Accurate question answering (QA) in disaster management requires reasoning over uncertain and conflicting information, a setting poorly captured by existing benchmarks built on clean evidence. We introduce DisastQA, a rigorously verified benchmark of 3,000 expert-annotated questions (2,000 multiple-choice and 1,000 open-ended) spanning eight disaster types. The benchmark is constructed via a human-LLM collaboration pipeline with stratified sampling to ensure balanced coverage. Models are evaluated under varying evidence conditions, from closed-book to noisy evidence integration, enabling separation of internal knowledge from reasoning under imperfect information. For open-ended QA, we propose a human-verified keypoint-based evaluation protocol emphasizing factual completeness over verbosity. Experiments with 20 models reveal substantial divergences from general-purpose leaderboards such as MMLU-Pro. While recent open-weight models approach proprietary systems in clean settings, performance degrades sharply under realistic noise, exposing critical reliability gaps for disaster response. All code, data, and evaluation resources are available at https://github.com/TamuChen18/DisastQA
论文检索
输入标题、作者或关键词，从 1,386 篇学术成果中精准定位
MedScore: Generalizable Factuality Evaluation of Open-ended Long-form Medical Answers by Domain-adapted Claim Decomposition and Verification
PDF ↗While Large Language Models (LLMs) can generate fluent and convincing responses, they are not necessarily correct. This is especially apparent in the popular decompose-then-verify factuality evaluation pipeline, where LLMs evaluate generated text by decomposing it into individual, valid claims. Factuality evaluation is especially important for medical answers, since incorrect medical information could seriously harm the patient. However, existing factuality systems are a poor match for the medical domain, as they are typically only evaluated on objective, entity-centric, formulaic texts such as biographies and historical topics. This differs from condition-dependent, conversational, hypothetical, sentence-structure diverse, and subjective medical answers, making decomposition into valid facts challenging. We propose MedScore, a new pipeline to decompose medical answers into condition-aware valid facts and verify against in-domain corpora. Our method extracts up to three times as many valid facts as existing methods, reducing hallucination and vague references, and retaining condition-dependency in facts. We also find MedScore is generalizable to non-medical domains without any specific tuning. The resulting factuality score substantially varies by decomposition method, verification corpus, and used backbone LLM, highlighting the importance of customizing each step for reliable factuality evaluation by using our generalizable and modularized pipeline for domain adaptation.
Large Language Models (LLMs) can generate factually inaccurate content even if they have corresponding knowledge, which critically undermines their reliability. Existing approaches attempt to mitigate this by incorporating uncertainty in QA prompt during training, but these numerical scores lack the semantic richness for LLM to properly understand its internal states of trustworthiness and honestness, leading to insufficient factuality alignment. We introduce FAITH (Factuality Alignment through Integrating Trustworthiness and Honestness), a post-training framework for factuality alignment that integrates natural-language uncertainty signals with external knowledge. Specifically, we augment training datasets by computing confidence scores and semantic entropy from LLM outputs and mapping them into a knowledge state quadrant that describes the model’s internal knowledge possession (trustworthiness) and answering behaviors (honestness) in natural language. Based on this enhanced data, we design a reward function that considers both correctness and uncertainty signals, and fine-tune the LLM using the Proximal Policy Optimization (PPO) algorithm. To further mitigate weakly grounded responses, we design a retrieval-augmented module that retrieves relevant external passages, improving the consistency between internal and external knowledge representations. Extensive experiments on four knowledge-intensive benchmarks demonstrate that FAITH enhances the factual accuracy and truthfulness of LLMs.
MINED: Probing and Updating with Multimodal Time-Sensitive Knowledge for Large Multimodal Models
PDF ↗Large Multimodal Models (LMMs) encode rich factual knowledge via cross-modal pre-training, yet their static representations struggle to maintain an accurate understanding of time-sensitive knowledge. Existing benchmarks remain constrained by static designs, inadequately evaluating LMMs’ ability to understand time-sensitive knowledge. To address this gap, we propose MINED, a comprehensive benchmark containing 2,104 time-sensitive knowledge samples spanning six knowledge types, which evaluates temporal awareness along 6 key dimensions and 11 challenging tasks: cognition, awareness, trustworthiness, understanding, reasoning, and robustness. Evaluating 15 widely used LMMs on MINED shows that Gemini-2.5-Pro achieves the highest average CEM score of 63.07, while most open-source LMMs still lack time understanding ability. Meanwhile, LMMs perform best on organization knowledge, whereas their performance is weakest on sport. To address these challenges, we investigate the feasibility of updating time-sensitive knowledge in LMMs through knowledge editing methods and observe that LMMs can effectively update knowledge via knowledge editing methods in single editing scenarios.
