on&nbsp;the current step and what surface form to&nbsp;use. Our method Claim Conditioned Probability (CCP) measures only the uncertainty of&nbsp;a&nbsp;particular claim value expressed by&nbsp;the model. Experiments on&nbsp;the task of&nbsp;biography generation demonstrate strong improvements for CCP compared to&nbsp;the baselines for seven LLMs and four languages. Human evaluation reveals that the <nobr>fact-checking</nobr> pipeline based on&nbsp;uncertainty quantification is&nbsp;competitive with a&nbsp;<nobr>fact-checking</nobr> tool that leverages external knowledge.</p>
+        </div>
+      </section>
+      <section class="video-section" id="video-section">
+        <div class="video-container">
+          <h2>Video</h2>
+          <div class="video-wrapper">
+            <iframe
+              class="iframe"
+              src="https://rutube.ru/play/embed/c6cc4d620b1d4338901770a44b3e82f4/"
+              frameborder="0"
+              allow="clipboard-write; autoplay"
+              webkitAllowFullScreen
+              mozallowfullscreen
+              allowfullscreen
+            ></iframe>
+          </div>
+        </div>
+      </section>
+      <section class="text-section-border" id="text-section-border">
+        <div class="text-section-border__container">
+          <h2>Introduction</h2>
+          <p> Large language models (LLMs) have become a&nbsp;ubiquitous and versatile tool for addressing a&nbsp;variety of&nbsp;natural language processing (NLP) tasks. People use these models for tasks including information search Sun et&nbsp;al. (2023b), to&nbsp;ask medical questions Thirunavukarasu et&nbsp;al. (2023), or&nbsp;to&nbsp;generate new content Sun et&nbsp;al. (2023a). Recently, there has been a&nbsp;notable shift in&nbsp;user behavior, indicating an&nbsp;increasing reliance on&nbsp;and trust in&nbsp;LLMs as&nbsp;primary information sources, often surpassing traditional channels. However, a&nbsp;significant challenge with the spread of&nbsp;these models is&nbsp;their tendency to&nbsp;produce &laquo;hallucinations&raquo;, i.e., factually incorrect generations that contain misleading information Bang et&nbsp;al. (2023); Dale et&nbsp;al. (2023). This is&nbsp;a&nbsp;<nobr>side-effect</nobr> of&nbsp;the way modern LLMs are designed and trained Kalai and Vempala (2023). </p> <p> LLM hallucinations are a&nbsp;major concern because the deceptive content at&nbsp;the surface level can be&nbsp;highly coherent and persuasive. Common examples include the creation of&nbsp;fictitious biographies or&nbsp;the assertion of&nbsp;unfounded claims. The danger is&nbsp;that a&nbsp;few occasional false claims might be&nbsp;easily obscured by&nbsp;a&nbsp;large number of&nbsp;factual statements, making it&nbsp;extremely hard for people to&nbsp;spot them. As&nbsp;hallucinations in&nbsp;LLM outputs are hard to&nbsp;eliminate completely, users of&nbsp;such systems could be&nbsp;informed via highlighting some potential caveats in&nbsp;the text, and this is&nbsp;where our approach can help. </p> <p> <nobr>Fact-checking</nobr> is&nbsp;a&nbsp;research direction that addresses this problem. It&nbsp;is&nbsp;usually approached using complex systems that leverage external knowledge sources Guo et&nbsp;al. (2022); Nakov et&nbsp;al. (2021); Wadden et&nbsp;al. (2020). This introduces problems related to&nbsp;the incomplete nature of&nbsp;such sources and notable overhead in&nbsp;terms of&nbsp;storing the knowledge. We&nbsp;argue that information about whether a&nbsp;generation is&nbsp;a&nbsp;hallucination is&nbsp;encapsulated in&nbsp;the model output itself, and can be&nbsp;extracted using uncertainty quantification (UQ) Gal et&nbsp;al. (2016); Kotelevskii et&nbsp;al. (2022); Vazhentsev et&nbsp;al. (2022, 2023a). This avoids implementing complex and expensive <nobr>fact-checking</nobr> systems that require additional computational overhead and rely on&nbsp;external resources. </p> <p> Prior work has mainly focused on&nbsp;quantification of&nbsp;uncertainty for the whole generated text and been mostly limited to&nbsp;tasks such as&nbsp;machine translation Malinin and Gales (2020), question answering Kuhn et&nbsp;al. (2023), and text summarization van der Poel et&nbsp;al. (2022). However, the need for an&nbsp;uncertainty score for only a&nbsp;part of&nbsp;the generation substantially complicates the problem. We&nbsp;approach it&nbsp;by&nbsp;leveraging <nobr>token-level</nobr> uncertainty scores and aggregating them into <nobr>claim-level</nobr> scores. Moreover, we&nbsp;introduce a&nbsp;new <nobr>token-level</nobr> uncertainty score, namely <nobr>claim-conditioned</nobr> probability (CCP), which demonstrates confident improvements over several baselines for seven LLMs and four languages. </p> <p> To&nbsp;the best of&nbsp;our knowledge, there is&nbsp;no&nbsp;previous work that has investigated the quality of&nbsp;<nobr>claim-level</nobr> UQ&nbsp;techniques for LLM generation. Therefore, for this purpose, we&nbsp;construct a&nbsp;novel benchmark based on&nbsp;<nobr>fact-checking</nobr> of&nbsp;biographies of&nbsp;individuals generated using a&nbsp;range of&nbsp;LLMs. Note that different LLMs produce different outputs, which generally have higher variability than, e.g., outputs in&nbsp;such tasks as&nbsp;machine translation or&nbsp;question answering. Therefore, we&nbsp;compare the predictions and uncertainty scores to&nbsp;the results of&nbsp;an&nbsp;automatic external <nobr>fact-checking</nobr> system FactScore Min et&nbsp;al. (2023). Human evaluation verifies that our constructed benchmark based on&nbsp;FactScore can adequately evaluate the performance of&nbsp;the uncertainty scores. </p> <p>Our contributions are as&nbsp;follows:</p> <ul> <li> We&nbsp;propose a&nbsp;novel framework for <nobr>fact-checking</nobr> LLM generations using <nobr>token-level</nobr> uncertainty quantification. We&nbsp;provide a&nbsp;procedure for efficiently estimating the uncertainty of&nbsp;atomic claims generated by&nbsp;a&nbsp;<nobr>white-box</nobr> model and highlighting potentially deceptive fragments by&nbsp;mapping them back to&nbsp;the original response. </li> <li> We&nbsp;propose a&nbsp;novel method for <nobr>token-level</nobr> uncertainty quantification that outperforms baselines and can be&nbsp;used as&nbsp;a&nbsp;<nobr>plug-in</nobr> a&nbsp;<nobr>fact-checking</nobr> framework. </li> <li> We&nbsp;design a&nbsp;novel approach to&nbsp;evaluation of&nbsp;<nobr>token-level</nobr> UQ&nbsp;methods for <nobr>white-box</nobr> LLMs based on&nbsp;<nobr>fact-checking</nobr>, which can be&nbsp;applied to&nbsp;other <nobr>white-box</nobr> LLMs. </li> <li> We&nbsp;provide an&nbsp;empirical and ablation analysis of&nbsp;the method for <nobr>fact-checking</nobr> of&nbsp;LLM generations, and find that the uncertainty scores we&nbsp;produce can help to&nbsp;spot claims with factual errors for seven LLMs over four languages: English, Chinese, Arabic, and Russian. </li> <li> The method is&nbsp;implemented as&nbsp;a&nbsp;part of&nbsp;the <nobr>LM-Polygraph</nobr> library Fadeeva et&nbsp;al. (2023). All the code and data for experiments is&nbsp;publicly available1. </li>
+          </ul>
+        </div>
+      </section>
+      <section class="swiper-section" id="swiper-section">
+        <div class="swiper-container">
+          <h2>Swiper</h2>
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+        <div class="lighbox-container">
+          <h2>Lightbox</h2>
+          <div class="lighbox-row">
+            <img src="public/x5.png" alt="" class="thumbnail" id="openLightbox1" />
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+        </div>
+      </section>
+      <section class="before-after-section" id="before-after-section">
+        <h2>Before-after</h2>
+        <div class="compare-container">
+          <div class="compare-images">
+            <img
+              src="public/flower1.jpg"
+              alt="Before"
+              class="compare-img before-img"
+            />
+            <img
+              src="public/flower2.jpg"
+              alt="After"
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+            />
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+        <div class="swiper-on-button__container">
