Computation and Language
AITutor-EvalKit: Exploring the Capabilities of AI Tutors
Numaan Naeem, Kaushal Kumar Maurya, Kseniia Petukhova et al. (+1 more)
We present AITutor-EvalKit, an application that uses
language technology to evaluate the pedagogical quality of AI tutors, provides software for demonstration and evaluation, as well as model inspection and data visualization. This tool is aimed at education stakeholders as well as *ACL community at large, as it supports learning and can also be used to collect user feedback and annotation.
Read the full abstract ▾
All authors Numaan Naeem, Kaushal Kumar Maurya, Kseniia Petukhova, Ekaterina Kochmar
Computation and Language
Different types of syntactic agreement recruit the same units within large language models
Daria Kryvosheieva, Andrea de Varda, Evelina Fedorenko et al. (+1 more)
Large
language models (LLMs) can reliably distinguish grammatical from ungrammatical sentences, but how grammatical knowledge is represented within the models remains an open question. We investigate whether different syntactic phenomena recruit shared or distinct components in LLMs. Using a functional localization approach inspired by cognitive neuroscience, we identify the LLM units most responsive to 67 English syntactic phenomena in seven open-weight models. These units are consistently recruited across sentences containing the phenomena and causally support the models' syntactic performance. Critically, different types of syntactic agreement (e.g., subject-verb, anaphor, determiner-noun) recruit overlapping sets of units, suggesting that agreement constitutes a meaningful functional category for LLMs. This pattern holds in English, Russian, and Chinese; and further, in a cross-lingual analysis of 57 diverse
languages, structurally more similar
languages share more units for subject-verb agreement. Taken together, these findings reveal that syntactic agreement-a critical marker of syntactic dependencies-constitutes a meaningful category within LLMs' representational spaces.
Read the full abstract ▾
All authors Daria Kryvosheieva, Andrea de Varda, Evelina Fedorenko, Greta Tuckute
Computation and Language
Evaluating Hydro-Science and Engineering Knowledge of Large Language Models
Shiruo Hu, Wenbo Shan, Yingjia Li et al. (+16 more)
Hydro-Science and Engineering (Hydro-SE) is a critical and irreplaceable domain that secures human water supply, generates clean hydropower energy, and mitigates flood and drought disasters. Featuring multiple engineering objectives, Hydro-SE is an inherently interdisciplinary domain that integrates scientific knowledge with engineering expertise. This integration necessitates extensive expert collaboration in decision-making, which poses difficulties for intelligence. With the rapid advancement of large
language models (LLMs), their potential application in the Hydro-SE domain is being increasingly explored. However, the knowledge and application abilities of LLMs in Hydro-SE have not been sufficiently evaluated. To address this issue, we propose the Hydro-SE LLM evaluation
benchmark (Hydro-SE Bench), which contains 4,000 multiple-choice questions. Hydro-SE Bench covers nine subfields and enables evaluation of LLMs in aspects of basic conceptual knowledge, engineering application ability, and
reasoning and calculation ability. The evaluation results on Hydro-SE Bench show that the accuracy values vary among 0.74 to 0.80 for commercial LLMs, and among 0.41 to 0.68 for small-parameter LLMs. While LLMs perform well in subfields closely related to natural and physical sciences, they struggle with domain-specific knowledge such as industry standards and hydraulic structures. Model scaling mainly improves
reasoning and calculation abilities, but there is still great potential for LLMs to better handle problems in practical engineering application. This study highlights the strengths and weaknesses of LLMs for Hydro-SE tasks, providing model developers with clear training targets and Hydro-SE researchers with practical guidance for applying LLMs.