Large language models often hallucinate, producing content that is factually incorrect or not grounded in the sources. Reliable faithfulness verification is critical for trustworthy deployment. In the provided-source (closed-world) setting, existing verifiers either classify whole passages in one step or check sentences independently, overlooking cross-sentence context. We present ContextCheck, a framework for sentence-level faithfulness verification with context-aware disambiguation. Each sentence is verified against the grounding document while conditioning on preceding sentences, enabling pronouns and references to be resolved directly in context. This design avoids the separate decontextualization step of rewriting claims into self-contained forms, casting verification as a context-conditioned task. Fine-tuned from Llama-3.1-8B-Instruct, ContextCheck sets a new state of the art on three context-dependent datasets; it improves Macro F1 by over 10 points compared to the strongest baselines, and matches or slightly surpasses the strongest baselines on 14 standard single-sentence datasets compared to prior 8B-scale verifiers (average Macro F1 73.5 vs. 72.8). These results show that ContextCheck offers a practical and effective approach for sentence-level hallucination detection.
Spotlight and Shadow: Attention-Guided Dual-Anchor Introspective Decoding for MLLM Hallucination Mitigation
PDF ↗Multimodal Large Language Models (MLLMs) have demonstrated remarkable reasoning capabilities yet continue to suffer from hallucination, where generated text contradicts visual content. In this paper, we introduce Dual-Anchor Introspective Decoding (DaID), a novel contrastive decoding framework that dynamically calibrates each token generation by mining the model’s internal perceptual discrepancies. Specifically, DaID identifies a Spotlight layer to amplify visual factual signals and a Shadow layer to suppress textual inertia. By leveraging visual attention distributions to guide this dual-anchor selection process, our method ensures precise, token-specific adaptation. Experimental results across multiple benchmarks and MLLMs demonstrate that DaID significantly mitigates hallucination while enhancing general reasoning capabilities.
Multimodal large language models (MLLMs) are increasingly deployed in Web-scale applications—such as image search, social media captioning, and e-commerce product description generation—where factual consistency is critical for user trust and content reliability. However, we observe that MLLMs frequently hallucinate in these settings due to two relevant phenomena: the massive activation phenomenon and positional information decay. The former refers to the tendency of attention mechanisms to concentrate on a small set of tokens with extreme activation values in query and key projections, which play indispensable roles in contextual understanding. In our investigation, we discover that perturbing these tokens leads to significant performance drops, highlighting their utmost importance. As for positional information decay, it occurs due to the common rotary position encoding strategy, where the attention to early visual tokens diminishes over time, especially in long-sequence generation tasks, such as image caption. To address these challenges, we propose TokenTruth, a token-level intervention strategy that dynamically suppresses irrelevant visual tokens while preserving key contextual signals. Our method is grounded in an in-depth analysis of massive activations and attention sink behaviors, and introduces a targeted token penalty mechanism that reallocates attention more faithfully toward informative visual regions. Extensive experiments demonstrate that TokenTruth significantly improves factual consistency across various MLLMs on standard image understanding benchmarks.
The Mechanics of Interference: Defusing Distractors in RAG via Sparse Autoencoder Interventions
PDF ↗Large language models exhibit a critical vulnerability to distractor interference in retrieval-augmented contexts: they fail to prioritize relevant, factually correct documents over topically similar but misleading content. We introduce Lat-Defuse, a mechanistic framework that corrects this failure mode through targeted interventions in the model’s latent space. Using Sparse Autoencoders (SAEs), our method operates in an interpretable feature space and formulates correction as constrained counterfactual optimization. On Gemma-2 and Llama-3 model families across three QA benchmarks (BioASQ, Natural Questions, PopQA), our method achieves recovery rates of up to 94% on distractor-vulnerable samples. Successful correction through sparse modifications reveals distractor interference as a localized, systematically addressable phenomenon, opening directions toward universal distractor robustness in LLMs.
