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Non-compositional expressions (e.g., idioms, proverbs, and metaphors) pose significant challenges for neural machine translation systems because their meanings cannot be derived from individual words alone. These expressions encode rich, cultural meaning, and have both figurative and literal meanings, making accurate translation difficult. Because models are fairly good at translating compositional text, we investigate GRPO-style fine-tuning using Machine Translation Quality Estimation (MTQE) models as reward functions. Using Chinese and Hindi idiom datasets, we find that idiom translation abilities improve by ~13 points, general, non-idiomatic translation implicitly improves by ~8 points, and cross-lingual translation abilities (trained on one language, evaluated on another) improves by ~6 points. Overall, our work quantifies the non-compositional translation gap and offers insights for developing LLMs with stronger cross-cultural and figurative language understanding.
Membership inference attacks (MIAs) assess whether a sample was used to train a model, posing privacy risks for large language models (LLMs). Existing MIAs typically rely on single loss or confidence signals, which are often unstable and easily confounded by noise, leading to limited robustness and generalization. In this paper, we propose Group-Invariant Membership Inference Attack (GI-MIA) that explicitly exploits semantic-preserving transformations to capture stability differences between members and non-members. Our method constructs an orbit of paraphrased variants for each input using LLM-driven transformations, and extracts invariant statistics (mean, maximum, and variance of normalized losses) across the orbit. A simple decision rule based on these statistics effectively discriminates members from non-members by leveraging the model’s inherent invariance on training data. We conducted extensive experiments on the Pythia family of LLMs (different model parameter sizes) across six datasets. Results show that GI-MIA consistently outperforms strong baselines, achieving state-of-the-art performance while maintaining robustness against semantic-preserving transformations. Our study highlights new directions for MIAs and calls for further research into LLM privacy protection, emphasizing the need for developing reliable models with better privacy resilience.
Preference optimization is widely used to align Large Language Models (LLMs) with preference feedback. However, most existing methods train on a single positive-negative pair per prompt, discarding additional supervision available in preference datasets that typically contain multiple candidate responses. Motivated by this limitation, recent work explores group-wise preference optimization, which jointly contrasts multiple responses for the same prompt, but its empirical behavior and scalability remain underexplored due to the memory overhead of group-coupled objectives. In this work, we present a unified empirical and systems study of group-wise preference optimization and develop a memory-efficient implementation for group-coupled objectives. By instantiating first-order linearization with objective-specific per-response coefficients, our implementation preserves first-order gradients while decoupling samples during backpropagation, substantially reducing peak memory usage and enabling scalable training with larger groups. Across offline and online settings, we show that leveraging multiple responses consistently outperforms single-pair training. Furthermore, incorporating a negative log-likelihood (NLL) term on positive responses is critical for both performance gains and training stability.
The development of high-quality multilingual models depends on well-curated multilingual datasets, which are typically obtained through filtering large web-crawled corpora. Such filtering approaches must be effective across a wide range of languages in order to improve model quality proportionally across the entire linguistic space. In recent years multiple Natural Language Processing (NLP) approaches for data processing and Large Language Model (LLM) training were developed. However, the most significant progress has been made for rich-resource languages such as English and Chinese, leaving multilingual methods largely underexplored. In this work, we present data selection strategies based on Internet registers (genres) and Web Document Score (WDS). We study 15 European languages utilizing HPLT 3.0 datasets. We train and evaluate four small generative language models per language, and compare how specific registers and WDS affect model training in separate and combined approaches. We use five common evaluation tasks covering models’ linguistic, knowledge-based, and reasoning skills. We analyze the impact of the studied strategies on each language, their average performance, and show how combining register-based selection with WDS improves evaluation performance. These results indicate that both registers and WDS can generalize across multiple languages, offering a new path towards multilingual data filtering.
When LLMs judge moral dilemmas, do they reach different conclusions in different languages, and if so, why? Two factors could drive such differences: the language of the dilemma itself, or the language in which the model is instructed to express its reasoning. Standard evaluation conflates these by testing only matched conditions (e.g., English dilemma with English reasoning). We introduce a methodology that separately manipulates each factor, covering also mismatched conditions (e.g., English dilemma with Chinese reasoning), enabling decomposition of their contributions. To study what changes, we propose an approach to interpret the moral judgments in terms of Moral Foundations Theory. As a side result, we identify evidence for splitting the Authority dimension into a family-related and an institutional dimension. Applying this methodology to English-Chinese moral judgment with 13 LLMs, we demonstrate its diagnostic power: (1) in this EN–CN case study, the framework isolates the language of generated rationale as contributing roughly twice the variance of input-language effects; (2) it detects context-dependency in 4 of 9 EN–CN-tested models that standard evaluation misses; and (3) a diagnostic taxonomy translates these patterns into deployment guidance. We release our code and datasets at https://github.com/aida-ugent/LLMCrossCulturalMoralJudgement.