Read the full abstract ▾
All authors Shiruo Hu, Wenbo Shan, Yingjia Li, Zhiqi Wan, Xinpeng Yu, Yunjia Qi, Haotian Xia, Yang Xiao, Dingxiao Liu, Jiaru Wang, Chenxu Gong, Ruixi Zhang, Shuyue Wu, Shibo Cui, Chee Hui Lai, Wei Luo, Yubin He, Bin Xu, Jianshi Zhao
Computation and Language
Generative AI Practices, Literacy, and Divides: An Empirical Analysis in the Italian Context
Beatrice Savoldi, Giuseppe Attanasio, Olga Gorodetskaya et al. (+10 more)
The rise of generative AI (GenAI) chatbots accessible via conversational interfaces is transforming digital interactions and holds economic promise. However, these tools might deepen existing inequalities – not only through uneven, socially stratified adoption, but through differentials in their purposeful, critical use. Drawing on original survey data from 1,906 Italian-speaking adults, we provide a comprehensive analysis of GenAI adoption, literacy, and usage patterns. Our findings show that GenAI is supporting diversified personal and professional activities and replacing traditional information-seeking tools. Yet less-educated and older individuals, and those with lower technology familiarity, are less likely to adopt it; 40% cite competence barriers as a key obstacle. Among users, AI training emerges as the primary predictor of purposeful, capital-enhancing engagement – content creation, learning, and creativity enhancement – while more passive, recreational uses (e.g., companionship, information seeking) remain insensitive to competence levels. We thus highlight digital literacy as a lever for how people leverage GenAI, not just whether they use it. Finally, gender operates as a persistent cross-cutting divide, shaping both adoption and usage frequency. These findings challenge the assumption that high accessibility translates into broadly shared gains. Rather, they offer a granular, multi-level account of emerging disparities in the GenAI era – with implications for how this technology may ultimately drive outcomes and benefit divides.
Read the full abstract ▾
All authors Beatrice Savoldi, Giuseppe Attanasio, Olga Gorodetskaya, Marta Marchiori Manerba, Elisa Bassignana, Silvia Casola, Matteo Negri, Tommaso Caselli, Luisa Bentivogli, Alan Ramponi, Arianna Muti, Nicoletta Balbo, Debora Nozza
Computation and Language · Cross-listed from cs.CV
Optical Context Compression Is Just (Bad) Autoencoding
Ivan Yee Lee, Cheng Yang and Taylor Berg-Kirkpatrick
DeepSeek-OCR shows that rendered text can be reconstructed from a small number of
vision tokens, sparking excitement about using
vision as a compression medium for long textual contexts. But this pipeline requires rendering
token embeddings to pixels and compressing from there – discarding learned representations in favor of an image the
vision encoder must then recover from. We ask whether this detour helps. Comparing DeepSeek-OCR's
vision encoder against near-zero-parameter mean pooling and a learned hierarchical encoder, we find it does not. For reconstruction, simple direct methods match or surpass
vision at every compression ratio. For
language modeling,
vision performs comparably to truncation – a baseline that simply discards context – and loses to the hierarchical encoder at every compression ratio. As expected, all compression methods outperform truncation for factual recall, but
vision never surpasses the best direct baseline. The excitement around optical context compression outpaces the evidence. Code and checkpoints are available at https://github.com/ivnle/bad-autoencoding.
Read the full abstract ▾
Computation and Language
AlignCheck: a Semantic Open-Domain Metric for Factual Consistency Assessment
Ahmad Aghaebrahimian
Large
Language Models have significantly advanced natural
language processing tasks, but remain prone to generating incorrect or misleading but plausible arguments. This issue, known as
hallucination, is particularly concerning in high-stakes domains like clinical applications, where factual inaccuracies can have severe consequences. Existing evaluation metrics fail to adequately assess factual consistency and lack interpretability, making diagnosing and mitigating errors difficult. We propose an interpretable framework for factual consistency assessment for in-domain and open-domain texts to address these limitations. Our approach decomposes text into atomic facts and introduces a flexible, schema-free methodology. Unlike previous methods with an absolute metric, we incorporate a weighted metric to enhance factual evaluation. Additionally, we propose a mechanism to control assessment complexity in intricate domains. We
benchmark our approach on popular general and clinical
datasets and release our code to support fact-aware model training in future research.
Read the full abstract ▾
Computation and Language · Cross-listed from cs.CR
SELF: A Robust Singular Value and Eigenvalue Approach for LLM Fingerprinting
Hanxiu Zhang, Qiang Xu and Yue Zheng
Safeguarding the Intellectual Property (IP) of Large
Language Models (LLMs) against model theft represents a critical challenge in contemporary AI research. Although fingerprinting is essential for establishing forensic evidence for model derivation, existing methods are susceptible to false claim attacks, weight manipulations, or high false positive rates, undermining their utility. To overcome these limitations, we propose SELF, \rev{a novel intrinsic weight-based fingerprinting scheme tailored for robust IP forensics via
adversarial model lineage verification}. SELF eliminates dependency on input and inherently resists false claims, achieving robust \rev{provenance detection} through unique, scalable and transformation-invariant fingerprint extraction via singular value and
eigenvalue decomposition of LLM
attention weights. Experimental results demonstrate that SELF maintains high IP infringement detection accuracy while significantly outperforming state-of-the-art baselines in reducing false positives and showing strong robustness against various downstream modifications, including quantization,
pruning, and fine-tuning attacks. Our code is available at https://github.com/HanxiuZhang/SELF_v4.
