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230 231 232<p><input type="text" id="bibsearch" spellcheck="false" autocomplete="off" class="search bibsearch-form-input" placeholder="Type to filter"></p> 233 234<div class="publications"> 235 236<h2 class="bibliography">2026</h2> 237<ol class="bibliography"> 238<li> 239<div class="row"> 240 241 <div class="col col-sm-2 abbr"> 242 <abbr class="badge rounded w-100" style="background-color:#001a3a"> 243 244 <a href="https://colm2026.org/" rel="external nofollow noopener" target="_blank">COLM</a> 245 246 </abbr> 247 248 249 250 </div> 251 252 253 <!-- Entry bib key --> 254 <div id="kirsten2026epistemic" class="col-sm-8"> 255 <!-- Title --> 256 <div class="title">On Epistemic Diversity in Large Language Models</div> 257 <!-- Author --> 258 <div class="author"> 259 260 261 262 Elisabeth 263 Kirsten, Nicole C. 264 Krämer, and Muhammad Bilal 265 Zafar 266 267 </div> 268 269 <!-- Journal/Book title and date --> 270 271 272 273 274 275 276 277 278 279 280 <div class="periodical"> 281 <em>In COLM 2026</em>, Oct 2026 282 </div> 283 <div class="periodical"> 284 285 </div> 286 287 <!-- Links/Buttons --> 288 <div class="links"> 289 290 291 <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> 292 293 294 295 296 297 298 299 <a href="https://arxiv.org/pdf/2609.04835" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a> 300 301 302 303 304 305 306 <a href="https://github.com/aisoc-lab/llm-epistemic-diversity" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a> 307 308 309 310 311 </div> 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 <!-- Hidden abstract block --> 331 <div class="abstract hidden"> 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 usersâ 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> 333 </div> 334 335 336 337 338 339 </div> 340</div> 341</li> 342<li> 343<div class="row"> 344 345 <div class="col col-sm-2 abbr"> 346 <abbr class="badge rounded w-100" style="background-color:#002f6c"> 347 348 <a href="https://2026.aclweb.org/" rel="external nofollow noopener" target="_blank">ACL Findings</a> 349 350 </abbr> 351 352 353 354 </div> 355 356 357 <!-- Entry bib key --> 358 <div id="kirsten2025characterizing" class="col-sm-8"> 359 <!-- Title --> 360 <div class="title">Characterizing Web Search in The Age of Generative AI</div> 361 <!-- Author --> 362 <div class="author"> 363 364 365 366 Elisabeth 367 Kirsten, Jost GroÃe 368 Perdekamp, Qinyuan 369 Wu, and 370 <span class="more-authors" title="click to view 3 more authors" onclick=" 371 var element = $(this); 372 element.attr('title', ''); 373 var more_authors_text = element.text() == '3 more authors' ? 'Mihir Upadhyay, Krishna P. Gummadi, Muhammad Bilal Zafar' : '3 more authors'; 374 var cursorPosition = 0; 375 var textAdder = setInterval(function(){ 376 element.html(more_authors_text.substring(0, cursorPosition + 1)); 377 if (++cursorPosition == more_authors_text.length){ 378 clearInterval(textAdder); 379 } 380 }, '10'); 381 ">3 more authors</span> 382 383 384 </div> 385 386 <!-- Journal/Book title and date --> 387 388 389 390 391 392 393 394 395 396 397 <div class="periodical"> 398 <em>In Findings of the Association for Computational Linguistics: ACL 2026</em>, Jul 2026 399 </div> 400 <div class="periodical"> 401 402 </div> 403 404 <!-- Links/Buttons --> 405 <div class="links"> 406 407 408 <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> 409 410 411 412 413 414 415 416 <a href="https://arxiv.org/pdf/2510.11560" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a> 417 418 419 420 421 422 423 <a href="https://github.com/aisoc-lab/generative-search-eval" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a> 424 425 426 427 428 </div> 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 <!-- Hidden abstract block --> 448 <div class="abstract hidden"> 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> 450 </div> 451 452 453 454 455 456 </div> 457</div> 458</li> 459<li> 460<div class="row"> 461 462 <div class="col col-sm-2 abbr"> 463 <abbr class="badge rounded w-100" style="background-color:#0046ff"> 464 465 <a href="https://www2026.thewebconf.org/" rel="external nofollow noopener" target="_blank">WWW</a> 466 467 </abbr> 468 469 470 471 </div> 472 473 474 <!-- Entry bib key --> 475 <div id="dash2026algorithmic" class="col-sm-8"> 476 <!-- Title --> 477 <div class="title">The algorithmic self-portrait: Deconstructing memory in ChatGPT<
477/div> 478 <!-- Author --> 479 <div class="author"> 480 481 482 483 Abhisek 484 Dash, Soumi 485 Das, Elisabeth 486 Kirsten, and 487 <span class="more-authors" title="click to view 6 more authors" onclick=" 488 var element = $(this); 489 element.attr('title', ''); 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'; 491 var cursorPosition = 0; 492 var textAdder = setInterval(function(){ 493 element.html(more_authors_text.substring(0, cursorPosition + 1)); 494 if (++cursorPosition == more_authors_text.length){ 495 clearInterval(textAdder); 496 } 497 }, '10'); 498 ">6 more authors</span> 499 500 501 </div> 502 503 <!-- Journal/Book title and date --> 504 505 506 507 508 509 510 511 512 513 514 <div class="periodical"> 515 <em>In Proceedings of the ACM Web Conference 2026</em>, Jul 2026 516 </div> 517 <div class="periodical"> 518 519 </div> 520 521 <!