CoDA: Restoring Contextual Dominance via Copy-Encouraged Attention Intervention for Mitigating RAG Hallucinations
PDF ↗Retrieval-augmented generation reduces hallucination by grounding model outputs in external evidence, yet hallucinations can still occur even when the retrieved context is accurate and sufficient. From the perspective of information routing in the residual stream, this reflects an imbalance where internal parametric knowledge overwhelms external context during generation. We present an attention-centric analysis of RAG hallucination under valid evidence, showing that hallucinated and factual tokens diverge in mid-to-late Transformer layers as context-selective attention routing weakens, allowing parametric influence to dominate the residual stream. Motivated by prior studies showing that some attention heads—often referred to as copying heads—exhibit stronger information transport capacity, we aim to extend similar evidence-carrying behavior to a broader set of attention heads. To this end, we introduce CoDA, a lightweight inference-time attention intervention that amplifies evidence-aligned value states, enabling more attention heads to transport reliable external evidence in a copy-encouraged manner. Experiments demonstrate that CoDA improves contextual faithfulness, reduces hallucination, and remains robust under long and noisy contexts with modest and stable inference overhead.
Follow the Path: Reasoning over Knowledge Graph Paths to Improve Large Language Model Factuality
PDF ↗We introduce fs1, a simple yet effective method that improves the factuality of reasoning traces by sourcing them from large reasoning models and grounding them by conditioning on knowledge graph (KG) paths. We fine-tune eight instruction-tuned Large Language Models (LLMs) on 3.9K factually grounded reasoning traces and rigorously evaluate them on six complex open-domain question-answering (QA) benchmarks encompassing 23.9K questions. Our results demonstrate that our fs1-tuned model consistently outperforms instruction-tuned counterparts with parallel sampling by 6-14 absolute points (pass@). Our detailed analysis shows that fs1 considerably improves model performance over more complex questions (requiring 3 or more hops on KG paths) and numerical answer types compared to the baselines. Furthermore, in single-pass inference, we notice that smaller LLMs show the most improvements. While prior works demonstrate the effectiveness of reasoning traces primarily in the STEM domains, our work shows strong evidence that anchoring reasoning to factual KG paths is a critical step in transforming LLMs for reliable knowledge-intensive tasks.
Propaganda Signals in LLMs: Perspectival Divergence and Narrative Framing in the Russia-Ukraine War
PDF ↗Large Language Models (LLMs) are increasingly used to explain, summarize, and translate real-world events, including ongoing geopolitical conflicts. Yet it remains unclear whether they reproduce conflict-specific propaganda and, if so, how this appears in their outputs. We study this question for the Russia-Ukraine war through perspectival divergence, the extent to which model outputs align with competing narratives from different information ecosystems. We construct a conflict-aware evaluation set of neutral English event statements paired with Russian (RU)- and Ukrainian (UA)-oriented reference texts drawn from news outlets and Telegram channels. We then evaluate multiple LLMs under several prompting contexts using a reference-based semantic distance metric that measures directional proximity to RU- and UA-oriented references. To explain not only which side a model is closer to but also how that alignment is expressed, we further analyze outputs using five propaganda-relevant categories: Framing Narrative, Emotional Manipulation, Source Credibility, Social Pressure Identity, and Toponymy Naming. Across models, we find stable, model-specific leanings and technique profiles that persist across prompts and are not captured by standard factuality-oriented metrics. Our findings show that models that appear neutral under conventional evaluations can still encode systematic, conflict-specific propaganda patterns, underscoring the need for conflict-aware evaluation frameworks.
FIFA: Unified Faithfulness Evaluation Framework for Text-to-Video and Video-to-Text Generation
PDF ↗Video Multimodal Large Language Models (VideoMLLMs) have achieved remarkable progress in both Video-to-Text and Text-to-Video tasks. However, they often suffer from hallucinations, generating content that contradicts the visual input. Existing evaluation methods are limited to one task (V2T) and also fail to assess hallucinations in open-ended, free-form responses. To address this gap, we propose FIFA, a unified FaIthFulness evAluation framework that extracts comprehensive descriptive facts, models their semantic dependencies via a Spatio-Temporal Semantic Dependency Graph, and verifies them using VideoQA models. We further introduce , a tool-based correction framework that revises hallucinated content. Extensive experiments demonstrate that FIFA aligns more closely with human judgment than existing evaluation methods, and that effectively improves factual consistency in both text and video generation.