+          <h2>Swiper with buttons</h2>
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+              Slide 1
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+            <button class="swiper-button" data-slide-to="1">Slide 2</button>
+            <button class="swiper-button" data-slide-to="2">Slide 3</button>
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+          <div class="swiper my-swiper-on-button">
+            <div class="swiper-wrapper">
+              <div class="swiper-slide">
+                <canvas
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+      </section>
+      <section class="diagrams-section" id="diagrams-section">
+        <div class="diagrams-section__container">
+          <h2>Charts</h2>
+          <canvas class="myChart" id="BarChart"></canvas>
+          <p class="caption">
+            The comparison of token-level uncertainty quantification methods in
+            terms of ROC-AUC scores, measured for Chinese dataset. The results
+            are split into bins when considering only facts from the first 2, 5,
+            and all sentences.
+          </p>
+          <canvas class="myChart" id="LineChart"></canvas>
+          <p class="caption">
+            The comparison of token-level uncertainty quantification methods in
+            terms of ROC-AUC scores, measured for Chinese dataset. The results
+            are split into bins when considering only facts from the first 2, 5,
+            and all sentences.
+          </p>
+          <canvas class="myChart" id="PieChart"></canvas>
+          <p class="caption">
+            The comparison of token-level uncertainty quantification methods in
+            terms of ROC-AUC scores, measured for Chinese dataset. The results
+            are split into bins when considering only facts from the first 2, 5,
+            and all sentences.
+          </p>
+        </div>
+      </section>
+      <section class="table-section" id="table-section">
+        <h2>
+          ROC-AUC of claim-level UQ methods with manual annotation as the ground
+          truth.
+        </h2>
+        <div class="table-section__container">
+          <table>
+            <thead>
+              <th>Model</th>
+              <th>Yi 6b, Chinese</th>
+              <th>Jais 13b, Arabic</th>
+              <th>GPT-4, Arabic</th>
+              <th>Vikhr 7b, Russian</th>
+            </thead>
+            <tbody>
+              <tr>
+                <td>CCP (ours)</td>
+                <td>0.64 ± 0.03</td>
+                <td>0.66 ± 0.02</td>
+                <td>0.56 ± 0.05</td>
+                <td>0.68 ± 0.04</td>
+              </tr>
+              <tr>
+                <td>Maximum Prob.</td>
+                <td>0.52 ± 0.03</td>
+                <td>0.59 ± 0.02</td>
+                <td>0.55 ± 0.08</td>
+                <td>0.63 ± 0.04</td>
+              </tr>
+              <tr>
+                <td>Perplexity</td>
+                <td>0.51 ± 0.04</td>
+                <td>0.56 ± 0.02</td>
+                <td>0.54 ± 0.08</td>
+                <td>0.58 ± 0.04</td>
+              </tr>
+              <tr>
+                <td>Token Entropy</td>
+                <td>0.51 ± 0.04</td>
+                <td>0.56 ± 0.02</td>
+                <td>0.54 ± 0.08</td>
+                <td>0.58 ± 0.04</td>
+              </tr>
+              <tr>
+                <td>P(True)</td>
+                <td>0.52 ± 0.03</td>
+                <td>0.59 ± 0.02</td>
+                <td>0.55 ± 0.08</td>
+                <td>0.63 ± 0.04</td>
+              </tr>
+            </tbody>
+          </table>
+        </div>
+      </section>
+      <section class="numbers-section" id="numbers-section">
+        <div class="numbers-section__container">
+          <h2>Number of claims</h2>
+          <div class="numbers__container">
+            <div class="number__card">
+              <h1>100</h1>
+              <p>Vicuna 13b, English</p>
+            </div>
+            <div class="number__card">
+              <h1>1,603</h1>
+              <p>Yi 6b, Chinese</p>
+            </div>