Read the full abstract ▾
Computation and Language
Fine-grained Narrative Classification in Biased News Articles
Zeba Afroz, Harsh Vardhan, Pawan Bhakuni et al. (+3 more)
Narratives are the cognitive and emotional scaffolds of propaganda. They organize isolated persuasive techniques into coherent stories that justify actions, attribute blame, and evoke identification with ideological camps. In this paper, we propose a novel fine-grained narrative classification in biased news articles. We also explore article-bias classification as the precursor task to narrative classification and fine-grained persuasive technique identification. We develop INDI-PROP, the first ideologically grounded fine-grained narrative
dataset with multi-level annotation for analyzing propaganda in Indian news media. Our
dataset INDI-PROP comprises 1,266 articles focusing on two polarizing socio-political events in recent times: CAA and the Farmers' protest. Each article is annotated at three hierarchical levels: (i) ideological article-bias (pro-government, pro-opposition, neutral), (ii) event-specific fine-grained narrative frames anchored in ideological polarity and communicative intent, and (iii) persuasive techniques. We propose FANTA and TPTC, two GPT-4o-mini guided multi-hop
prompt-based
reasoning frameworks for the bias, narrative, and persuasive technique classification. FANTA leverages multi-layered communicative phenomena by integrating information extraction and contextual framing for hierarchical
reasoning. On the other hand, TPTC adopts systematic decomposition of persuasive cues via a two-stage approach. Our evaluation suggests substantial improvement over underlying baselines in each case.
Read the full abstract ▾
All authors Zeba Afroz, Harsh Vardhan, Pawan Bhakuni, Aanchal Punia, Rajdeep Kumar, Md. Shad Akhtar
Computation and Language · Cross-listed from cs.CV
CartoMapQA: A Fundamental Benchmark Dataset Evaluating Vision-Language Models on Cartographic Map Understanding
Huy Quang Ung, Guillaume Habault, Yasutaka Nishimura et al. (+8 more)
The rise of Visual-
Language Models (LVLMs) has unlocked new possibilities for seamlessly integrating visual and textual information. However, their ability to interpret cartographic maps remains largely unexplored. In this paper, we introduce CartoMapQA, a
benchmark specifically designed to evaluate LVLMs' understanding of cartographic maps through question-answering tasks. The
dataset includes over 2000 samples, each composed of a cartographic map, a question (with open-ended or multiple-choice answers), and a ground-truth answer. These tasks span key low-, mid- and high-level map interpretation skills, including symbol recognition, embedded information extraction, scale interpretation, and route-based
reasoning. Our evaluation of both open-source and proprietary LVLMs reveals persistent challenges: models frequently struggle with map-specific semantics, exhibit limited geospatial
reasoning, and are prone to Optical Character Recognition (OCR)-related errors. By isolating these weaknesses, CartoMapQA offers a valuable tool for guiding future improvements in LVLM architectures. Ultimately, it supports the development of models better equipped for real-world applications that depend on robust and reliable map understanding, such as
navigation, geographic search, and urban planning. Our source code and data are openly available to the research community at: https://github.com/ungquanghuy-kddi/CartoMapQA.git
Read the full abstract ▾
All authors Huy Quang Ung, Guillaume Habault, Yasutaka Nishimura, Hao Niu, Roberto Legaspi, Tomoki Oya, Ryoichi Kojima, Masato Taya, Chihiro Ono, Atsunori Minamikawa, Yan Liu
Computation and Language · Cross-listed from cs.IR
Adithya S Kolavi and Vyoman Jain
Multimodal document
retrieval systems have shown strong progress in aligning visual and textual content for semantic search. However, most existing approaches remain heavily English-centric, limiting their effectiveness in
multilingual contexts. In this work, we present M3DR (
Multilingual Multimodal Document
Retrieval), a framework designed to bridge this gap across
languages, enabling applicability across diverse linguistic and cultural contexts. M3DR leverages synthetic
multilingual document data and generalizes across different
vision-
language architectures and model sizes, enabling robust cross-lingual and cross-modal
alignment. Using contrastive training, our models learn unified representations for text and document images that transfer effectively across
languages. We validate this capability on 22 typologically diverse
languages, demonstrating consistent performance and adaptability across linguistic and script variations. We further introduce a comprehensive
benchmark that captures real-world
multilingual scenarios, evaluating models under monolingual,
multilingual, and mixed-
language settings. M3DR generalizes across both single dense vector and ColBERT-style
token-level multi-vector
retrieval paradigms. Our models, NetraEmbed and ColNetraEmbed achieve state-of-the-art performance with ~150% relative improvements on cross-lingual
retrieval.