Understanding LLMs’ summarization capabilities: an analysis of biomedical abstract and lay summary generation
PDF ↗Scientific abstracts and lay summaries serve distinct but critical roles in research communication. Abstracts use technical language for academic audiences, while lay summaries aim to make findings accessible to non-specialists. With the rise of large language models (LLMs), there is increasing interest in automating the generation of both types of summaries—especially in the biomedical domain, where clarity and factual accuracy are essential. This study evaluates the performance of lightweight LLMs (under 8B parameters) in generating biomedical abstracts and lay summaries in a zero-shot setting. We assess outputs across three key dimensions: relevance, readability, and factuality. Additionally, we introduce a novel analysis of the sectional origin and desirability of information—where desirability reflects the utility of content from the reader’s perspective. We further compare human and LLM preferences using an objective ranking task. Our results show that LLM-generated summaries often contain comparable levels of desirable information to gold-standard human references. In several cases, LLM outputs are preferred by human evaluators and occasionally mistaken for human-authored text. These findings demonstrate the potential of lightweight LLMs for scalable, high-quality summarization and suggest their practical use in domains requiring both technical and accessible communication. The codebase for this study is publicly available on GitHub: https://github.com/batuinmetz/Understanding-LLMs-summarization-capabilities
Knowledge editing methods such as ROME and MEMIT update factual associations in transformer models by modifying MLP weights. While evaluated mainly by output behavior, their internal mechanism remains underexplored. We investigate whether edits rely on a common mechanism, regardless of which fact is modified. Despite fact-specific weight changes, we argue that ROME and MEMIT target the same subset of weights critical for maintaining edits. To isolate this subset, we train a compact binary mask over the edited weights. The mask reverses 80% of edits on the training set and over 70% on the test set, confirming that diverse edits share a common functional structure. Our analysis reveals that the mask reverses edits by eliminating overattention in later layers. Additionally, we show that injecting the mask during editing drops editing success from 98% to 38%, demonstrating that this mechanism is necessary for edits to succeed. Our finding that edits suppress rather than overwrite knowledge explains why ROME and MEMIT fail to propagate changes to related facts. The identified common functional subspace informs detection and defense against unwanted edits.
While most reading comprehension benchmarks for LLMs focus on factual information that can be answered by localizing specific textual evidence, many real-world tasks require understanding distributional information, such as population-level trends and preferences expressed across collections of text. We introduce Text2DistBench, a reading comprehension benchmark for evaluating LLMs’ ability to infer distributional knowledge from natural language. Built from real-world YouTube comments about movie and music entities, the benchmark provides models with entity metadata and associated comments, and requires them to answer distributional questions, such as estimating the proportions of positive and negative comments, or identifying the most and second most frequent topics discussed among viewers. To support reliable and long-term evaluation, the construction pipeline of Text2DistBench is fully automated and continuously updated to incorporate newly emerging entities over time. Experiments across multiple LLMs show that while models substantially outperform random baselines, performance varies widely across different distribution types and characteristics. These findings highlight both the capabilities and limitations of current LLMs in distributional reading comprehension and demonstrate the value of Text2DistBench as a practical and scalable testbed for future research.
Logical Structure as Knowledge: Enhancing LLM Reasoning via Structured Logical Knowledge Density Estimation
PDF ↗The reasoning capabilities of Large Language Models (LLMs) are increasingly attributed to training data quality rather than mere parameter scaling. However, existing data-centric paradigms often equate quality with factuality or diversity and ignore the internal logical complexity of training samples. In this work, we propose that natural language harbors Structured Logical Knowledge manifested through entailment relationships and logical topologies. To quantify this, we introduce Structured Logical Knowledge Density (SLKD), a novel metric that measures logical information content by decomposing natural language into executable predicates and logical primitives. Our analysis reveals a significant logical disparity in current datasets where sparse logical signals predominate. Consequently, we propose a density-aware re-cognizing optimization strategy that prioritizes high-density logical samples to align training with the model’s reasoning boundary. Extensive experiments demonstrate that our approach enhances reasoning performance and generalization without increasing total data volume. These results, further validated within a reinforcement learning framework, suggest that elevating logical density is more critical than expanding data scale for realizing the full cognitive potential of LLMs. The anonymized code is available in the Appendix C.
