Hasil untuk "Biography"

Harold Triedman, Alexios Mantzarlis

Elon Musk released Grokipedia on 27 October 2025 to provide an alternative to Wikipedia, the crowdsourced online encyclopedia. In this paper, we provide the first comprehensive analysis of Grokipedia and compare it to a dump of Wikipedia, with a focus on article similarity and citation practices. Although Grokipedia articles are much longer than their corresponding English Wikipedia articles, we find that much of Grokipedia's content (including both articles with and without Creative Commons licenses) is highly derivative of Wikipedia. Nevertheless, citation practices between the sites differ greatly, with Grokipedia citing many more sources deemed "generally unreliable" or "blacklisted" by the English Wikipedia community and low quality by external scholars, including dozens of citations to sites like Stormfront and Infowars. We then analyze article subsets: one about elected officials, one about controversial topics, and one random subset for which we derive article quality and topic. We find that the elected official and controversial article subsets showed less similarity between their Wikipedia version and Grokipedia version than other pages. The random subset illustrates that Grokipedia focused rewriting the highest quality articles on Wikipedia, with a bias towards biographies, politics, society, and history. Finally, we publicly release our nearly-full scrape of Grokipedia, as well as embeddings of the entire Grokipedia corpus.

Yijun Yang, Zeyu Huang, Wenhao Zhu et al.

Existing frameworks for evaluating long-context language models (LCLM) can be broadly categorized into real-world applications (e.g, document summarization) and synthetic tasks (e.g, needle-in-a-haystack). Despite their utility, both approaches are accompanied by certain intrinsic limitations. Real-world tasks often involve complexity that makes interpretation challenging and suffer from data contamination, whereas synthetic tasks frequently lack meaningful coherence between the target information (needle) and its surrounding context (haystack), undermining their validity as proxies for realistic applications. In response to these challenges, we posit that an ideal long-context evaluation framework should be characterized by three essential features: 1) seamless context 2) controllable setting and 3) sound evaluation. This study introduces $\textbf{LongBioBench}$, a benchmark that utilizes artificially generated biographies as a controlled environment for assessing LCLMs across dimensions of understanding, reasoning, and trustworthiness. Our experimental evaluation, which includes 18 LCLMs in total, demonstrates that most models still exhibit deficiencies in semantic understanding and elementary reasoning over retrieved results and are less trustworthy as context length increases. Our further analysis indicates some design choices employed by existing synthetic benchmarks, such as contextual non-coherence, numerical needles, and the absence of distractors, rendering them vulnerable to test the model's long-context capabilities. To sum up, compared to previous synthetic benchmarks, LongBioBench achieves a better trade-off between mirroring authentic language tasks and maintaining controllability, and is highly interpretable and configurable.

Atahan Özer, Çağatay Yıldız

Large language models (LLMs) exhibit remarkable capabilities in question answering and reasoning thanks to their extensive parametric memory. However, their knowledge is inherently limited by the scope of their pre-training data, while real-world information evolves continuously. Updating this knowledge typically requires costly and brittle re-training, or in-context learning (ICL), which becomes impractical at scale given the volume and volatility of modern information. Motivated by these limitations, we investigate how LLMs perform when exposed to temporal text corpora, or documents that reflect evolving knowledge over time, such as sports biographies where facts like a player's "current team" change year by year. To this end, we introduce two new benchmarks: Temporal Wiki, which captures factual drift across historical Wikipedia snapshots, and Unified Clark, which aggregates timestamped news articles to simulate real-world information accumulation. Our analysis reveals that LLMs often struggle to reconcile conflicting or outdated facts and can be misled when multiple versions of a fact appear in context. To address these issues, we propose a lightweight, agentic framework that incrementally builds a structured, external memory from source documents without requiring re-training. This knowledge organization strategy enables models to retrieve and reason over temporally filtered, relevant information at inference time. Empirically, our method outperforms ICL and RAG baselines across both benchmarks, especially on questions requiring more complex reasoning or integration of conflicting facts.

Renfei Dang, Peng Hu, Zhejian Lai et al.