Read the full abstract ▾
Computation and Language
Understanding LLM Reasoning for Abstractive Summarization
Haohan Yuan and Haopeng Zhang
Reasoning has substantially improved Large
Language Models (LLMs) on analytical tasks such as mathematics and code generation, but its value for abstractive summarization remains unclear. To address this gap, we adapt general
reasoning strategies to the summarization setting and conduct a large-scale comparative study of 8
reasoning strategies and 3 Large
Reasoning Models (LRMs) across 8 diverse
datasets, evaluating both summary quality and factual faithfulness. Our results show that
reasoning is not a universal solution and its effectiveness depends strongly on the strategy and the summarization setting. In particular, we find a trade-off between summary quality and factual faithfulness. Explicit
reasoning strategies often improve reference-based quality, but may weaken factual grounding, whereas implicit
reasoning in LRMs shows the opposite tendency. We further find that increasing an LRM's internal
reasoning budget does not reliably improve summarization and can even reduce factual consistency. These findings suggest that, for summarization, more
reasoning is not always better. Effective
reasoning should preserve faithful compression rather than induce over-elaboration. Our source code is publicly available.
Read the full abstract ▾
Computation and Language · Cross-listed from cs.CV
NAS-LoRA: Empowering Parameter-Efficient Fine-Tuning for Visual Foundation Models with Searchable Adaptation
Renqi Chen, Haoyang Su and Shixiang Tang
The Segment Anything Model (SAM) has emerged as a powerful visual foundation model for image
segmentation. However, adapting SAM to specific downstream tasks, such as medical and agricultural imaging, remains a significant challenge. To address this, Low-Rank Adaptation (LoRA) and its variants have been widely employed to enhancing SAM's adaptation performance on diverse domains. Despite advancements, a critical question arises: can we integrate inductive bias into the model? This is particularly relevant since the
Transformer encoder in SAM inherently lacks spatial priors within image patches, potentially hindering the acquisition of high-level semantic information. In this paper, we propose NAS-LoRA, a new Parameter-Efficient Fine-Tuning (PEFT) method designed to bridge the semantic gap between pre-trained SAM and specialized domains. Specifically, NAS-LoRA incorporates a lightweight Neural Architecture Search (NAS) block between the encoder and decoder components of LoRA to dynamically optimize the prior knowledge integrated into weight updates. Furthermore, we propose a stage-wise optimization strategy to help the ViT encoder balance weight updates and architectural adjustments, facilitating the gradual learning of high-level semantic information. Various Experiments demonstrate our NAS-LoRA improves existing PEFT methods, while reducing training cost by 24.14% without increasing inference cost, highlighting the potential of NAS in enhancing PEFT for visual foundation models.
Read the full abstract ▾
Computation and Language
A Preliminary Study on the Promises and Challenges of Native Top-$k$ Sparse Attention
Di Xiu, Hongyin Tang, Bolin Rong et al. (+4 more)
Large
Language Models (LLMs) are increasingly prevalent in the field of long-context modeling, however, their inference computational costs have become a critical bottleneck hindering the advancement of tasks such as
agents and
multimodal applications. This report conducts a preliminary investigation into the effectiveness and theoretical mechanisms of the Top-$k$
Attention mechanism during both the decoding and training phases. First, we validate the effectiveness of exact Top-$k$ Decoding through extensive experimentation. Experiments demonstrate that retaining only the pivotal Keys with the highest similarity to the Query as the context window during the decoding stage achieves performance comparable to, or even surpassing, full
attention on downstream tasks such as HELMET and LongBench v2. Second, we further explore the native Top-$k$
Attention training strategy. Experiments confirm that ensuring the consistency between training and inference regarding Top-$k$
Attention operations facilitates the further unlocking of Top-$k$ Decoding's potential, thereby significantly enhancing model performance. Furthermore, considering the high computational complexity of exact Top-$k$
Attention, we investigate the impact of approximate Top-$k$
algorithm precision on downstream tasks. Our research confirms a positive correlation between downstream task performance and approximation fidelity, and we provide statistical evaluations of the Lightning Indexer's precision within the DeepSeek-V3.2-Exp model. Finally, this report provides a theoretical interpretation from the perspective of
Entropy. Experimental observations indicate that models subjected to Top-$k$
Attention SFT exhibit a distinct phenomenon of
entropy reduction in downstream tasks, which validates the hypothesis that low-
entropy states are better adapted to Top-$k$ Decoding.
