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97              <span class="font-weight-bold">Elisabeth</span>
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236<h2 class="bibliography">2026</h2>
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242          <abbr class="badge rounded w-100" style="background-color:#001a3a">
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244              <a href="https://colm2026.org/" rel="external nofollow noopener" target="_blank">COLM</a>
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254  <div id="kirsten2026epistemic" class="col-sm-8">
255    <!-- Title -->
256    <div class="title">On Epistemic Diversity in Large Language Models</div>
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259      
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261      
262      Elisabeth
263            Kirsten, Nicole C.
264            Krämer, and Muhammad Bilal
265            Zafar
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281      <em>In COLM 2026</em>,  Oct 2026
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332        <p>Large language models (LLMs) are increasingly used not only to retrieve information, but to answer questions, explain, teach, and support inquiry. In such settings, evaluation cannot be exhausted by accuracy or alignment alone. A system may give a correct answer while still narrowing users’ access %to knowledge. to alternative valid answers, explanations, or reasoning routes. Drawing on the broader notion of epistemic diversity in philosophy and social epistemology, we formalize it in the context of LLMs as the range of valid answers, explanations, and reasoning routes that an LLM exposes to users. We argue that epistemic diversity is a useful evaluation dimension for settings where LLMs are used to support knowledge-intensive tasks. We propose a preliminary framework for conceptualizing and measuring epistemic diversity in LLMs, and operationalize it in two domains. We find that frontier LLMs often exhibit epistemic narrowness, repeatedly collapsing large valid answer spaces onto small canonical subsets. These findings suggest that LLM evaluation should move beyond accuracy-oriented paradigms and treat epistemic diversity as an important dimension of model capability.</p>
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358  <div id="kirsten2025characterizing" class="col-sm-8">
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360    <div class="title">Characterizing Web Search in The Age of Generative AI</div>
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363      
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366      Elisabeth
367            Kirsten, Jost Große
368            Perdekamp, Qinyuan
369            Wu, and
370        <span class="more-authors" title="click to view 3 more authors" onclick="
371              var element = $(this);
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373              var more_authors_text = element.text() == '3 more authors' ? 'Mihir Upadhyay, Krishna P. Gummadi, Muhammad Bilal Zafar' : '3 more authors';
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398      <em>In Findings of the Association for Computational Linguistics: ACL 2026</em>,  Jul 2026
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449        <p>The advent of LLMs has given rise to generative search, a new search paradigm in which LLMs retrieve information from the web related to a query and synthesize it into a single, coherent response. This paradigm differs fundamentally from traditional web search, where results are returned as a ranked list of independent web pages. In this paper, we ask: Along what dimensions does generative search differ from traditional search?We conduct a systematic comparison between Google organic search and five generative search systems from three providers: Google, OpenAI, and Perplexity. Our analysis reveals substantial variation among engines in their reliance on internal v.s. external knowledge, source diversity, and stability. While generative systems often achieve topical coverage comparable to traditional search, they do so using markedly different retrieval footprints and synthesis strategies. We further show that the outputs of generative search can vary across time and executions, raising new challenges for robustness. Our findings demonstrate that generative search introduces new dimensions that are not captured by existing evaluation paradigms, motivating the development of evaluations that explicitly account for retrieval behavior, synthesis, and stability in generative search systems.</p>
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477    <div class="title">The algorithmic self-portrait: Deconstructing memory in ChatGPT<
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480      
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482      
483      Abhisek
484            Dash, Soumi
485            Das, Elisabeth
486            Kirsten, and
487        <span class="more-authors" title="click to view 6 more authors" onclick="
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490              var more_authors_text = element.text() == '6 more authors' ? 'Qinyuan Wu, Sai Keerthana Karnam, Krishna P Gummadi, Thorsten Holz, Muhammad Bilal Zafar, Savvas Zannettou' : '6 more authors';
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515      <em>In Proceedings of the ACM Web Conference 2026</em>,  Jul 2026
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540        <a href="https://github.com/SoumiDas/Memories_WWW_2026/" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a>