Prior works have shown that fine-tuning on new knowledge can induce factual hallucinations in large language models (LLMs), leading to incorrect outputs when evaluated on previously known information. However, the specific manifestations of such hallucination and its underlying mechanisms remain insufficiently understood. Our work addresses this gap by designing a controlled dataset \textit{Biography-Reasoning}, and conducting a fine-grained analysis across multiple knowledge types and two task types, including knowledge question answering (QA) and knowledge reasoning tasks. We find that hallucinations not only severely affect tasks involving newly introduced knowledge, but also propagate to other evaluation tasks. Moreover, when fine-tuning on a dataset in which a specific knowledge type consists entirely of new knowledge, LLMs exhibit elevated hallucination tendencies. This suggests that the degree of unfamiliarity within a particular knowledge type, rather than the overall proportion of new knowledge, is a stronger driver of hallucinations. Through interpretability analysis, we show that learning new knowledge weakens the model's attention to key entities in the input question, leading to an over-reliance on surrounding context and a higher risk of hallucination. Conversely, reintroducing a small amount of known knowledge during the later stages of training restores attention to key entities and substantially mitigates hallucination behavior. Finally, we demonstrate that disrupted attention patterns can propagate across lexically similar contexts, facilitating the spread of hallucinations beyond the original task.

Sitao Cheng, Xunjian Yin, Ruiwen Zhou et al.

The mechanism by which RL contributes to reasoning capabilities-whether it incentivizes the synthesis of new skills or merely amplifies existing behaviors-remains a subject of intense debate. In this work, we investigate this question through the lens of Complementary Reasoning, a complex task that requires integrating internal parametric knowledge with external contextual information. Using a controlled synthetic dataset of human biographies, we strictly decouple this ability into two atomic skills: Parametric Reasoning (relying on internal knowledge) and Contextual Reasoning (depending on external information). To rigorously assess capability boundaries, we evaluate generalization across three distinct levels of difficulty: I.I.D., Composition, and Zero-shot settings. We find that while SFT is sufficient for in-distribution performance, it struggles with O.O.D. generalization, particularly in Zero-shot settings where relational combinations are novel. Crucially, we identify the SFT Generalization Paradox: Models supervised solely on the composite task achieve near-perfect in-distribution accuracy but collapse on out-of-distribution generalization, indicating their reliance on rote memorization of path shortcuts. In contrast, we find that RL acts as a reasoning synthesizer rather than a probability amplifier. However, we uncover a strict atomic prerequisite: RL can only synthesize these complex strategies if the base model has first mastered the independent atomic skills (Parametric and Contextual) via SFT. These findings challenge the view of RL as a mere amplifier, suggesting that given sufficient atomic foundations, RL can actively synthesize complex reasoning strategies from learned primitives without explicit supervision on such complex strategies. This indicates that decoupled atomic training followed by RL offers a scalable path to generalization for complex reasoning tasks.

Yixin Wan, Kai-Wei Chang

Social biases can manifest in language agency. However, very limited research has investigated such biases in Large Language Model (LLM)-generated content. In addition, previous works often rely on string-matching techniques to identify agentic and communal words within texts, falling short of accurately classifying language agency. We introduce the Language Agency Bias Evaluation (LABE) benchmark, which comprehensively evaluates biases in LLMs by analyzing agency levels attributed to different demographic groups in model generations. LABE tests for gender, racial, and intersectional language agency biases in LLMs on 3 text generation tasks: biographies, professor reviews, and reference letters. Using LABE, we unveil language agency social biases in 3 recent LLMs: ChatGPT, Llama3, and Mistral. We observe that: (1) LLM generations tend to demonstrate greater gender bias than human-written texts; (2) Models demonstrate remarkably higher levels of intersectional bias than the other bias aspects. (3) Prompt-based mitigation is unstable and frequently leads to bias exacerbation. Based on our observations, we propose Mitigation via Selective Rewrite (MSR), a novel bias mitigation strategy that leverages an agency classifier to identify and selectively revise parts of generated texts that demonstrate communal traits. Empirical results prove MSR to be more effective and reliable than prompt-based mitigation method, showing a promising research direction.