Read the full abstract ▾
All authors Di Xiu, Hongyin Tang, Bolin Rong, Lizhi Yan, Jingang Wang, Yifan Lu, Xunliang Cai
Computation and Language · Cross-listed from cs.CR
Tuning for TraceTarnish: Techniques, Trends, and Testing Tangible Traits
Robert Dilworth
In this study, we more rigorously evaluated our attack script $\textit{TraceTarnish}$, which leverages
adversarial stylometry principles to anonymize the authorship of text-based messages. To ensure the efficacy and utility of our attack, we sourced, processed, and analyzed Reddit comments – comments that were later alchemized into $\textit{TraceTarnish}$ data – to gain valuable insights. The transformed $\textit{TraceTarnish}$ data was then further augmented by $\textit{StyloMetrix}$ to manufacture stylometric features – features that were culled using the Information Gain criterion, leaving only the most informative, predictive, and discriminative ones. Our results found that function words and function word types ($L\_FUNC\_A$ $\&$ $L\_FUNC\_T$); content words and content word types ($L\_CONT\_A$ $\&$ $L\_CONT\_T$); and the Type-
Token Ratio ($ST\_TYPE\_TOKEN\_RATIO\_LEMMAS$) yielded significant Information-Gain readings. The identified stylometric cues – function-word frequencies, content-word distributions, and the Type-
Token Ratio – serve as reliable indicators of compromise (IoCs), revealing when a text has been deliberately altered to mask its true author. Similarly, these features could function as forensic beacons, alerting defenders to the presence of an
adversarial stylometry attack; granted, in the absence of the original message, this signal may go largely unnoticed, as it appears to depend on a pre- and post-transformation comparison. "In trying to erase a trace, you often imprint a larger one." Armed with this understanding, we framed $\textit{TraceTarnish}$'s operations and outputs around these five isolated features, using them to conceptualize and implement enhancements that further strengthen the attack.
Read the full abstract ▾
Computation and Language · Cross-listed from cs.CV
Text-Printed Image: Bridging the Image-Text Modality Gap for Text-centric Training of Large Vision-Language Models
Shojiro Yamabe, Futa Waseda, Daiki Shiono et al. (+1 more)
Recent large
vision-
language models (LVLMs) have been applied to diverse VQA tasks. However, achieving practical performance typically requires task-specific fine-tuning with large numbers of image-text pairs, which are costly to collect. In this work, we study text-centric training, a setting where only textual descriptions are available and no real images are provided, as a paradigm for low-cost data scaling. Unlike images, whose collection is often restricted by
privacy constraints and scarcity in niche domains, text is widely available. Moreover, text is easily editable, enabling automatic diversification and expansion with LLMs at minimal human effort. While this offers clear advantages over image collection in terms of scalability and cost, training on raw text without images still yields limited gains on VQA tasks because of the image-text modality gap. To address this issue, we propose a Text-Printed Image (TPI), which generates synthetic images by directly rendering the given textual description on a plain white canvas. This simple rendering projects text into the image modality and can be integrated into arbitrary existing LVLM training pipelines at low cost. Moreover, TPI preserves the semantics of the text, whereas text-to-image models often fail to do. Across four models and seven
benchmarks, our systematic experiments show that TPI enables more effective text-centric training than synthetic images generated by a
diffusion model. We further explore TPI as a low-cost data-augmentation strategy and demonstrate its practical utility. Overall, our findings highlight the significant potential of text-centric training and, more broadly, chart a path toward fully automated data generation for LVLMs.
Read the full abstract ▾
All authors Shojiro Yamabe, Futa Waseda, Daiki Shiono, Tsubasa Takahashi
Computation and Language
PretrainZero: Reinforcement Active Pretraining
Xingrun Xing, Zhiyuan Fan, Jie Lou et al. (+3 more)
Mimicking human behavior to actively learning from general experience and achieve artificial general intelligence has always been a human dream. Recent reinforcement learning (RL) based large-thinking models demonstrate impressive expert-level abilities, i.e., software and math, but still rely heavily on verifiable rewards in specific domains, placing a significant bottleneck to extend the performance boundary of general
reasoning capabilities. In this work, we propose PretrainZero, a reinforcement active learning framework built on the pretraining corpus to extend RL from domain-specific post-training to general pretraining. PretrainZero features the following characteristics: 1) Active pretraining: inspired by the active learning ability of humans, PretrainZero learns a unified
reasoning policy to actively identify reasonable and informative contents from pretraining corpus, and reason to predict these contents by RL. 2) Self-supervised learning: without any verifiable labels, pretrained reward models, or supervised fine-tuning, we directly pretrain reasoners from 3 to 30B base models on the general Wikipedia corpus using RL, significantly breaking the verification data-wall for general
reasoning. 3) Verification scaling: by tackling increasingly challenging masked spans, PretrainZero substantially enhances the general
reasoning abilities of pretrained base models. In reinforcement pretraining, PretrainZero improves Qwen3-4B-Base for 8.43, 5.96 and 10.60 on MMLU-Pro, SuperGPQA and math average
benchmarks. In post-training, the pretrained models can also serve as
reasoning foundation models for downstream RLVR tasks.