-- Links/Buttons --> 522 <div class="links"> 523 524 525 <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> 526 527 528 529 530 531 532 533 <a href="https://dl.acm.org/doi/10.1145/3774904.3792671" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a> 534 535 536 537 538 539 540 <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> 541 542 543 544 545 </div> 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 <!-- Hidden abstract block --> 565 <div class="abstract hidden"> 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 âa new form of personalization derived from usersâ 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 frameworkâ Attribution Shield âthat anticipates these inferences, alerts about potentially sensitive memory inferences, and suggests query reformulations to protect personal information without sacrificing utility.</p> 567 </div> 568 569 570 571 572 573 </div> 574</div> 575</li> 576</ol> 577<h2 class="bibliography">2025</h2> 578<ol class="bibliography"> 579<li> 580<div class="row"> 581 582 <div class="col col-sm-2 abbr"> 583 <abbr class="badge rounded w-100" style="background-color:#1f77b4"> 584 585 <a href="https://facctconference.org/" rel="external nofollow noopener" target="_blank">FACCT</a> 586 587 </abbr> 588 589 590 591 </div> 592 593 594 <!-- Entry bib key --> 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> 598 <!-- Author --> 599 <div class="author"> 600 601 602 603 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'; 611 var cursorPosition = 0; 612 var textAdder = setInterval(function(){ 613 element.html(more_authors_text.substring(0, cursorPosition + 1)); 614 if (++cursorPosition == more_authors_text.length){ 615 clearInterval(textAdder); 616 } 617 }, '10'); 618 ">1 more author</span> 619 620 621 </div> 622 623 <!-- Journal/Book title and date --> 624 625 626 627 628 629 630 631 632 633 634 <div class="periodical"> 635 <em>In Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency</em>, Jul 2025 636 </div> 637 <div class="periodical"> 638 639 </div> 640 641 <!-- Links/Buttons --> 642 <div class="links"> 643 644 645 <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> 646 647 648 649 650 651 652 653 <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> 654 655 656 657 658 659 660 661 662 663 </div> 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 <!-- Hidden abstract block --> 683 <div class="abstract hidden"> 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 othersâ 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 userâs 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> 685 </div> 686 687 688 689 690 691 </div> 692</div> 693</li> 694<li> 695<div class="row"> 696 697 <div class="col col-sm-2 abbr"> 698 <abbr class="badge rounded w-100" style="background-color:#00369f"> 699 700 <a href="https://2025.naacl.org/" rel="external nofollow noopener" target="_blank">NAACL</a> 701 702 </abbr> 703 704 705 706 </div> 707 708 709 <!-- Entry bib key --> 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> 713 <!-- Author --> 714 <div class="author"> 715 716 717 718 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); 724 element.attr('title', ''); 725 var more_authors_text = element.text() == '1 more author' ? 'Muhammad Bilal Zafar' : '1 more author'; 726 var cursorPosition = 0; 727 var textAdder = setInterval(function(){ 728 element.html(more_authors_text.substring(0, cursorPosition + 1)); 729 if (++cursorPosition == more_authors_text.length){ 730 clearInterval(textAdder); 731 } 732 }, '10'); 733 ">1 more author</span> 734 735 736 </div> 737 738 <!-- Journal/Book title and date --> 739 740 741 742 743 744 745 746 747 748 749 <div class="periodical"> 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 751 </div> 752 <div class="periodical"> 753 754 </div> 755 756 <!-- Links/Buttons --> 757 <div class="links"> 758 759 760 <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> 761 762 763 764 765 766 767 768 <a href="https://arxiv.org/pdf/2410.22118" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a> 769 770 771 772 773 774 775 <a href="https://github.com/aisoc-lab/inference-acceleration-bias" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a> 776 777 778 779 780 </div> 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 <!-- Hidden abstract block --> 800 <div class="abstract hidden"> 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> 802 </div> 803 804 805 806 807 808 </div> 809</div> 810</li> 811</ol> 812 813</div> 814 815 </article> 816 817 818 819 820</div> 821 822 823 </div> 824 825 <!-- Footer --> 826 827 828 829 <footer class="fixed-bottom" role="contentinfo"> 830 <div class="container mt-0"> 831 832 © Copyright 2026 833 Elisabeth 834 835 Kirsten. Powered by <a href="https://jekyllrb.com/" target="_blank" rel="external nofollow noopener">Jekyll</a> with <a href="https://github.com/alshedivat/al-folio" rel="external nofollow noopener" target="_blank">al-folio</a> theme. 836 837 838 839 Last updated: October 02, 2026. 840 841 842 </div> 843 </footer> 844 845 846 847 <!-- JavaScripts --> 848 <!-- jQuery -->
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942<script src="/assets/js/search-setup.js?6c304f7b1992d4b60f7a07956e52f04a"></script>
942 943
943<script src="/assets/js/search-data.js"></script>
943 944
944<script src="/assets/js/shortcut-key.js?6f508d74becd347268a7f822bca7309d"></script>
944 945 946 947 948 949 </body> 950</html>
Line numbers count LF bytes from the start of the resource, as the search results do. Vendor segments are library code the classifier recognised; they are stored but not indexed. Bytes are shown as Latin1 characters, one per byte.