Large language models (LLMs) exhibit social biases, prompting the development of various debiasing methods. However, debiasing methods may degrade the capabilities of LLMs. Previous research has evaluated the impact of bias mitigation primarily through tasks measuring general language understanding, which are often unrelated to social biases. In contrast, cultural commonsense is closely related to social biases, as both are rooted in social norms and values. The impact of bias mitigation on cultural commonsense in LLMs has not been well investigated. Considering this gap, we propose SOBACO (SOcial BiAs and Cultural cOmmonsense benchmark), a Japanese benchmark designed to evaluate social biases and cultural commonsense in LLMs in a unified format. We evaluate several LLMs on SOBACO to examine how debiasing methods affect cultural commonsense in LLMs. Our results reveal that the debiasing methods degrade the performance of the LLMs on the cultural commonsense task (up to 75% accuracy deterioration). These results highlight the importance of developing debiasing methods that consider the trade-off with cultural commonsense to improve fairness and utility of LLMs.
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RJE: A Retrieval-Judgment-Exploration Framework for Efficient Knowledge Graph Question Answering with LLMs
PDF ↗Knowledge graph question answering (KGQA) aims to answer natural language questions using knowledge graphs.Recent research leverages large language models (LLMs) to enhance KGQA reasoning, but faces limitations: retrieval-based methods are constrained by the quality of retrieved information, while agent-based methods rely heavily on proprietary LLMs.To address these limitations, we propose Retrieval-Judgment-Exploration (RJE), a framework that retrieves refined reasoning paths, evaluates their sufficiency, and conditionally explores additional evidence. Moreover, RJE introduces specialized auxiliary modules enabling small-sized LLMs to perform effectively: Reasoning Path Ranking, Question Decomposition, and Retriever-assisted Exploration. Experiments show that our approach with proprietary LLMs (such as GPT-4o-mini) outperforms existing baselines while enabling small open-source LLMs (such as 3B and 8B parameters) to achieve competitive results without fine-tuning LLMs.Additionally, RJE substantially reduces the number of LLM calls and token usage compared to agent-based methods, yielding significant efficiency improvements.
Unlike traditional search engines that present ranked lists of webpages, generative search engines rely solely on in-line citations as the key gateway to original real-world webpages, making it crucial to examine whether LLM-generated citations have biases—particularly for politically sensitive queries. To investigate this, we first construct AllSides-2024, a new dataset comprising the latest real-world news articles (Jan. 2024 - Dec. 2024) labeled with left- or right-leaning stances. Through systematic evaluations, we find that LLMs exhibit a consistent tendency to cite left-leaning sources at notably higher rates compared to traditional retrieval systems (e.g., BM25 and dense retrievers). Controlled experiments further reveal that this bias arises from a preference for media outlets identified as left-leaning, rather than for left-oriented content itself. Meanwhile, our findings show that while LLMs struggle to infer political bias from news content alone, they can almost perfectly recognize the political orientation of media outlets based on their names. These insights highlight the risk that, in the era of generative search engines, information exposure may be disproportionately shaped by specific media outlets, potentially shaping public perception and decision-making.
Scientific information extraction (SciIE) has primarily relied on entity-relation extraction in narrow domains, limiting its applicability to interdisciplinary research and struggling to capture the necessary context of scientific information, often resulting in fragmented or conflicting statements. In this paper, we introduce SciEvent, a novel multi-domain benchmark of scientific abstracts annotated via a unified event extraction (EE) schema designed to enable structured and context-aware understanding of scientific content. It includes 500 abstracts across five research domains, with manual annotations of event segments, triggers, and fine-grained arguments. We define SciIE as a multi-stage EE pipeline: (1) segmenting abstracts into core scientific activities—\textit{Background}, \textit{Method}, \textit{Result}, and \textit{Conclusion}; and (2) extracting the corresponding triggers and arguments. Experiments with fine-tuned EE models, large language models (LLMs), and human annotators reveal a performance gap, with current models struggling in domains such as sociology and humanities. SciEvent serves as a challenging benchmark and a step toward generalizable, multi-domain SciIE.
