Austria

The rapid rise of Large Language Models (LLMs) has encouraged the development of detectors for machine-generated text, but their high benchmark scores often do not hold up in real-world scenarios, making these detectors unreliable. This paper reviews datasets with AI-generated content and proposes methods to evaluate their quality and adequacy to train robust detectors capable to distinguish human-written and machine-generated texts.

ACL 2025

Are AI Datasets Good Enough? A Survey on Quality of Datasets With Machine-Generated Texts

workshop paper

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.* 
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Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

We can think of Visual Question Answering as a (multimodal) conversation between a human and an AI system. Here, we explore the sensitivity of Vision Language Models (VLMs) through the lens of cooperative principles of conversation proposed by Grice. Specifically, even when Grice's maxims of conversation are flouted, humans typically do not have much difficulty in understanding the conversation even though it requires more cognitive effort. Here, we study if VLMs are capable of handling violations to Grice's maxims in a manner that is similar to humans. Specifically, we add modifiers to human-crafted questions and analyze the response of VLMs to these modifiers. We use three state-of-the-art VLMs in our study, namely, GPT-4o, Claude-3.5-Sonnet and Gemini-1.5-Flash on questions from the VQA v2.0 dataset. Our initial results seem to indicate that the performance of VLMs consistently diminish with the addition of modifiers which indicates our approach as a promising direction to understand the limitations of VLMs.

Analyzing the Sensitivity of Vision Language Models in Visual Question Answering

Large Language Models (LLMs) have demonstrated great potential as evaluators of NLG systems, allowing for high-quality, reference-free, and multi-aspect assessments. However, existing LLM-based metrics suffer from two major drawbacks: reliance on proprietary models to generate training data or perform evaluations, and a lack of fine-grained, explanatory feedback. In this paper, we introduce OpeNLGauge, a fully open-source, reference-free NLG evaluation metric that provides accurate explanations based on error spans. OpeNLGauge is available as a two-stage ensemble of larger open-weight LLMs, or as a small fine-tuned evaluation model, with confirmed generalizability to unseen tasks, domains and aspects. Our extensive meta-evaluation shows that OpeNLGauge achieves competitive correlation with human judgments, outperforming state-of-the-art models on certain tasks while maintaining full reproducibility and providing explanations more than twice as accurate.

OpeNLGauge: An Explainable Metric for NLG Evaluation with Open-Weights LLMs

Large language models (LLMs) are very costly and inefficient to update with new information. To address this limitation, retrieval-augmented generation (RAG) has been proposed as a solution that dynamically incorporates external knowledge during inference, improving factual consistency and reducing hallucinations. Despite its promise, RAG systems face practical challenges-most notably, a strong dependence on the quality of the input query for accurate retrieval. In this paper, we investigate the sensitivity of different components in the RAG pipeline to various types of query perturbations. Our analysis reveals that the performance of commonly used retrievers can degrade significantly even under minor query variations. We study each module in isolation as well as their combined effect in an end-to-end question answering setting, using both general-domain and domain-specific datasets. Additionally, we propose an evaluation framework to systematically assess the query-level robustness of RAG pipelines and offer actionable recommendations for practitioners based on the results of more than 1092 experiments we performed.

Investigating the Robustness of Retrieval-Augmented Generation at the Query Level

Table-to-text generation is a challenging subtask of data-to-text generation, where a natural language generation (NLG) system is generating insights from a given data table. Recent research has built on neural language models (LMs), including large language models (LLMs). However, LLMs were shown to memorize common benchmarks, inflating their true performance. Following prior work on dynamic dataset construction, we developed an approach for obtaining up-to-date benchmarks for table-to-text generation, dubbed FreshTab. This dataset family, based on Wikipedia tables, is not affected by the problems of LLM memorization and benchmark contamination, as the underlying tables are newer than the LLM's knowledge cutoff date. We also introduce basic domain labels for each table, allowing for domain-specific evaluation insights. In our experiments with tables from February-March 2025 collected using FreshTab, we show that all recent LLMs perform worse on average than on a comparable set of tables from the earlier LoTNLG/LogicNLG benchmark.

Sourcing Fresh Resources for Table-to-Text Generation Evaluation

This paper presents a comprehensive evaluation framework for aligning Persian Large Language Models (LLMs) with critical ethical dimensions, including safety, fairness, and social norms. It addresses the gaps in existing LLM evaluation frameworks by adapting them to Persian linguistic and cultural contexts. This benchmark creates three types of Persian-language benchmarks: (i) translated data, (ii) new data generated synthetically, and (iii) new naturally collected data. We translate Anthropic Red Teaming data, AdvBench, HarmBench, and DecodingTrust into Persian. Furthermore, we create ProhibiBench-fa, SafeBench-fa, FairBench-fa, and SocialBench-fa as new datasets to address harmful and prohibited content in indigenous culture. Moreover, we collect extensive dataset as GuardBench-fa to consider Persian cultural norms. By combining these datasets, our work establishes a unified framework for evaluating Persian LLMs, offering a new approach to culturally grounded alignment evaluation. A systematic evaluation of Persian LLMs is performed across the three alignment aspects: safety (avoiding harmful content), fairness (mitigating biases), and social norms (adhering to culturally accepted behaviors). We present a publicly available leaderboard that benchmarks Persian LLMs with respect to safety, fairness, and social norms.

