Austria

The widespread use of Large Multimodal Models (LMMs) has raised concerns about model toxicity. However, current research mainly focuses on explicit toxicity, with less attention to some more implicit toxicity regarding prejudice and discrimination. To address this limitation, we introduce a subtler type of toxicity named dual-implicit toxicity and a novel toxicity benchmark termed MDIT-Bench: Multimodal Dual-Implicit Toxicity Benchmark. Specifically, we first create the MDIT-Dataset with dual-implicit toxicity using the proposed Multi-stage Human-in-loop In-context Generation method. Based on this dataset, we construct the MDIT-Bench, a benchmark for evaluating the sensitivity of models to dual-implicit toxicity, with 317,638 questions covering 12 categories, 23 subcategories, and 780 topics. MDIT-Bench includes three difficulty levels, and we propose a metric to measure the toxicity gap exhibited by the model across them. In the experiment, we conducted MDIT-Bench on 13 prominent LMMs, and the results show that these LMMs cannot handle dual-implicit toxicity effectively. The model&#39;s performance drops significantly in hard level, revealing that these LMMs still contain a significant amount of hidden but activatable toxicity. The data will be released upon the paper&#39;s acceptance.

ACL 2025

MDIT-Bench: Evaluating the Dual-Implicit Toxicity in Large Multimodal Models

large multimodal models

benchmark

metrics

dataset

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The widespread use of Large Multimodal Models (LMMs) has raised concerns about model toxicity. However, current research mainly focuses on explicit toxicity, with less attention to some more implicit toxicity regarding prejudice and discrimination. To address this limitation, we introduce a subtler type of toxicity named dual-implicit toxicity and a novel toxicity benchmark termed MDIT-Bench: Multimodal Dual-Implicit Toxicity Benchmark. Specifically, we first create the MDIT-Dataset with dual-implicit toxicity using the proposed Multi-stage Human-in-loop In-context Generation method. Based on this dataset, we construct the MDIT-Bench, a benchmark for evaluating the sensitivity of models to dual-implicit toxicity, with 317,638 questions covering 12 categories, 23 subcategories, and 780 topics. MDIT-Bench includes three difficulty levels, and we propose a metric to measure the toxicity gap exhibited by the model across them. In the experiment, we conducted MDIT-Bench on 13 prominent LMMs, and the results show that these LMMs cannot handle dual-implicit toxicity effectively. The model's performance drops significantly in hard level, revealing that these LMMs still contain a significant amount of hidden but activatable toxicity. The data will be released upon the paper's acceptance.

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Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Routing networks in sparse activated mixture-of-experts dynamically allocate input tokens to top-k experts through differentiable sparse transformations, which can expand neural networks while maintaining computational efficiency. Traditional MoE networks use an expert capacity constraint to achieve GPU-friendly computing. However, when the token is routed to experts with saturated capacity, token dropping occurs, and the padding operation of underutilized experts leads to low hardware efficiency. Removing the capacity constraint can also result in low computational efficiency and decreased load balancing. We propose **Maximum Score Routing (MaxScore)** in this work, a new MoE routing paradigm developed by modeling MoE routing as the minimum cost maximum flow problem and integrating a Soft-Topk operator. MaxScore retains the expert capacity constraint, and under the same FLOPs, compared to the constrained and unconstrained baselines, MaxScore exhibits lower training losses and higher evaluation scores.

Maximum Score Routing For Mixture-of-Experts

Pre-trained language models have shown remarkable performances in recent years, setting a new paradigm for natural language processing (NLP) research. The legal domain has received some attention from the NLP community partly due to its textual nature. Question-answering (QA) systems represent some of the tasks in this domain. This work explores the legal Multiple-Choice QA (MCQA) for a low-resource language, Romanian. The contribution of this work is multi-fold. We introduce JuRO, the first openly available Romanian legal MCQA dataset, comprising 10,836 questions from three examinations. Along with this dataset, we introduce CROL, an organized corpus of laws with a total of 93 distinct documents with their modifications over 763 time spans, which we used for information retrieval techniques in this work. Additionally, we construct Law-RoG, the first graph of legal knowledge for the Romanian language derived from the corpus mentioned above. Lastly, we propose a novel approach for MCQA, namely Graph Retrieval Augmented by Facts (GRAF), which achieves competitive results with generally accepted state-of-the-art methods and even exceeds them in most settings.