+            <div class="number__card">
+              <h1>200</h1>
+              <p>GPT-4, Arabic</p>
+            </div>
+            <div class="number__card">
+              <h1>146</h1>
+              <p>Vikhr 7b, Russian</p>
+            </div>
+          </div>
+        </div>
+      </section>
+      <section class="columns-section" id="columns-section">
+        <div class="columns-section__container">
+          <h2>Datasets and Statistics</h2>
+          <div class="row g-6 justify-content-between">
+            <div class="col-md-6 p-2">
+              <div class="column">
+                <p>For Arabic, using <nobr>GPT-4</nobr>, we&nbsp;generate 100 biographies of&nbsp;people randomly selected from the list of&nbsp;the most visited websites in&nbsp;Arabic Wikipedia. The used Arabic prompt is&nbsp;the translation of: &laquo;Tell me&nbsp;the biography of&nbsp;{person name}&raquo;. To&nbsp;extract claims, we&nbsp;prompt <nobr>GPT-4</nobr> in&nbsp;the following way: &laquo;Convert the following biography into Arabic atomic factual claims that can be&nbsp;verified, one claim per line. Biography is: {biography}&raquo;. Arabic biographies and claims are translated into English using Google Translate. It&nbsp;is&nbsp;worth mentioning that almost <nobr>one-third</nobr> of&nbsp;the names in&nbsp;the list of&nbsp;person names are foreign</p>
+              </div>
+            </div>
+            <div class="col-md-6 p-2">
+              <div class="column">
+                <p>For Jais 13b experiments, we&nbsp;use the same prompts used for <nobr>GPT-4</nobr>. We&nbsp;notice that the biographies generated by&nbsp;Jais 13b are much shorter than the ones generated by&nbsp;<nobr>GPT-4</nobr> (almost <nobr>half-length</nobr>). Similarly, we&nbsp;use <nobr>GPT-4</nobr> to&nbsp;extract claims from the generated biographies. On&nbsp;average, biographies generated by&nbsp;Jais 13b have nine claims. Jais 13b generates empty biographies for seven names (out of&nbsp;100) with response messages like: &laquo;I&nbsp;am&nbsp;sorry! I&nbsp;cannot provide information about {name}&raquo;, or&nbsp;&laquo;What do&nbsp;you want to&nbsp;know exactly?&raquo;. Two random claims from each biography are verified manually (total = 186 claims).</p>
+              </div>
+            </div>
+          </div>
+          <div class="row g-6 justify-content-between">
+            <div class="col-md-4 p-2">
+              <div class="column">
+                <p>Since FactScore only supports English, for Arabic, Chinese, and Russian, we&nbsp;generate biographies of&nbsp;<nobr>well-known</nobr> people and annotate them only manually. We&nbsp;also manually annotate English claims generated by&nbsp;Vicuna 13b. The statistics for annotated datasets are presented in&nbsp;Table 7.</p>
+              </div>
+            </div>
+            <div class="col-md-4 p-2">
+              <div class="column">
+                <p>For Chinese, we&nbsp;first prompt ChatGPT to&nbsp;generate a&nbsp;list of&nbsp;famous people. Then use the same way as&nbsp;we&nbsp;have done Arabic, but change the prompt to&nbsp;Chinese, to&nbsp;biographies and claims. We&nbsp;use Yi&nbsp;6b to&nbsp;generate texts <nobr>GPT-4</nobr> to&nbsp;split them into atomic claims.</p>
+              </div>
+            </div>
+            <div class="col-md-4 p-2">
+              <div class="column">
+                <p>For Russian, we&nbsp;conduct a&nbsp;similar approach, prompting to&nbsp;generate a&nbsp;list of&nbsp;100 famous people and checking result to&nbsp;obtain representative personalities from different areas such as&nbsp;science, sport, literature, art, activity, cinematography, heroes, etc.</p>
+              </div>
+            </div>
+          </div>
+        </div>
+      </section>
+      <section class="dropdown-section" id="dropdown-section">
+        <div class="dropdown-section__container">
+          <h2>Ablation Studies</h2>
+          <div class="dropdown__row">
+            <div class="dropdown" data-dropdown>
+              <button class="dropdown__button">
+                Aggregation of C⁢C⁢Pword for obtaining C⁢C⁢Pclaim.