Fardin Ahsan Sakib, Saadat Hasan Khan, A. H. M. Rezaul Karim

Large-scale language models (LLMs) have emerged as a groundbreaking innovation in the realm of question-answering and conversational agents. These models, leveraging different deep learning architectures such as Transformers, are trained on vast corpora to predict sentences based on given queries. Among these LLMs, ChatGPT, developed by OpenAI, has ushered in a new era by utilizing artificial intelligence (AI) to tackle diverse problem domains, ranging from composing essays and biographies to solving intricate mathematical integrals. The versatile applications enabled by ChatGPT offer immense value to users. However, assessing the performance of ChatGPT's output poses a challenge, particularly in scenarios where queries lack clear objective criteria for correctness. For instance, evaluating the quality of generated essays becomes arduous and relies heavily on manual labor, in stark contrast to evaluating solutions to well-defined, closed-ended questions such as mathematical problems. This research paper delves into the efficacy of ChatGPT in solving programming problems, examining both the correctness and the efficiency of its solution in terms of time and memory complexity. The research reveals a commendable overall success rate of 71.875\%, denoting the proportion of problems for which ChatGPT was able to provide correct solutions that successfully satisfied all the test cases present in Leetcode. It exhibits strengths in structured problems and shows a linear correlation between its success rate and problem acceptance rates. However, it struggles to improve solutions based on feedback, pointing to potential shortcomings in debugging tasks. These findings provide a compact yet insightful glimpse into ChatGPT's capabilities and areas for improvement.

Katherine Tian, Eric Mitchell, Huaxiu Yao et al.

The fluency and creativity of large pre-trained language models (LLMs) have led to their widespread use, sometimes even as a replacement for traditional search engines. Yet language models are prone to making convincing but factually inaccurate claims, often referred to as 'hallucinations.' These errors can inadvertently spread misinformation or harmfully perpetuate misconceptions. Further, manual fact-checking of model responses is a time-consuming process, making human factuality labels expensive to acquire. In this work, we fine-tune language models to be more factual, without human labeling and targeting more open-ended generation settings than past work. We leverage two key recent innovations in NLP to do so. First, several recent works have proposed methods for judging the factuality of open-ended text by measuring consistency with an external knowledge base or simply a large model's confidence scores. Second, the direct preference optimization algorithm enables straightforward fine-tuning of language models on objectives other than supervised imitation, using a preference ranking over possible model responses. We show that learning from automatically generated factuality preference rankings, generated either through existing retrieval systems or our novel retrieval-free approach, significantly improves the factuality (percent of generated claims that are correct) of Llama-2 on held-out topics compared with RLHF or decoding strategies targeted at factuality. At 7B scale, compared to Llama-2-chat, we observe 58% and 40% reduction in factual error rate when generating biographies and answering medical questions, respectively.

Marina M. Imasheva

The author analyzes the memoirs of Ibrahim Makhmudovich Makhmudov (1893-1970), an Astrakhan Yurt Tatar, one of the active builders of the Soviet system in the Tatar villages of the Astrakhan region. Shortly before his death, in 1969, I.M. Makhmudov completed a handwritten version of his memoirs, in which he reflected aspects of the daily life of the Muslim community of the Yurt-Tatar village of Zatsarevo in 1900-14. Based on the personal observations, Makhmudov compiled memories of the last decade and a half of the quiet life of the Tatar-Muslim community of a provincial Russian town before the turbulent events of wars and revolutions that ended with the establishment of Soviet power. The author of the memoirs, as an eyewitness and bearer of cultural tradition, comprehensively and deeply, sometimes scrupulously, covers the events of the early 20th century in a closed Muslim community - the mahalla. However, his assessments to these events and lifestyle area also assessments of a Soviet party leader, who both was an atheist and a person with a huge life experience in the struggle for the ideals of Soviet power as well as a convinced supporter.

Shivprasad Sagare, Tushar Abhishek, Bhavyajeet Singh et al.

Multiple business scenarios require an automated generation of descriptive human-readable text from structured input data. Hence, fact-to-text generation systems have been developed for various downstream tasks like generating soccer reports, weather and financial reports, medical reports, person biographies, etc. Unfortunately, previous work on fact-to-text (F2T) generation has focused primarily on English mainly due to the high availability of relevant datasets. Only recently, the problem of cross-lingual fact-to-text (XF2T) was proposed for generation across multiple languages alongwith a dataset, XALIGN for eight languages. However, there has been no rigorous work on the actual XF2T generation problem. We extend XALIGN dataset with annotated data for four more languages: Punjabi, Malayalam, Assamese and Oriya. We conduct an extensive study using popular Transformer-based text generation models on our extended multi-lingual dataset, which we call XALIGNV2. Further, we investigate the performance of different text generation strategies: multiple variations of pretraining, fact-aware embeddings and structure-aware input encoding. Our extensive experiments show that a multi-lingual mT5 model which uses fact-aware embeddings with structure-aware input encoding leads to best results on average across the twelve languages. We make our code, dataset and model publicly available, and hope that this will help advance further research in this critical area.