Web Intellectual Property at Risk: Preventing Unauthorized Real-Time Retrieval by Large Language Models
PDF ↗The protection of cyber Intellectual Property (IP) such as web content is an increasingly critical concern. The rise of large language models (LLMs) with online retrieval capabilities enables convenient access to information but often undermines the rights of original content creators. As users increasingly rely on LLM-generated responses, they gradually diminish direct engagement with original information sources, which will significantly reduce the incentives for IP creators to contribute, and lead to a saturating cyberspace with more AI-generated content. In response, we propose a novel defense framework that empowers web content creators to safeguard their web-based IP from unauthorized LLM real-time extraction and redistribution by leveraging the semantic understanding capability of LLMs themselves. Our method follows principled motivations and effectively addresses an intractable black-box optimization problem. Real-world experiments demonstrated that our methods improve defense success rates from 2.5% to 88.6% on different LLMs, outperforming traditional defenses such as configuration-based restrictions.
Aligning large language models (LLMs) with human preferences is a central challenge for building reliable AI systems. Most existing alignment approaches rely on static signals, such as predefined principles or offline human annotations to guide model behavior toward a fixed approximation of human preferences. However, LLMs can exhibit distributional drift during training, and static alignment mechanisms lack the capacity to adaptively correct misaligned behaviors as they emerge. To address this limitation, we develop a two-stage framework that enables dynamic and continuous alignment. In the first stage, a constitution is continually revised based on observed model behaviors, and models are trained to comply with these evolving principles. In the second stage, this learned constitution is used to guide reinforcement learning, encouraging the model to align with the updated normative signals. We refer to this framework as COCOA: Co-evolution of Constitutions and AI Models. We show that COCOA enables a 7B model to greatly improve safety—raising StrongReject score from 0.741 to 0.935 and Safe-RLHF accuracy from 77.76% to 90.64% without human annotations, reaching performance close to much larger state-of-the-art models.
Igniting Creative Writing in Small Language Models: LLM-as-a-Judge versus Multi-Agent Refined Rewards
PDF ↗Large Language Models (LLMs) have demonstrated remarkable creative writing capabilities, yet their substantial computational demands hinder widespread use. Enhancing Small Language Models (SLMs) offers a promising alternative, but current methods like Supervised Fine-Tuning (SFT) struggle with novelty, and Reinforcement Learning from Human Feedback (RLHF) is costly. This paper explores two distinct AI-driven reward strategies within a Reinforcement Learning from AI Feedback (RLAIF) framework to ignite the creative writing of a 7B-parameter SLM, specifically for generating Chinese greetings. The first strategy employs a Reward Model (RM) trained on high-quality preference data curated by a novel multi-agent rejection sampling framework designed for creative tasks. The second, more novel, strategy utilizes a principle-guided LLM-as-a-Judge, whose reward function is optimized via an adversarial training scheme with a reflection mechanism, to directly provide reward signals. Comprehensive experiments reveal that while both approaches significantly enhance creative output over baselines, the principle-guided LLM-as-a-Judge demonstrably yields superior generation quality. Furthermore, it offers notable advantages in training efficiency and reduced dependency on human-annotated data, presenting a more scalable and effective path towards creative SLMs. Our automated evaluation methods also exhibit strong alignment with human judgments.
Inter-sentence Context Modeling and Structure-aware Representation Enhancement for Conversational Sentiment Quadruple Extraction
PDF ↗Conversational aspect-based sentiment quadruple analysis (DiaASQ) is a newly-emergent task aiming to extract quadruples of target-aspect-opinion-sentiment from a conversation text. Existing studies struggle to capture complete dialogue semantics, largely due to inadequate inter-utterance modeling and the underutilization of dialogue structure. To address these issues, we propose an Inter-sentence Context Modeling and Structure-aware Representation Enhancement model (ICMSR) to extract dialogue aspect sentiment quadruple. We design the Dialog Inter-sentence Contextual Enhancer (DICE) module after the sentence-by-sentence encoding phase to enhance inter-sentence interactions and mitigate contextual fragmentation caused by traditional sequential encoding. Moreover, to fully exploit structural information within dialogues, we propose the Dialog Feature Amplifier (DFA), which consists of two submodules: STREAM and SMM. The STREAM module integrates diverse structural dialogue information to generate structure-aware sentence representations, effectively improving the modeling of intra-dialogue structural relations. Furthermore, the Structural Multi-scale Mechanism (SMM) employs a multi-scale modeling approach, simulating varying extents of contextual awareness, thereby enhancing the model’s ability to capture cross-sentence structural dependencies. We extensively evaluate our method on benchmark datasets, and the empirical results consistently confirm its effectiveness.