+              </button>
+              <div class="dropdown__content">
+                <div class="withline__container">
+                  <span class="vertical-line blue-line"></span>
+                  <div class="dropdown__item" data-value="opt1-1">
+                    Besides the product of probabilities, we also tried the
+                    normalized product, minimum, and average probability. All
+                    these approaches perform slightly worse than the product
+                    (see Table 9).
+                  </div>
+                </div>
+              </div>
+            </div>
+            <div class="dropdown" data-dropdown>
+              <button class="dropdown__button">NLI model</button>
+              <div class="dropdown__content">
+                <div class="withline__container">
+                  <span class="vertical-line red-line"></span>
+                  <div class="dropdown__item" data-value="opt2-1">
+                    We investigate the influence of the specific NLI model on
+                    the performance of CCP. Table 10 shows that CCP’s
+                    effectiveness is not critically dependent on the complexity
+                    of the NLI model employed. Notably, even a relatively small
+                    model with 22M parameters maintains strong performance
+                    without any degradation.
+                  </div>
+                </div>
+              </div>
+            </div>
+            <div class="dropdown" data-dropdown>
+              <button class="dropdown__button">NLI context</button>
+              <div class="dropdown__content">
+                <div class="withline__container">
+                  <span class="vertical-line green-line"></span>
+                  <div class="dropdown__item" data-value="opt3-1">
+                    We analyze what context is sufficient for NLI in CCP (Table
+                    11). In addition to the standard variant in CCP, where we
+                    keep the claim that precedes the word in question, we
+                    experiment with a single target word without context and the
+                    whole sentence that precedes the target word. All variants
+                    demonstrate lower performance. No context results in a drop
+                    of 0.02 ROC-AUC, and longer contexts – of more than 0.07.
+                  </div>
+                </div>
+              </div>
+            </div>
+          </div>
+        </div>
+      </section>
+      <section class="code-section" id="code-section">
+        <div class="code-section__container">
+          <h2>Code</h2>
+          <pre><code><div class="code__container" ><p class="code">items.forEach((item) => {
+        item.addEventListener('click', function () {
+        const value = this.textContent
+        selectedValue.textContent = value
+        dropdown.classList.remove('open')
+    })
+})</p></div></code></pre>
+        </div>
+      </section>
+      <section class="bibTeX-section" id="bibTeX-section">
+        <div class="bibTeX-section__container">
+          <h2>BibTeX</h2>
+          <pre><code><div class="bibTeX__container" ><p class="bibTeX">@misc{salnikov2025geopolitical,
+    title={Geopolitical biases in LLMs: what are the "good" and the "bad" countries according to contemporary language models},
+    author={Mikhail Salnikov and Dmitrii Korzh and Ivan Lazichny and Elvir Karimov and Artyom Iudin and Ivan Oseledets and Oleg Y. Rogov and Alexander Panchenko and Natalia Loukachevitch and Elena Tutubalina},
+    year={2025},
+    eprint={2506.06751},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL},
+    url={https://arxiv.org/abs/2506.06751}
+}</p></div></code></pre>
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