ELAB: Extensive LLM Alignment Benchmark in Persian Language

The reliance on translated or adapted datasets from English or multilingual resources introduces challenges regarding linguistic and cultural suitability. This study addresses the need for robust and culturally appropriate benchmarks by evaluating the quality of 17 commonly used Turkish benchmark datasets. Using a comprehensive framework that assesses six criteria, both human and LLM-judge annotators provide detailed evaluations to identify dataset strengths and shortcomings.

Our results reveal that 70% of the benchmark datasets fail to meet our heuristic quality standards. The correctness of the usage of technical terms is the strongest criterion, but 85% of the criteria are not satisfied in the examined datasets. Although LLM judges demonstrate potential, they are less effective than human annotators, particularly in understanding cultural common sense knowledge and interpreting fluent, unambiguous text. GPT-4o has stronger labeling capabilities for grammatical and technical tasks, while Llama3.3-70B excels at correctness and cultural knowledge evaluation. Our findings emphasize the urgent need for more rigorous quality control in creating and adapting datasets for low-resource languages.

Evaluating the Quality of Benchmark Datasets for Low-Resource Languages: A Case Study on Turkish

Toxic language in digital environments presents significant challenges with implications for user well-being and the structure of online discourse. The spread of harmful text has been linked to consequences such as effects on mental health, the reinforcement of social biases, and the exclusion of specific groups. These concerns have led to the development of systems to detect and reduce toxicity across different contexts. Despite progress in toxic content detection, most publicly available datasets rely on binary annotations (e.g., “toxic” vs. “non-toxic”). This simplification limits the representation of linguistic and social attributes, such as the targeted identity group, the tone or intensity of the message, and the subtype of harmful expression. Consequently, models trained on such datasets often struggle to generalize to more implicit manifestations of toxicity, including covert hate speech and indirect discriminatory language. In this work, we investigate classifier-guided and prompt-based strategies for generating synthetic toxicity datasets. We compare their effects on linguistic variation, demographic coverage, and generalization across benchmarks, focusing on existing frameworks such as ToxiGen and ToxEvol.

Prompt-Based Evolution for Diverse and Generalizable Toxic Language Datasets

Evaluation of intermediate language model checkpoints during training is critical for effective model development and selection. However, reliable evaluation using the popular multiple-choice question (MCQ) format is challenging, as small and non-instruction-tuned models often lack the symbolic reasoning required for the task, despite the fact that MCQ evaluation is often used and needed to distinguish between the performance of different training runs. In particular, when prompted with a question and a set of labeled answer choices (e.g., "A. …, B. …, C. …''), many models struggle to emit the correct label (e.g., "C''), even when they can select the correct string answer choice. We propose an alternative evaluation method: fine-tuning the model on an auxiliary MCQ dataset prior to outputting labels. We validate this approach empirically and analyze its generalization across datasets and varying amounts of training data, finding that the model is indeed able to generalize the format from one dataset to another, potentially providing a better indication of the strength of intermediate model checkpoints.

Fine-Tune on the Format: First Improving Multiple-Choice Evaluation for Intermediate LLM Checkpoints

Aligning large language models (LLMs) with human preferences remains a key challenge in AI. Preference-based optimization methods, such as Reinforcement Learning with Human Feedback (RLHF) and Direct Preference Optimization (DPO), rely on human-annotated datasets to improve alignment. In this work, we identify a crucial property of the existing learning method: the distinguishing signal obtained in preferred responses is often concentrated in the early tokens. We refer to this as $\textit{shallow preference signals}$.

To explore this property, we systematically truncate preference datasets at various points and train both reward models and DPO models on the truncated data. $\textbf{Surprisingly}$, models trained on truncated datasets, retaining only the first half or fewer tokens, achieve comparable or even superior performance to those trained on full datasets. For example, a reward model trained on the Skywork-Reward-Preference-80K-v0.2 dataset outperforms the full dataset when trained on a 40% truncated dataset. This pattern is consistent across multiple datasets, suggesting the widespread presence of $\textit{shallow preference signals}$.

We further investigate the distribution of the reward signal through decoding strategies. We consider two simple decoding strategies motivated by the shallow reward signal observation, namely Length Control Decoding and KL Threshold Control Decoding, which leverage shallow preference signals to optimize the trade-off between alignment and computational efficiency. The performance is even better, which again validates our hypothesis.

The phenomenon of $\textit{shallow preference signals}$ highlights potential issues in LLM alignment: existing alignment methods often focus on aligning only the initial tokens of responses, rather than considering the full response. This could lead to discrepancies with real-world human preferences, resulting in suboptimal alignment performance.

Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data?

Human evaluation is indispensable in natural language processing (NLP), as automatic metrics are known to not always align well with human judgments.
However, the reproducibility of human evaluations can be problematic since results are susceptible to many factors, the details of which are often missing from the respective works.
As part of the ReproHum project, this work aims to reproduce the human evaluation of a single criterion in the paper "MemSum: Extractive Summarization of Long Documents Using Multi-Step Episodic Markov Decision Processes" (Gu et al, 2022).
The results of our reproduction differ noticeably from those of the original study. To explain this discrepancy, we discuss differences in the experimental setup, as well as more general characteristics of the selected domain and the generated summaries.

ReproHum #0729-04: Human Evaluation Reproduction Report for "MemSum: Extractive Summarization of Long Documents Using Multi-Step Episodic Markov Decision Processes"

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Analyzing the Sensitivity of Vision Language Models in Visual Question Answering

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