Mathematical reasoning through Chain-of-Thought (CoT) has emerged as a powerful capability of Large Language Models (LLMs), which can be further enhanced through Test-Time Scaling (TTS) methods like Beam Search and DVTS. However, these methods, despite improving accuracy by allocating more computational resources during inference, often suffer from path homogenization and inefficient use of intermediate results. To address these limitations, we propose Stepwise Reasoning Checkpoint Analysis (SRCA), a framework that introduces checkpoints between reasoning steps. It incorporates two key strategies: (1) Answer-Clustered Search, which groups reasoning paths by their intermediate checkpoint answers to maintain diversity while ensuring quality, and (2) Checkpoint Candidate Augmentation, which leverages all intermediate answers for final decision-making. Our approach effectively reduces path homogenization and creates a fault-tolerant mechanism by utilizing high-quality intermediate results. Experimental results show that SRCA improves reasoning accuracy compared to existing TTS methods across various mathematical datasets.
PychoAgent: Psychology-driven LLM Agents for Explainable Panic Prediction on Social Media during Sudden Disaster Events
PDF ↗Accurately predicting public panic sentiment on social media is crucial for proactive governance and crisis management. Current efforts on this problem face three main challenges: lack of finely annotated data hinders emotion prediction studies, unmodeled risk perception causes prediction inaccuracies, and insufficient interpretability of panic formation mechanisms limits mechanistic insight. We address these issues by proposing a Psychology-driven generative Agent framework (PsychoAgent) for explainable panic prediction based on emotion arousal theory. Specifically, we first construct a fine-grained panic emotion dataset (namely COPE) via human-AI (Large Language Models, LLMs) collaboration, combining scalable LLM-based labeling with human annotators to ensure accuracy for panic emotion and to mitigate biases from linguistic variations. Then, we construct PsychoAgent integrating cross-domain heterogeneous data grounded in psychological mechanisms to model risk perception and cognitive differences in emotion generation. To enhance interpretability, we design an LLM-based role-playing agent that simulates individual psychological chains through dedicatedly designed prompts. Experimental results on our annotated dataset show that PsychoAgent improves panic emotion prediction performance by 13% to 21% compared to baseline models. Furthermore, the explainability and generalization of our approach is validated. Crucially, this represents a paradigm shift from opaque “data-driven fitting” to transparent “role-based simulation with mechanistic interpretation” for panic emotion prediction during emergencies. Our implementation is publicly available at: https://github.com/supersonic0919/PsychoAgent.
Accurately evaluating the word sense disambiguation (WSD) capabilities of large language models (LLMs) remains challenging, as existing studies primarily rely on single-task evaluations and classification-based metrics that overlook the fundamental differences between generative LLMs and traditional classification models. To bridge this gap, we proposeRoDEval, the first comprehensive evaluation framework specifically tailored for assessing LLM-based WSD methods. RoDEval introduces four novel metrics: Disambiguation Scope, Disambiguation Robustness, Disambiguation Reliability, and Definition Generation Quality Score, enabling a multifaceted evaluation of LLMs’ WSD capabilities. Experimental results using RoDEval across five mainstream LLMs uncover significant limitations in their WSD performance. Specifically, incorrect definition selections in multiple-choice WSD tasks stem not from simple neglect or forget of correct options, but rather from incomplete acquisition of the all senses for polysemous words. Instead, disambiguation reliability is often compromised by the models’ persistent overconfidence. In addition, inherent biases continue to affect performance, and scaling up model parameters alone fails to meaningfully enhance their ability to generate accurate sense definitions. These findings provide actionable insights for enhancing LLMs’ WSD capabilities. The source code and evaluation scripts are open-sourced at https://github.com/DayDream405/RoDEval.
Are Large Language Models Chronically Online Surfers? A Dataset for Chinese Internet Meme Explanation
PDF ↗Large language models (LLMs) are trained on vast amounts of text from the Internet, but do they truly understand the viral content that rapidly spreads online—commonly known as memes? In this paper, we introduce CHIME, a dataset for CHinese Internet Meme Explanation. The dataset comprises popular phrase-based memes from the Chinese Internet, annotated with detailed information on their meaning, origin, example sentences, types, etc. To evaluate whether LLMs understand these memes, we designed two tasks. In the first task, we assessed the models’ ability to explain a given meme, identify its origin, and generate appropriate example sentences. The results show that while LLMs can explain the meanings of some memes, their performance declines significantly for culturally and linguistically nuanced meme types. Additionally, they consistently struggle to provide accurate origins for the memes. In the second task, we created a set of multiple-choice questions (MCQs) requiring LLMs to select the most appropriate meme to fill in a blank within a contextual sentence. While the evaluated models were able to provide correct answers, their performance remains noticeably below human levels. We have made CHIME public and hope it will facilitate future research on computational meme understanding.