Valentina Rognoli, Bruna Petreca, Barbara Pollini et al.

The fashion industry is highly responsible for critical environmental problems and the sector is increasingly aware of the urgent need to embark on a sustainable transition. Materials, primarily textiles, are particularly problematic for the sector’s unsustainability, despite the intensive research into alternative solutions that is currently underway. This article presents a comprehensive analysis of these socio-environmental challenges and describes how governments, industry, and designers are seeking to address the situation. Furthermore, it identifies a panorama of alternative bio-based and bio-fabricated materials that could facilitate the transition toward more sustainable fashion. We present a selection of 24 case studies of newly developed bio-based and bio-fabricated materials and group them by their origin. Analysis of the cases led to the delineation of five “materials biography categories” to help understand the prominent narratives and to communicate their characteristics and fundamental attributes. This taxonomy also serves to support concepts for a circular economy by helping to build a sort of “material passport” or “product biography,” two concepts underpinning the outcome of this study, and emphasizes the need for tools to further the communication and traceability of these emergent materials. We propose “materials biography,” an overarching idea that catalogues essential dimensions and offer it to designers, companies, and final users to enhance their perception and awareness of such novel materials.

Jan M. van Daal, Branko F. van Oppen de Ruiter

A Roman-Egyptian mummy portrait of a young woman in a pink tunic is part of the Allard Pierson collection in Amsterdam. The portrait is well-known and a key piece of the collection, but has received little scholarly attention so far. The life and the afterlife of the portrait are therefore poorly understood. The authors approach the portrait from different perspectives: its provenance and acquisition, the artist’s materials and techniques, the dating conventions surrounding mummy portraits and their cultural context. The authors advocate for this in-depth multidisciplinary approach primarily because it spotlights specific areas in mummy portraits (in this case, the pearl earrings) where iconography, materials and techniques and ancient socio-economic developments converge. Provenance research proved important not only for securing the object’s bona fide acquisition, but also for tracing its second-life biography. These converging perspectives effectively cast light on research areas where more work remains desirable. In lieu of secure documentation of the archaeological findspot (which is the case with most mummy portraits) this approach is a powerful tool to nonetheless compose histories that help to understand the meaning of mummy portraits in the past and in the present and provide a durable framework for future research.

Elisa Bolchi

This study investigates the representation of two literary dogs: Flush, the cocker spaniel protagonist of Virginia Woolf’s Flush. A Biography, and Argo, the protagonist and narrator of Italo Svevo’s novella Argo e il suo padrone. With the rise of the phenomenon of language skepticism around 1900, the topos of narrating dogs became of particular interest and both these works can be placed in the fashion of dog novels, but while Svevo, although with reversed roles, draws from the literary fashion of the philosopher dog, in which “canine narrators eloquently master the human language” (Driscoll and Hoffmann 2018), Woolf plays with the very British, and Victorian, tradition of ‘illustrious biographies’ and writes the biography of Elizabeth Barrett Browning’s cocker spaniel. By means of a zooanthropological reading of the two works, the article enquires whether the two writers try to resist anthropomorphic constructedness in the narration of their nonhuman characters and what kind of narrative device they enact to underline similarities and differences between humans and dogs. It will also try to understand if the underlying presumption of the two writers is that language is only ‘linguistic’ language, or if diverse and alternative, but equally valid, forms of communication and reciprocal understanding exist.

Domininkas Burba

Many changes took place in the upper echelons of Lithuanian society at the turn of the 18th and 19th centuries: a divide began to emerge between the conservative aristocracy and the supporters of Enlightenment ideas. The latter sought reforms, the independence of the state and progress in society. The former did not support the changes, and sought to preserve the former structure of society, often seeking the support of politicians in the Russian Empire. It is important to study not only the actions of the elite of that period in the field of politics, but also their households and lifestyles. The paper explains what can be learnt about factors that may have influenced the health of Ludwik Skumin Tyszkiewicz (1751–1808), one of the most prominent Lithuanian political figures at the turn of the 18th and 19th centuries. The main character in the article is regarded in historiography as a noble with conservative views, who focused on the accumulation of material goods. The paper investigates what is known about the health problems of the count. It also explains what products were used in his kitchen, and what the stresses were in his life that could have affected his health. The circumstances of his death and funeral are also mentioned.