GRAF: Graph Retrieval Augmented by Facts for Romanian Legal Multi-Choice Question Answering

Bridging the gap between visual and language remains a pivotal challenge for multimodal community. Traditional VQA benchmarks encounter modality gap and over-reliance on language priors, whereas human cognition excels at intuitive semiosis, associating abstract visual symbols to linguistic semantics. Inspired by this neurocognitive mechanism, we focus on emojis, the visual cipher conveying abstract textual semantics. Specifically, we propose a novel task of generating abstract linguistics from emoji sequence images, where such reasoning underpins critical application in cryptography, thus challenging MLLMs' reasoning of decoding complex semantics of visual ciphers. We introduce eWe-bench (Express What you SeE), assessing MLLMs' capability of intuitive semiosis like human. Our data construction framework ensures high visual sensitivity and data quality, which can be extended to future data enhancement. Evaluation results on advanced MLLMs highlight critical deficiencies in visual intuitive symbolic reasoning. We believe our interesting insights for advancing visual semiosis in MLLMs will pave the way for cryptographic analysis and high-level intuitive cognition intelligence of MLLMs.

Express 💬 What You See 👀: Can Multimodal LLMs Decode Visual Ciphers with Intuitive Semiosis Comprehension?

Biomedical reasoning often requires traversing interconnected relationships across entities such as drugs, diseases, and proteins. Despite the increasing prominence of large language models (LLMs), existing benchmarks lack the ability to evaluate multi-hop reasoning in the biomedical domain, particularly for queries involving one-to-many and many-to-many relationships. This gap leaves the critical challenges of biomedical multi-hop reasoning underexplored. To address this, we introduce BioHopR, a novel benchmark designed to evaluate multi-hop, multi-answer reasoning in structured biomedical knowledge graphs. Built from the comprehensive PrimeKG, BioHopR includes 1-hop and 2-hop reasoning tasks that reflect real-world biomedical complexities.

Evaluations of state-of-the-art models reveal that O3-mini, a proprietary reasoning-focused model, achieves 37.93% precision on 1-hop tasks and 14.57% on 2-hop tasks, outperforming proprietary models such as GPT4O and open-source biomedical models including HuatuoGPT-o1-70B and Llama-3.3-70B. However, all models exhibit significant declines in multi-hop performance, underscoring the challenges of resolving implicit reasoning steps in the biomedical domain. By addressing the lack of benchmarks for multi-hop reasoning in biomedical domain, BioHopR sets a new standard for evaluating reasoning capabilities and highlights critical gaps between proprietary and open-source models while paving the way for future advancements in biomedical LLMs. BioHopR is available at https://huggingface.co/datasets/knowlab-research/BioHopR.

BioHopR: A Benchmark for Multi-Hop, Multi-Answer Reasoning in Biomedical Domain

A core part of legal work which has been under-explored in Legal NLP is the writing and editing of legal briefs. Writing and editing briefs not only requires a thorough understanding of the law of a jurisdiction from judgments to statutes, but also the ability to make new arguments to try to expand the law in a new direction and make novel and creative arguments that are persuasive to judges. To capture and evaluate these legal skills in language models, we introduce BriefMe, a new dataset focused on legal briefs. It contains three tasks for language models to assist legal professionals in writing briefs: argument summarization, argument completion, and case retrieval. In this work we describe the creation of these tasks, analyze these tasks, and show how current models perform. We see that current-generation large language models (LLMs) are already quite good at the summarization and completion tasks, even beating human-generated headings. We hope this dataset encourages more development in Legal NLP in ways that will specifically aid people in performing legal work.

BriefMe: A Legal NLP Benchmark for Assisting with Legal Briefs

Large language models (LLMs) are increasingly becoming valuable to corporate data management due to their ability to process text from various document formats and facilitate user interactions through natural language queries. However, LLMs must consider the sensitivity of information when communicating with employees, especially given access restrictions. Simple filtering based on user clearance levels can pose both performance and privacy challenges. To address this, we propose the concept of sensitivity awareness (SA), which enables LLMs to adhere to predefined access rights rules. In addition, we developed a benchmarking environment called ACCESS DENIED INC to evaluate SA. Our experimental findings reveal significant variations in model behavior, particularly in managing unauthorized data requests while effectively addressing legitimate queries. This work establishes a foundation for benchmarking sensitivity-aware language models and provides insights to enhance privacy-centric AI systems in corporate environments.