Effective prompt engineering remains a central challenge in fully harnessing the capabilities of LLMs. While well-designed prompts can dramatically enhance performance, crafting them typically demands expert intuition and a nuanced understanding of the task. Moreover, the most impactful prompts often hinge on subtle semantic cues, ones that may elude human perception but are crucial for guiding LLM behavior. In this paper, we introduce PRL (Prompts from Reinforcement Learning), a novel RL-based approach for automatic prompt generation. Unlike most prior methods, PRL can also generate novel few-shot demonstrations rather than selecting only from examples seen during training. Empirically, our approach achieves state-of-the-art performance across a diverse set of benchmarks, including text classification, text simplification, and mathematical reasoning, while remaining competitive on text summarization. Our code is available at https://github.com/Batorskq/prl.
Large reasoning models have demonstrated remarkable proficiency in various tasks. However, we observe that they frequently exhibit a problematic reliance on familiar reasoning patterns, a phenomenon we term reasoning rigidity. Despite explicit instructions from users, these models often override clearly stated conditions and default to habitual reasoning, leading to incorrect conclusions. This behavior presents significant challenges, not only in reasoning-intensive domains but also in realistic settings. To systematically investigate reasoning rigidity, a behavior unexplored in prior work, we introduce a dataset, ReasoningTrap, consisting of math problems, puzzles, and agentic tasks designed to diagnose reasoning rigidity. Using this dataset, we identify patterns that occur when models default to ingrained reasoning, and suggest inference-level and GRPO-based post-training remedies. Our diagnostic set and evaluation code are available at the project page to facilitate future research on mitigating reasoning rigidity in language models.
Generative search systems are increasingly replacing link-based retrieval with AI-generated summaries, yet little is known about how these systems differ in sources, language, and fidelity to cited material. We examine responses to 11,000 real search queries across five systems—vanilla GPT, Search GPT, Perplexity Search with Grok, Google AI Overviews, and traditional Google Search—at three levels: source diversity, linguistic characterization of the generated summary, and source-summary fidelity. We find that generative search systems exhibit significant source-selection biases in their citations, favoring certain sources over others. Incorporating search also selectively attenuates epistemic markers, reducing hedging by up to 60% while preserving confidence language in the AI-generated summaries. At the same time, AI summaries further compound the citation biases: Wikipedia and longer sources are disproportionately overrepresented, whereas cited social media content and negatively framed sources are substantially underrepresented. Our findings highlight the potential for answer bubbles, in which identical queries yield structurally different information realities across systems, with implications for user trust, source visibility, and the transparency of AI-mediated information access.
Recent representation analyses have reported structure-like or invariant behavior even in randomly initialized transformers, motivating the interpretation that aspects of linguistic structure may be architectural rather than learned. We show that such conclusions can be artifacts of coarse symbolic discretization. We formalize symbolic analysis of neural representations as operating over discretized activation trajectories and identify symbolic resolution as a critical, previously uncontrolled variable. Varying resolution reveals a sharp collapse of apparent similarity in random models, while pretrained models maintain stable, operator-selective differentiation across all tested resolutions. We introduce resolution stability as a necessary validity criterion for symbolic and automata-based analyses, providing a falsifiable, architecture-agnostic diagnostic for auditing internal linguistic structure in neural language models.
Peer review serves as a backbone of academic research, but in most AI conferences, the review quality is degrading as the number of submissions explodes. To reliably detect low-quality reviews, we define misinformed review points as either "weaknesses" in a review that contain incorrect premises, or "questions" in a review that can be already answered by the paper. We verify that 15.2% of weaknesses and 26.4% of questions are misinformed and introduce ReviewScore indicating if a review point is misinformed. To evaluate the factuality of each premise of weaknesses, we propose an automated engine that reconstructs every explicit and implicit premise from a weakness. We build a human expert-annotated ReviewScore dataset to check the ability of LLMs to automate ReviewScore evaluation. Then, we measure human-model agreements on ReviewScore using eight current state-of-the-art LLMs. The models show F1 scores of 0.4–0.5 and kappa scores of 0.3–0.4, indicating moderate agreement but also suggesting that fully automating the evaluation remains challenging. A thorough disagreement analysis reveals that most errors are due to models’ incorrect reasoning. We also prove that evaluating premise-level factuality shows significantly higher agreements than evaluating weakness-level factuality.