Read the full abstract ▾
All authors Xingrun Xing, Zhiyuan Fan, Jie Lou, Guoqi Li, Jiajun Zhang, Debing Zhang
Computation and Language
Dual LoRA: Enhancing LoRA with Magnitude and Direction Updates
Yixing Xu, Chao Li, Xuanwu Yin et al. (+4 more)
Low-rank adaptation (LoRA) is one of the most popular methods among parameter-efficient fine-tuning (PEFT) methods to adapt pre-trained large
language models (LLMs) to specific downstream tasks. However, the model trained based on LoRA often has an unsatisfactory performance due to its low-rank assumption. In this paper, we propose a novel method called Dual LoRA to improve the performance by incorporating an inductive bias into the original LoRA. Specifically, we separate low-rank
matrices into two groups: the magnitude group to control whether or not and how far we should update a parameter and the direction group to decide whether this parameter should move forward or backward, to better simulate the parameter updating process of the full fine-tuning based on gradient-based optimization
algorithms. We show that this can be simply achieved by adding a ReLU function to the magnitude group and a sign function to the direction group. We conduct several experiments over a wide range of NLP tasks, including natural
language understanding (NLU) and commonsense
reasoning datasets on RoBERTa, DeBERTa, and LLaMA-1/2/3 as baseline models. The results show that we consistently outperform LoRA and its state-of-the-art variants with the same number of trainable parameters.
Read the full abstract ▾
All authors Yixing Xu, Chao Li, Xuanwu Yin, Spandan Tiwari, Dong Li, Ashish Sirasao, Emad Barsoum
Computation and Language
Characterizing Language Use in a Collaborative Situated Game
Nicholas Tomlin, Naitian Zhou, Eve Fleisig et al. (+10 more)
Cooperative
video games, where multiple participants must coordinate by communicating and
reasoning under uncertainty in complex environments, yield a rich source of
language data. We collect the Portal Dialogue Corpus: a corpus of 11.5 hours of spoken human dialogue in the co-op mode of the popular Portal 2 virtual puzzle game, comprising 24.5K total utterances. We analyze player
language and behavior, identifying a number of linguistic phenomena that rarely appear in most existing chitchat or task-oriented dialogue corpora, including complex spatial reference, clarification and repair, and ad-hoc convention formation. To support future analyses of
language use in complex, situated, collaborative problem-solving scenarios, we publicly release the corpus, which comprises player
videos, audio, transcripts, game state data, and both manual and automatic annotations of
language data.
Read the full abstract ▾
All authors Nicholas Tomlin, Naitian Zhou, Eve Fleisig, Liangyuan Chen, Téa Wright, Lauren Vinh, Laura X. Ma, Seun Eisape, Ellie French, Tingting Du, Tianjiao Zhang, Alexander Koller, Alane Suhr
Computation and Language
Hanting Chen, Chong Zhu, Kai Han et al. (+6 more)
Transformers have achieved significant success across various domains, relying on self-
attention to capture dependencies. However, the standard first-order
attention mechanism is often limited by a low-rank bottleneck, struggling to capture intricate, multi-hop relationships within a single layer. In this paper, we propose the Nexus, a novel architecture designed to enhance representational power through a recursive framework. Unlike standard approaches that use static linear projections for Queries and Keys, Nexus dynamically refines these representations via nested self-
attention mechanisms. Specifically, the Query and Key vectors are themselves outputs of inner
attention loops, allowing
tokens to aggregate global context and model high-order correlations prior to the final
attention computation. We enforce a parameter-efficient weight-sharing strategy across recursive steps, ensuring that this enhanced expressivity incurs $\mathcal{O}(1)$ additional parameters. We provide theoretical analysis demonstrating that our method breaks the linear bottleneck of standard
attention. Empirically, Nexus outperforms standard
Transformers on multiple
benchmarks.