Incorporating Diverse Perspectives in Cultural Alignment: Survey of Evaluation Benchmarks Through A Three-Dimensional Framework
PDF ↗Large Language Models (LLMs) increasingly serve diverse global audiences, making it critical for responsible AI deployment across cultures. While recent works have proposed various approaches to enhance cultural alignment in LLMs, a systematic analysis of their evaluation benchmarks remains needed. We propose a novel framework that conceptualizes alignment along three dimensions: Cultural Group (who to align with), Cultural Elements (what to align), and Awareness Scope (how to align: majority-focused vs. diversity-aware). Through this framework, we analyze 105 cultural alignment evaluation benchmarks, revealing significant imbalances: Region (37.9%) and Language (28.9%) dominate Cultural Group representation; Social and Political Relations (25.1%) and Speech and Language (20.9%) concentrate Cultural Elements coverage; and an overwhelming majority (97.1%) of datasets adopt majority-focused Awareness Scope approaches. In a case study examining AI safety evaluation across nine Asian countries (Section 5), we demonstrate how our framework reveals critical gaps between existing benchmarks and real-world cultural biases identified in the study, providing actionable guidance for developing more comprehensive evaluation resources tailored to specific deployment contexts.
Recent studies have shown that Large Language Models (LLMs) augmented with chain-of-thought (CoT) reasoning demonstrate impressive problem-solving abilities. However, in this work, we identify a recurring issue where these models occasionally generate overly short reasoning, leading to degraded performance on even simple mathematical problems. Specifically, we investigate how reasoning length is embedded in the hidden representations of reasoning models and its impact on accuracy. Our analysis reveals that reasoning length is governed by a linear direction in the representation space, allowing us to induce overly short reasoning by steering the model along this direction. Building on this insight, we introduce ThinkEdit, a simple yet effective weight-editing approach to mitigate the issue of overly short reasoning. We first identify a small subset of attention heads (approximately 4%) that predominantly drive short reasoning behavior. We then edit the output projection weights of these heads to remove the short reasoning direction. With changes to only 0.2% of the model’s parameters, ThinkEdit effectively reduces overly short reasoning and yields notable accuracy gains for short reasoning outputs (+6.39%), along with an overall improvement across multiple math benchmarks (+3.34%). Our findings provide new mechanistic insights into how reasoning length is controlled within LLMs and highlight the potential of fine-grained model interventions to improve reasoning quality.
REACT: Representation Extraction And Controllable Tuning to Overcome Overfitting in LLM Knowledge Editing
PDF ↗Large language model editing methods frequently suffer from overfitting, wherein factual updates can propagate beyond their intended scope, overemphasizing the edited target even when it’s contextually inappropriate. To address this challenge, we introduce REACT (Representation Extraction And Controllable Tuning), a unified two-phase framework designed for precise and controllable knowledge editing. In the initial phase, we utilize tailored stimuli to extract latent factual representations and apply Principal Component Analysis with a simple learnbale linear transformation to compute a directional “belief shift” vector for each instance. In the second phase, we apply controllable perturbations to hidden states using the obtained vector with a magnitude scalar, gated by a pre-trained classifier that permits edits only when contextually necessary. Relevant experiments on EVOKE benchmarks demonstrate that REACT significantly reduces overfitting across nearly all evaluation metrics, and experiments on COUNTERFACT and MQuAKE shows that our method preserves balanced basic editing performance (reliability, locality, and generality) under diverse editing scenarios.
While large language models (LLMs) achieve strong performance on text-to-SQL parsing, they sometimes exhibit unexpected failures in which they are confidently incorrect. Building trustworthy text-to-SQL systems thus requires eliciting reliable uncertainty measures from the LLM. In this paper, we study the problem of providing a calibrated confidence score that conveys the likelihood of an output query being correct. Our work is the first to establish a benchmark for post-hoc calibration of LLM-based text-to-SQL parsing. In particular, we show that Platt scaling, a canonical method for calibration, provides substantial improvements over directly using raw model output probabilities as confidence scores. Furthermore, we propose a method for text-to-SQL calibration that leverages the structured nature of SQL queries to provide more granular signals of correctness, named “sub-clause frequency” (SCF) scores. Using multivariate Platt scaling (MPS), our extension of the canonical Platt scaling technique, we combine individual SCF scores into an overall accurate and calibrated score. Empirical evaluation on two popular text-to-SQL datasets shows that our approach of combining MPS and SCF yields further improvements in calibration and the related task of error detection over traditional Platt scaling.