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566        <p>To enable personalized and context-aware interactions, conversational AI systems have introduced a new mechanism: Memory. Memory creates what we refer to as the Algorithmic Self-portrait —a new form of personalization derived from users’ self-disclosed information divulged within private conversations. While memory enables more coherent exchanges, the underlying processes of memory creation remain opaque, raising critical questions about data sensitivity, user agency, and the fidelity of the resulting portrait.To bridge this research gap, we analyze 2,050 memory entries from 80 real-world ChatGPT users. Our analyses reveal three key findings: (1) a striking 96% of memories in our dataset are created unilaterally by the conversational system, potentially shifting agency away from the user; (2) Memories, in our dataset, contain a rich mix of GDPR-defined personal data (in 28% memories) along with psychological insights about participants (in 52% memories); and (3) A significant majority of the memories (84%) are directly grounded in user context, indicating faithful representation of the conversations. Finally, we introduce a framework— Attribution Shield —that anticipates these inferences, alerts about potentially sensitive memory inferences, and suggests query reformulations to protect personal information without sacrificing utility.</p>
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595  <div id="neumann2025position" class="col-sm-8">
596    <!-- Title -->
597    <div class="title">Position is Power: System Prompts as a Mechanism of Bias in Large Language Models (LLMs)</div>
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600      
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602      
603      Anna
604            Neumann, Elisabeth
605            Kirsten, Muhammad Bilal
606            Zafar, and
607        <span class="more-authors" title="click to view 1 more author" onclick="
608              var element = $(this);
609              element.attr('title', '');
610              var more_authors_text = element.text() == '1 more author' ? 'Jatinder Singh' : '1 more author';
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635      <em>In Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency</em>,  Jul 2025
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653          <a href="https://dl.acm.org/doi/pdf/10.1145/3715275.3732038" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a>
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684        <p>System prompts in Large Language Models (LLMs) are predefined directives that guide model behaviour, taking precedence over user inputs in text processing and generation. LLM deployers increasingly use them to ensure consistent responses across contexts. While model providers set a foundation of system prompts, deployers and third-party developers can append additional prompts without visibility into others’ additions, while this layered implementation remains entirely hidden from end-users. As system prompts become more complex, they can directly or indirectly introduce unaccounted for side effects. This lack of transparency raises fundamental questions about how the position of information in different directives shapes model outputs. As such, this work examines how the placement of information affects model behaviour. To this end, we compare how models process demographic information in system versus user prompts across six commercially available LLMs and 50 demographic groups. Our analysis reveals significant biases, manifesting in differences in user representation and decision-making scenarios. Since these variations stem from inaccessible and opaque system-level configurations, they risk representational, allocative and potential other biases and downstream harms beyond the user’s ability to detect or correct. Our findings draw attention to these critical issues, which have the potential to perpetuate harms if left unexamined. Further, we argue that system prompt analysis must be incorporated into AI auditing processes, particularly as customisable system prompts become increasingly prevalent in commercial AI deployments.</p>
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710  <div id="kirsten-etal-2025-impact" class="col-sm-8">
711    <!-- Title -->
712    <div class="title">The Impact of Inference Acceleration on Bias of LLMs</div>
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715      
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717      
718      Elisabeth
719            Kirsten, Ivan
720            Habernal, Vedant
721            Nanda, and
722        <span class="more-authors" title="click to view 1 more author" onclick="
723              var element = $(this);
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725              var more_authors_text = element.text() == '1 more author' ? 'Muhammad Bilal Zafar' : '1 more author';
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750      <em>In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)</em>,  Apr 2025
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801        <p>Last few years have seen unprecedented advances in capabilities of Large Language Models (LLMs). These advancements promise to benefit a vast array of application domains. However, due to their immense size, performing inference with LLMs is both costly and slow. Consequently, a plethora of recent work has proposed strategies to enhance inference eff
801iciency, e.g., quantization, pruning, and caching. These acceleration strategies reduce the inference cost and latency, often by several factors, while maintaining much of the predictive performance measured via common benchmarks. In this work, we explore another critical aspect of LLM performance: demographic bias in model generations due to inference acceleration optimizations. Using a wide range of metrics, we probe bias in model outputs from a number of angles. Analysis of outputs before and after inference acceleration shows significant change in bias. Worryingly, these bias effects are complex and unpredictable. A combination of an acceleration strategy and bias type may show little bias change in one model but may lead to a large effect in another. Our results highlight a need for in-depth and case-by-case evaluation of model bias after it has been modified to accelerate inference.This paper contains prompts and outputs which may be deemed offensive.</p>
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