Two-sided digital platforms are inherently dynamic: user preferences shift, item popularity evolves, and reviews both reflect and drive these changes. Yet most sequential recommendation systems treat reviews as passive signals for updating user states, leaving two aspects underexplored. First, review generation is nonrandom, depending on evolving latent states of both users and items. Second, reviews can reshape item states, induce spillover across related items, and influence future user decisions. To address these gaps, we propose a two-sided state-space model (TS-SSM) for event-conditioned sequential recommendation. TS-SSM consists of three components: (1) a modality-missing-not-at-random fusion module that encodes review content and informative observation patterns; (2) user-state evolution with temporal variation and local graph message passing that uses related item states to refine user preferences; and (3) item-state evolution with asymmetric carryover of positive and negative review feedback. In experiments across six Amazon categories, TS-SSM increases Recall@20 over BSARec by 14.8%-18.8% and exceeds HM4SR by 11.7% on average. On Goodreads Fantasy, Recall@20 improves HM4SR from .5191 to .5847. Ablations highlight distinct contributions of observation patterns, local propagation, and item dynamics.
Alternative text is imperative for making the internet accessible to blind and low-vision individuals. Vision-language models (VLMs) have the potential to improve alt text coverage. This study considers how social stereotypes of race and gender are manifested in the alt-text language generated by GPT-4.1, Claude-Sonnet-4.5, and Gemini-2.5-Pro. A total of 1200 images are generated uniformly by gender and skin tone, and then the VLMs are prompted to generate the alternative text. The analysis of the generated alt text shows that the text descriptions differ significantly depending on the skin tone and gender of the person in an image. The VLMs tend to assume that whiteness is the default identity and the descriptions of those with darker skin are more likely to contain racialized terms. This bias suggests that AI-generated alt-text should be used with caution in the interest of preserving equity and preventing discrimination.
The tremendous commercial potential of large language models (LLMs) has heightened concerns over their unauthorized use. To address this, we focus on the task of identifying the origin of black-box LLMs. We further propose PlugAE, an effective and efficient identification method that proactively leverages LLM-specific adversarial embeddings and allows users to customize copyright tokens on a targeted query set. Extensive experiments demonstrate that PlugAE outperforms both state-of-the-art model watermarking and fingerprinting methods in accuracy and robustness. We further analyze its stealthiness and reliability from three complementary perspectives and conduct ablation studies under various configurations, confirming its practicality for real-world misuse detection.
Computational stylometry studies writing style through quantitative textual patterns, enabling applications such as authorship attribution, identity linking, and plagiarism detection. Despite the relevance of language modeling to these tasks, the pre-training of modern large language models (LLMs) has been underutilized in authorship attribution and verification. We introduce an unsupervised framework that uses the log-probabilities of an LLM to measure style transferability between two texts. This framework takes advantage of the extensive Causal Language Modeling (CLM) pre-training, one-shot capabilities and scale of LLMs, avoiding explicit supervision. Our methods substantially outperform prompting-based unsupervised baselines in authorship verification at similar model sizes, and is competitive with or improves contrastive baselines in most settings with sufficient model scale. We further observe strong performance across non-English languages. The effectiveness of the proposed framework improves consistently with increasing model scale. In the case of authorship verification, we propose an additional mechanism that increases test-time computation to improve accuracy; enabling flexible trade-offs between computational cost and task performance.
As Large Language Model (LLM) agents are increasingly deployed in open-ended domains like software engineering, they frequently encounter underspecified instructions that lack crucial context. While human developers naturally resolve underspecification by asking clarifying questions, current agents are largely optimized for autonomous execution. In this work, we systematically evaluate the clarification-seeking abilities of LLM agents on an underspecified variant of SWE-bench Verified. We propose an uncertainty-aware multi-agent scaffold that decouples underspecification detection from code execution. Across both proprietary and open-weight frontier LLMs, our scaffold achieves a 69.40% task resolve rate, significantly outperforming a standard single-agent setup and closing the performance gap with agents operating on fully specified instructions. Furthermore, we find that the multi-agent system exhibits well-calibrated information-seeking behavior, conserving queries on simple tasks while proactively seeking information on more complex issues. These findings indicate that current models can be turned into proactive collaborators, where agents independently recognize when to ask questions to elicit missing information in real-world, underspecified tasks.