Read the full abstract ▾
All authors Hanting Chen, Chong Zhu, Kai Han, Yuchuan Tian, Yuchen Liang, Tianyu Guo, Xinghao Chen, Dacheng Tao, Yunhe Wang
Computation and Language · Cross-listed from cs.CY
LLM-Generated Ads: From Personalization Parity to Persuasion Superiority
Elyas Meguellati, Stefano Civelli, Lei Han et al. (+3 more)
As large
language models (LLMs) become increasingly capable of generating persuasive content, understanding their effectiveness across different advertising strategies becomes critical. This paper presents a two-part investigation examining LLM-generated advertising through complementary lenses: (1) personality-based and (2) psychological persuasion principles. In our first study (n=400), we tested whether LLMs could generate personalized advertisements tailored to specific personality traits (openness and neuroticism) and how their performance compared to human experts. Results showed that LLM-generated ads achieved statistical parity with human-written ads (51.1% vs. 48.9%, p > 0.05), with no significant performance differences for matched personalities. Building on these insights, our second study (n=800) shifted focus from individual personalization to universal persuasion, testing LLM performance across four foundational psychological principles: authority, consensus, cognition, and scarcity. AI-generated ads significantly outperformed human-created content, achieving a 59.1% preference rate (vs. 40.9%, p < 0.001), with the strongest performance in authority (63.0%) and consensus (62.5%) appeals. Qualitative analysis revealed AI's advantage stems from crafting more sophisticated, aspirational messages and achieving superior visual-narrative coherence. Critically, this quality advantage proved robust: even after applying a 21.2 percentage point detection penalty when participants correctly identified AI-origin, AI ads still outperformed human ads, and 29.4% of participants chose AI content despite knowing its origin. These findings demonstrate LLMs'
evolution from parity in personalization to superiority in persuasive storytelling, with significant implications for advertising practice given LLMs' near-zero marginal cost and time requirements compared to human experts.
Read the full abstract ▾
All authors Elyas Meguellati, Stefano Civelli, Lei Han, Abraham Bernstein, Shazia Sadiq, Gianluca Demartini
Computation and Language
From Hypothesis to Premises: LLM-based Backward Logical Reasoning with Selective Symbolic Translation
Qingchuan Li, Mingyue Cheng, Zirui Liu et al. (+3 more)
Logical
reasoning is a core challenge in natural
language understanding and a fundamental capability of artificial intelligence, underpinning scientific discovery, mathematical
theorem proving, and complex decision-making. Despite the remarkable progress of large
language models (LLMs), most current approaches still rely on forward
reasoning paradigms, generating step-by-step rationales from premises to conclusions. However, such methods often suffer from redundant inference paths, hallucinated steps, and semantic drift, resulting in inefficient and unreliable
reasoning. In this paper, we propose a novel framework, Hypothesis-driven Backward Logical
Reasoning (HBLR). The core idea is to integrate confidence-aware symbolic translation with hypothesis-driven backward
reasoning. In the translation phase, only high-confidence spans are converted into logical form, such as First-Order Logic (FOL), while uncertain content remains in natural
language. A translation reflection module further ensures semantic fidelity by evaluating symbolic outputs and reverting lossy ones back to text when necessary. In the
reasoning phase, HBLR simulates human deductive thinking by assuming the conclusion is true and recursively verifying its premises. A
reasoning reflection module further identifies and corrects flawed inference steps, enhancing logical coherence. Extensive experiments on five
reasoning benchmarks demonstrate that HBLR consistently outperforms strong baselines in both accuracy and efficiency.
Read the full abstract ▾
All authors Qingchuan Li, Mingyue Cheng, Zirui Liu, Daoyu Wang, Yuting Zeng, Tongxuan Liu
Computation and Language
Idea-Gated Transformers: Enforcing Semantic Coherence via Differentiable Vocabulary Pruning
Darshan Fofadiya
Autoregressive
Language Models (LLMs) trained on Next-
Token Prediction (NTP) often suffer from Topic Drift where the generation wanders away from the initial
prompt due to a reliance on local associations rather than global planning. While scaling model size mitigates this, the fundamental myopia of the NTP objective remains. In this work, we introduce the Idea-Gated
Transformer, a novel architecture that separates semantic planning from syntactic generation. We introduce an auxiliary Idea Head trained to predict the bag-of-words distribution for a future context window, creating a latent “Concept Vector” that actively gates the main vocabulary during generation. We propose a differentiable gating mechanism that suppresses semantically irrelevant
tokens, effectively
pruning the search space in real-time. Experiments on WikiText-103 demonstrate that while the Idea-Gated model achieves comparable validation perplexity to a standard GPT-2 baseline, it exhibits significantly superior Domain Retention. Qualitative and quantitative analysis reveals that the gating mechanism successfully locks generation into specific semantic clusters (e.g., Finance, Science) and resists associative drift, offering a parameter-efficient path toward more controllable
language modeling.