What happens when a storyteller forgets its own story? Large Language Models (LLMs) can now generate narratives spanning tens of thousands of words, but they often fail to maintain consistency throughout. When generating long-form narratives, these models can contradict their own established facts, character traits, and world rules. Existing story generation benchmarks focus mainly on plot quality and fluency, leaving consistency errors largely unexplored. To address this gap, we present ConStory-Bench, a benchmark designed to evaluate narrative consistency in long-form story generation. It contains 2,000 prompts across four task scenarios and defines a taxonomy of five error categories with 19 fine-grained subtypes. We also develop ConStory-Checker, an automated pipeline that detects contradictions and grounds each judgment in explicit textual evidence. Evaluating a range of LLMs through five research questions, we find that consistency errors show clear tendencies: they are most common in factual and temporal dimensions, tend to appear around the middle of narratives, occur in text segments with higher token-level entropy, and certain error types tend to co-occur. These findings can inform future efforts to improve consistency in long-form narrative generation.
Large Language Models (LLMs) are challenged by generating hallucinations and factually incorrect responses, particularly in complex and specialized medical question answering (QA). Integrating knowledge graphs (KGs) through retrieval-augmented generation (RAG) methods has emerged as a promising direction. However, existing graph-based RAG methods heuristically retrieve and refine question-relevant subgraphs, potentially introducing redundant and noisy factual information that is difficult for LLMs to process, ultimately limiting reasoning capability. To incorporate a concise yet informative evidence subgraph, we propose an iterative medical QA framework. It optimizes graph-based RAG methods by selectively retrieving focused knowledge from KGs to construct a precise evidence subgraph and progressively pruning it utilizing structured feature representations. The targeted KG integration maintains coherent and reliable inference. Experiments on three medical QA benchmark datasets demonstrate that the framework achieves state-of-the-art performance against representative baseline competitors, highlighting the importance of efficient KG integration.
When Personalization Misleads: Understanding and Mitigating Hallucinations in Personalized LLMs
PDF ↗Personalized large language models (LLMs) adapt model behavior to individual users to enhance user satisfaction, yet personalization can inadvertently distort factual reasoning. We show that when personalized LLMs face factual queries, there exists a phenomenon where the model generates answers aligned with a user’s prior history rather than the objective truth, resulting in **personalization-induced hallucinations** that degrade factual reliability and may propagate incorrect beliefs, due to representational entanglement between personalization and factual representations. To address this issue, we propose **Factuality-Preserving Personalized Steering (FPPS)**, a lightweight inference-time approach that mitigates personalization-induced factual distortions while preserving personalized behavior. We further introduce **PFQABench**, the first benchmark designed to jointly evaluate factual and personalized question answering under personalization. Experiments across multiple LLM backbones and personalization methods show that FPPS substantially improves factual accuracy while maintaining personalized performance.
Breaking Language Preference in Multilingual RAG via Language-Controllable Retrieval and Language-Agnostic Reasoning
PDF ↗Retrieval-Augmented Generation (RAG) significantly improves the factual accuracy and generation quality of large language models by incorporating external knowledge. However, in multilingual settings, RAG systems suffer from severe language preference. On the one hand, the retrieval stage is sensitive to the query language: semantically equivalent queries expressed in different languages often lead to substantially different retrieval results. On the other hand, when retrieved documents contain knowledge written in multiple languages, large language models tend to be influenced by surface-level language forms, rather than reasoning solely based on semantic relevance to the query.To address these challenges, we propose a unified optimization framework that explicitly disentangles multilingual RAG into language-controllable retrieval and language-agnostic reasoning. Our framework allows LLM to adaptively select retrieval languages while enforcing cross-lingual consistency during reasoning, thereby mitigating language bias without modifying existing retrievers or translators. Experimental results demonstrate that our approach effectively reduces language bias in multilingual RAG and consistently outperforms baselines across multiple multilingual benchmarks.