Recent LLM-based embodied agents have advanced in high-level task planning. Yet, they frequently suffer from hallucinations and brittle reasoning due to passive observation—either omitting fine-grained attributes or overwhelming the planner with irrelevant information. We propose SceneQueryPlanner, a memory-environment dual-grounded agent that integrates active information seeking into the planning loop by decomposing instructions and selectively executing environmental interactions or structured queries over a dynamic 3D scene graph to retrieve task-relevant information on demand. By treating information acquisition as a first-class planning action, our approach enables explicit verification and supports reliable long-horizon reasoning over object attributes. For rigorous evaluation, we introduce AttPlan-Bench, a benchmark of multi-room, long-horizon household tasks requiring attribute-aware reasoning. Experiments demonstrate that our dual-grounding strategy outperforms passive baselines, yielding more stable and scalable reasoning across diverse tasks.
Recent multimodal sentiment analysis studies increasingly adopt text-centric fusion approaches to exploit the rich sentiment information inherent in the textual modality. However, these approaches often suffer from performance degradation during inference due to partially missing or noisy data in real-world scenarios, especially when sentiment-related cues are missing. To address this issue, we introduce a new completeness estimation approach that quantifies the degree of sentiment-relevant information preserved in incomplete data to guide the reconstruction of missing semantics. Furthermore, we propose a training strategy that stabilizes multi-task learning while jointly optimizing sentiment prediction and completeness estimation. Extensive experiments and in-depth analyses on three benchmark datasets demonstrate that the proposed approach enables more accurate semantic reconstruction, leading to more precise sentiment prediction.
Despite the impressive progress of recent MLLMs on spatio-temporal video grounding (STVG), existing evaluations and training data focus primarily on simple queries. They largely overlook the compositional queries prevalent in real-world scenarios, where a target must be disambiguated by jointly reasoning about its attributes and relations to other entities. To bridge this gap, we propose Compositional Spatio-Temporal Video Grounding (CompSTVG), a task that requires models to process complex textual queries where every intertwined attribute and relational cue is essential for disambiguation. To facilitate this task at scale, we build a synthetic data engine that leverages a spatio-temporal scene graph as a difficulty measure and casts difficulty-controlled query synthesis as a constraint programming problem, producing difficulty-graded data for both evaluation and training. Built on this engine, we introduce STVG-CompBench, a benchmark stratified by explicit difficulty levels that jointly capture temporal complexity and spatial interference. Evaluating 11 representative STVG models on STVG-CompBench reveals that current models perform poorly on compositional queries, exhibiting a sharp performance drop that is typically obscured by overall dataset-level averages. We further construct synthetic training data and propose CurrSTVG, a curriculum reinforcement learning framework that delivers consistent gains, with the largest improvements observed on the most challenging compositional queries.
Large language model (LLM) agents excel at complex, multi-step tasks but suffer from catastrophic forgetting during continual learning—acquiring new skills often degrades previously mastered capabilities. Existing post-training paradigms face a fundamental trade-off: Supervised Fine-Tuning (SFT) is sample-efficient but prone to severe forgetting, while Reinforcement Learning (RL) preserves prior capabilities but converges slowly without expert guidance. To bridge this gap, we introduce **T**rigger-based **S**upervised **R**einforcement **L**earning (TSRL), a framework that integrates the strengths of both paradigms through an adaptive, state-dependent expert supervision mechanism. TSRL employs RL as the optimization backbone, activating SFT selectively on "hard states" where the policy fails. Theoretically, we show that restricting supervision to failure states derives an upper bound on the cumulative parameter updates, thereby reducing forgetting compared to full SFT. Empirically, TSRL achieves consistent gains across three agent benchmarks (ALFWorld, WebShop, and Deep Search) at both 7B and 32B scales. In continual learning scenarios, our method retains up to 9.6 points more capability on prior tasks than SFT→RL baselines, while matching or exceeding target-task performance.
Conversational explainable AI (ConvXAI) agents often use GUS-style architectures with separate modules for intent recognition, dialogue management, and template-based response generation. While faithful to the underlying XAI methods, these pipelines are hard to adapt and often struggle with context-dependent follow-up questions. We present SimXAI, a lightweight LLM-centric conversational layer that replaces these modules with a single tool-calling LLM that bridges access to existing XAI resources. Rather than a standalone agent, SimXAI serves as a modular interface that enables conversational access to external XAI resources. We further introduce an evaluation suite for single- and multi-turn parsing, faithfulness, and contextualization. Across existing and new datasets, SimXAI achieves strong function-selection accuracy and high faithfulness while better handling context-dependent requests. In a user study replacing only the conversational layer of TalkToModel, a representative GUS-based agent, users completed tasks more accurately and in fewer turn. Overall, SimXAI improves GUS-based design while preserving faithfulness and improving conversational quality.