S. A. Gerasimova

The article is devoted to the issues of reflection of the intellectual culture of the Enlightenment in popular science discourse, represented in the activities of encyclopedic educators. The relevance of the research topic is due to the study of the views of the scientist, which influenced the value coordinates of popular science knowledge of the society of that time. The research methodology is based on a narrative approach in a historiographic perspective, as well as a culture-anthropocentric method that reveals the biography of a scientist as a manifestation of the socio-cultural processes of the era. The use of these methods determined the novelty of the research as a vector of movement of research thought, modeling the phenomenon of professionalism, since it contributes to a deeper understanding of the specifics of the professional picture of the scientist’s world. The dynamics of the views of the French educator Louis de Jaucourt is revealed, the degree of his contribution to the formation of the French Encyclopedia (Encyclopédie, ou Dictionnaire raisonné des sciences, des arts et des métiers) is determined. It is proved that moral satisfaction from participation in the creation of an encyclopedia, due to the creativity of his personality, has become significant for the scientist. The research material was the articles by L. de Jaucourt posted on the website of the first edition of the encyclopedia. Analysis of the articles shows that their pragmatics are aimed at trans-mitting information to an educated reader in popular science discourse.

Michail Anatol'jevič Perepelkin

The article examines several critical responses of L. Andreev's contemporaries from Samara to his works, as well as analyzes the creative dialogue with the writer, who leads his younger contemporary from Samara A. Tolstoy on the pages of his trilogy "The Road to Calvary". The analysis of the critical articles by A. Bostrom and A. Smirnov (Treplev), as well as several Samara episodes of the Tolstoy trilogy, allows us to conclude that the critical rethinking of the ideas of А. Tolstoy. Andreev and generations of his connoisseurs-contemporaries formed the basis for creating the image of one of the heroes of Samara episodes "The Road to Calvary"—Semyon Semyonovich Govyadin.

Mark A. Cheetham

John Guille Millais reported in his 1899 biography of his famous father, John Everett Millais, that The North-West Passage (1874) was “perhaps the most popular of all Millais’ paintings at the time”. The picture’s adoptive subtitle—“It might be done, and England should to do it”, purportedly uttered by the aged sailor in the painting—captured the patriotic zeal for the British Arctic Expedition of 1875–1876, rather than the past glories (and tragedies) of the British quest to traverse the Northwest Passage. “It” in this motto looks ahead to the planting of the British flag at the North Pole and to the treatment of the Arctic in contemporary art. Looking closely at this complex painting and its surrounding discourses in the Victorian period and in related works from our own time, I argue that The North-West Passage was and remains a “metapicture” that distilled speculation on Arctic voyaging from the Anglosphere in the 1870s and does so again today.

Marija Antic

By drawing on postcolonial feminist discourse and Hamid Naficy’s (2001) notion of ‘accented’ cinema, in particular his approach of combining the interstitial position of exilic and diasporic filmmakers with concepts of authorship and genre, this paper explores the intersection between biographical film, gendered rewriting of history, and self-narrative as a site of resistance to nationalist and patriarchal ideologies in Shirin Neshat’s Looking for Oum Kulthum (2017). I argue that Neshat’s authorial style and her position as an exilic artist inflect the biographical film in its traditional form, showcasing an innovative perspective on the genre, restructuring it to reveal the constructedness of not only a cinematic process, but also of history and historical figures. Blending the stories of a present-day Iranian woman filmmaker and the professional life of the legendary Egyptian singer Oum Kulthum, Neshat displaces the biopic from its Western-centric roots by explicitly opening it up to a discourse of contemporary gender politics in the Middle East. In doing so, she exposes the social forces that shape the production of the biopic in relation to the notion of female authorship in the context of the transcultural circuits and feminist reclaiming of Oum Kulthum’s international stardom.

Luca Peliti, Paolo Muratore-Ginanneschi

We present a translation of the 1933 paper by R. Fürth in which a profound analogy between quantum fluctuations and Brownian motion is pointed out. This paper opened in some sense the way to the stochastic methods of quantization developed almost 30 years later by Edward Nelson and others.