ACCESS DENIED INC: The First Benchmark Environment for Sensitivity Awareness

Scaling laws are a critical component of the LLM development pipeline, most famously as a way to forecast training decisions such as 'compute-optimally' trading-off parameter count and dataset size, alongside a more recent growing list of other crucial decisions. In this work, we ask whether compute-optimal scaling behaviour can be skill-dependent. In particular, we examine knowledge and reasoning-based skills such as knowledge-based QA and code generation, and we answer this question in the affirmative: scaling laws are skill-dependent. Next, to understand whether skill-dependent scaling is an artefact of the pretraining datamix, we conduct an extensive ablation of different datamixes and find that, also when correcting for datamix differences, knowledge and code exhibit fundamental differences in scaling behaviour. We conclude with an analysis of how our findings relate to standard compute-optimal scaling using a validation set, and find that a misspecified validation set can impact compute-optimal parameter count by nearly 50%, depending on its skill composition.

Compute Optimal Scaling of Skills: Knowledge vs Reasoning

Long-document QA presents challenges with large-scale text and long-distance dependencies. Recent advances in Large Language Models (LLMs) enable entire documents to be processed in a single pass. However, their computational cost is significantly high. Retrieval-Augmented Generation (RAG) methods split text into smaller chunks, but they often yield inferior results and may lose global context. Recent approaches that integrate LLMs into RAG via iterative summarization either underutilize LLM capabilities or still incur high computational costs. In this paper, we combine the high accuracy of LLMs with the efficiency of RAG and propose LLM-Guided Dynamic Progress Control with Attention-Based Hierarchical Weighted Graph (PECAN). Our method introduces two key improvements: (1) LLM-Guided Dynamic Progress Control: We leverage LLMs to dynamically control the retrieval process, adjusting the amount of retrieved information based on different queries to achieve a better balance of effectiveness and efficiency. (2) Attention-Guided Retrieval: We propose a novel retrieval method that constructs a hierarchical graph where edges are derived by LLM attention weights. Experimental results demonstrate that PECAN achieves LLM-level performance while maintaining computational complexity comparable to that of RAG methods on two single-document and two multi-document QA datasets.

PECAN: LLM-Guided Dynamic Progress Control with Attention-Guided Hierarchical Weighted Graph for Long-Document QA

Detecting AI-generated text is a difficult problem to begin with; detecting AI-generated text on social media is made even more difficult due to the short text length and informal, idiosyncratic language of the internet. It is nonetheless important to tackle this problem, as social media represents a significant attack vector in online influence campaigns, which may be bolstered through the use of mass-produced AI-generated posts supporting (or opposing) particular policies, decisions, or events. We approach this problem with the mindset and resources of a reasonably sophisticated threat actor, and create a dataset of 505,159 AI-generated social media posts from a combination of open-source, closed-source, and fine-tuned LLMs, covering 11 different controversial topics. We show that while the posts can be detected under typical research assumptions about knowledge of and access to the generating models, under the more realistic assumption that an attacker will not release their fine-tuned model to the public, detectability drops dramatically. This result is confirmed with a human study. Ablation experiments highlight the vulnerability of various detection algorithms to fine-tuned LLMs. This result has implications across all detection domains, since fine-tuning is a generally applicable and realistic LLM use case.

When Detection Fails: The Power of Fine-Tuned Models to Generate Human-Like Social Media Text

With the growing prevalence of large language models (LLMs), the safety of LLMs has raised significant concerns. However, evaluating their safety is still lack of definitive standards due to the subjective nature of current safety benchmarks. To address this gap, we conducted the first exploration of LLMs safety evaluation from a legal perspective by proposing the SafeLawBench benchmark. The SafeLawBench categorizes safety risks into three levels based on legal standards, providing a systematic and comprehensive framework for evaluation. It comprises 24,860 multi-choice questions and 1,107 open-domain question-answering (QA) tasks. Our evaluation encompassed 17 LLMs using both zero-shot and few-shot prompting, highlighting the advantages of closed-source models over open-source ones. We also evaluated the safety-related reasoning stability of the LLMs and their refusal behavior. Additionally, we found that a majority voting mechanism can enhance model performance. Notably, even leading SOTA models like GPT-4o and Claude-3.5-Sonnet have not exceeded 80.3% performance, while the average performance of 17 LLMs remains at 67% on SafeLawBench. We urge the community to prioritize research on the safety of LLMs.

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Maximum Score Routing For Mixture-of-Experts

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