Read the full abstract ▾
Computation and Language
PERCS: Persona-Guided Controllable Biomedical Summarization Dataset
Rohan Charudatt Salvi, Chirag Chawla, Dhruv Jain et al. (+3 more)
Automatic medical text simplification plays a key role in improving health literacy by making complex biomedical research accessible to diverse readers. However, most existing resources assume a single generic audience, overlooking the wide variation in medical literacy and information needs across user groups. To address this limitation, we introduce PERCS (Persona-guided Controllable Summarization), a
dataset of biomedical abstracts paired with summaries tailored to four personas: Laypersons, Premedical Students, Non-medical Researchers, and Medical Experts. These personas represent different levels of medical literacy and information needs, emphasizing the need for targeted, audience-specific summarization. Each summary in PERCS was reviewed by physicians for factual accuracy and persona
alignment using a detailed error taxonomy. Technical validation shows clear differences in readability, vocabulary, and content depth across personas. Along with describing the
dataset, we
benchmark four large
language models on PERCS using automatic evaluation metrics that assess comprehensiveness, readability, and faithfulness, establishing baseline results for future research. The
dataset, annotation guidelines, and evaluation materials are publicly available to support research on persona-specific communication and controllable biomedical summarization.
Read the full abstract ▾
All authors Rohan Charudatt Salvi, Chirag Chawla, Dhruv Jain, Swapnil Panigrahi, Md Shad Akhtar, Shweta Yadav
Computation and Language · Cross-listed from cs.SI
Epistemic Substitution: How Grokipedia's AI-Generated Encyclopedia Restructures Authority
Aliakbar Mehdizadeh, Faezeh Khazaee and Martin Hilbert
For more than two decades, Wikipedia has represented a decentralized, human-curated model of encyclopedic knowledge production. The emergence of AI-generated encyclopedias, such as Grokipedia, raises a fundamental question: do human- and AI-curated knowledge systems rely on the same foundations of epistemic authority? We examine this question through a comparative analysis of 1,000 matched Wikipedia–Grokipedia article pairs, comprising nearly 600,000 citation instances. Using an eight-category classification scheme, we construct the epistemic profiles of articles to examine the institutional composition of their cited sources. Our findings reveal substantial differences in sourcing practices. Grokipedia relies proportionally less on academic and journalistic sources and more on user-generated content and NGO and think-tank publications. These differences persist across broad topic domains, although their magnitude varies by subject. We also find that Grokipedia adds citations approximately linearly as articles grow, whereas Wikipedia exhibits greater variation in citation density. Together, these findings suggest a shift in the institutional sources invoked to justify encyclopedic claims, which we characterize as epistemic substitution. For the same topics, the two encyclopedias rest on different institutional sources. Our findings underscore the importance of systematic, ongoing audits of information provenance for accountability in generative knowledge systems.
Read the full abstract ▾
Computation and Language
Modeling Topics and Sociolinguistic Variation in Code-Switched Discourse: Insights from Spanish-English and Spanish-Guaraní
Nemika Tyagi, Nelvin Licona Guevara and Olga Kellert
This study presents an LLM-assisted annotation pipeline for the sociolinguistic and topical analysis of bilingual discourse in two typologically distinct contexts: Spanish-English and Spanish-Guaraní. Using large
language models, we automatically labeled topic, genre, and discourse-pragmatic functions across a total of 3,691 code-switched sentences, integrated demographic metadata from the Miami Bilingual Corpus, and enriched the Spanish-Guaraní
dataset with new topic annotations. The resulting distributions reveal systematic links between gender,
language dominance, and discourse function in the Miami data, and a clear diglossic division between formal Guaraní and informal Spanish in Paraguayan texts. These findings replicate and extend earlier interactional and sociolinguistic observations with corpus-scale quantitative evidence. The study demonstrates that large
language models can reliably recover interpretable sociolinguistic patterns traditionally accessible only through manual annotation, advancing computational methods for cross-linguistic and low-resource bilingual research.
Read the full abstract ▾