This paper presents a simple method that allows to easily enhance textual pre-trained large language models with speech information, when fine-tuned for a specific classification task. A classical issue with the fusion of many embeddings from audio with text is the large length of the audio sequence compared to the text one. Our method benefits from an existing speech tokenizer trained for Audio Speech Recognition that output long sequences of tokens from a large vocabulary, making it difficult to integrate it at low cost in a large language model. By applying a simple lasso-based feature selection on multimodal Bag-of-Words representation, we retain only the most important audio tokens for the task, and adapt the language model to them with a self-supervised language modeling objective, before fine-tuning it on the downstream task. We show this method improves the performances compared to an unimodal model, larger SpeechLMs, and audio integration via learned representations. We demonstrate its effectiveness on Argumentative Fallacy Detection and Classification tasks where audio was previously believed counterproductive, and affective computing tasks on a widely-used dataset. We also provide an in-depth analysis of the method, showing that even a random audio token selection helps enhancing the unimodal model. Our code is available online.
Politeness is key to building healthy communication. Brown and Levinson’s influential theory of politeness (BL’s theory) proposes that polite speech can be characterized by the presence of face-saving speech acts while impolite speech is characterized by the presence of face-threatening speech acts. Most existing politeness resources neither operationalize nor control politeness under BL’s theory; instead, they typically consist of short utterances with an intuitive account of politeness. In this paper, we introduce a long-text corpus that includes sentence-level face-act annotations and document-level politeness scores. Following BL’s theory, we annotate politeness along three dimensions: Directness vs. Indirectness, Positive Face Saving, and Negative Face Saving, and define Overall Politeness as their average. We formulate face-act tagging as a multi-label classification task and compare context-agnostic sentence classifiers to context-sensitive variants and sequence-labeling models. We also model document-level politeness via multi-target regression using the text, and enhance the regression model with the predictions of our face-act classifier. We show that context-sensitive classifiers outperform context-free classifiers for face-act tagging. Moreover, we demonstrate that for document-level politeness regression, enhancing the text with face-act predictions yields better performance. Our code and data are available online: alipanahroshad-oss/email-politeness-corpus
Large language models (LLMs) remain vulnerable to jailbreak attacks that elicit structured, credible misinformation. Existing benchmarks primarily track binary refusals, often overlooking the severity and exposure risk of the misinformation payload itself. In this work, we introduce Misinfo-Jailbreak Bench, a comprehensive framework that systematically maps diverse attack strategies to a claim-level scoring pipeline for precise risk quantification. Building on this diagnostic foundation, we propose Selective LoRA Ethical Surgery, a parameter-efficient defense mechanism. Instead of broad parameter updates, our approach identifies safety-relevant subspaces via contrastive representations to confine LoRA edits and employs inference-time gating to preserve benign capabilities. Experimental results on state-of-the-art models (e.g., Qwen2.5, Llama-4) demonstrate that our method reduces high-severity risk by 60-70% while limiting general capability degradation to less than 1%, offering a precise and minimally invasive paradigm for post-hoc alignment.
Structured data exists in many forms (tables, knowledge graphs, charts, and time series), and converting it into text may involve different generation tasks. However, most prior work on data-to-text (D2T) generation has focused on specific tasks and datasets, relying either on task-specific training data or on the zero-shot capabilities of large language models. We study cross-domain D2T generation in a setting where neither in-domain training text nor test references are available, and where domains, generation goals, and input structures vary substantially. We compare data-driven knowledge distillation (DDKD) against zero-shot inference and fine-tuning on out-of-domain D2T data, and introduce structure-preserving augmentation via structural subsampling and perturbation. Experiments on five benchmarks show that, at constant model size (1.7B parameters), DDKD consistently outperforms both fine-tuning and zero-shot inference. Moreover, the resulting small models outperform a much larger finetuned model on two of the five domains, achieving comparable performance on the remaining three. We further construct QUINTD-5, a fivefold extension of QUINTD-1, and show that simply scaling real target-domain inputs yields only modest gains, whereas our augmentation strategy remains more effective and more cost-efficient for cross-domain distillation.
Large language models (LLM) deployed as autonomous pricing agents may sustain supracompetitive prices through tacit coordination. We develop a causal graph divergence framework that separately measures structural faithfulness and intent faithfulness of LLM pricing agents in Bertrand competition. Across nine LLMs under duopoly and triopoly conditions, collusive behavior and chain-of-thought (CoT) faithfulness dissociate along both dimensions: the most collusive model accurately reports cooperative intent yet reasons structurally unfaithfully, while the most structurally faithful model sustains supra-Nash pricing under both market structures. These findings establish that CoT monitoring alone cannot serve as a standalone safeguard against algorithmic collusion.