Edge deployment of task-oriented semantic parsers demands high accuracy under tight latency and memory budgets. We present Grammar Pruning, a lightweight zero-shot framework that begins with a user-defined schema of API calls and couples a rule-based entity extractor with an iterative grammar-constrained decoder: extracted items dynamically prune the context-free grammar, limiting generation to only those intents, slots, and values that remain plausible at each step. This aggressive search-space reduction both reduces hallucinations and slashes decoding time. On the adapted FoodOrdering, APIMixSNIPS, and APIMixATIS benchmarks, Grammar Pruning with small language models achieves an average execution accuracy of over 90%—rivaling State-of-the-Art, cloud-based solutions—while sustaining at least 2x lower end-to-end latency than existing methods. By requiring nothing beyond the domain’s full API schema values yet delivering precise, real-time natural-language understanding, Grammar Pruning positions itself as a practical building block for future edge-AI applications that cannot rely on large models or cloud offloading.
Peer review is essential for scientific progress but faces growing challenges due to increasing submission volumes and reviewer fatigue. Existing automated review approaches struggle with factual accuracy, rating consistency, and analytical depth, often generating superficial or generic feedback lacking the insights characteristic of high-quality human reviews. We introduce ReviewRL, a reinforcement learning framework for generating comprehensive and factually grounded scientific paper reviews. Our approach combines: (1) an ArXiv-MCP retrieval-augmented context generation pipeline that incorporates relevant scientific literature, (2) supervised fine-tuning that establishes foundational reviewing capabilities, and (3) a reinforcement learning procedure with a composite reward function that jointly enhances review quality and rating accuracy. Experiments on ICLR 2025 papers demonstrate that ReviewRL significantly outperforms existing methods across both rule-based metrics and model-based quality assessments. ReviewRL establishes a foundational framework for RL-driven automatic critique generation in scientific discovery, demonstrating promising potential for future development in this domain. The implementation of ReviewRL will be released at GitHub.
Despite biases in Large Language Models (LLMs) being widely researched, systematic explorations of political biases in news article generation tasks remain underexplored. This study evaluates political bias across seven LLMs by leveraging our PublicViews dataset-extracted from the TwinViews-13K corpus-comprising 31 topics and 31,692 statements. We analyze 10,850 articles, finding left-leaning bias persists in generation tasks, with neutral content remaining rare even under balanced opinion settings. Models exhibit asymmetric behavior in minority opinion scenarios, amplifying preferred viewpoints when in minority while conforming to majority opinions otherwise. Notably, all models employ ‘stance-flipping quotations” (altering supporters’ statements to express opposite viewpoints) in 33-38% of quotations despite explicit instructions against distortion. Consistent with prior research, increased model size failed to enhance neutrality. This research measures political bias in LLM-generated news, analyzes its mechanisms, and reveals how opinion distribution and explicitness affect bias expression. Our results highlight how LLMs can introduce unintended political bias in generative contexts. We publicly release our PublicViews corpus and code at https://anonymous.4open.science/r/Fair-or-Framed-46F1.
Large Language Models (LLMs) have been shown to achieve breakthrough performances on complex logical reasoning tasks. Nevertheless, most existing research focuses on employing formal language to guide LLMs for deriving reliable reasoning paths, with systematic evaluations of these capabilities still being limited. In this paper, we aim to conduct a comprehensive evaluation of LLMs across various logical reasoning problems utilizing formal languages. From the perspective of three dimensions, i.e., spectrum of LLMs, taxonomy of tasks, and format of trajectories, our key findings are: 1) Thinking models significantly outperform Instruct models, especially when formal language is employed; 2). All LLMs exhibit limitations in inductive reasoning capability, irrespective of whether they use a formal language; 3). Data with PoT format achieves the best generalization performance across other languages. Additionally, we also curate the formal-relative training data to further enhance the small language models, and the experimental results indicate that a simple rejected fine-tuning method can better enable LLMs to generalize across formal languages and achieve the best overall performance.