Austria

This study evaluates Direct Preference Optimization (DPO) and its variants for aligning Large Language Models (LLMs) with human preferences, testing three configurations: (1) with Supervised Fine-Tuning (SFT), (2) without SFT, and (3) without SFT but using an instruction-tuned model. We further investigate how training set size influences model performance. Our evaluation spans 13 benchmarks—covering dialogue, reasoning, mathematical problem-solving, question answering, truthfulness, MT-Bench, Big Bench, and the Open LLM Leaderboard. We find that: (1) alignment methods often achieve near-optimal performance even with smaller subsets of training data; (2) although they offer limited improvements on complex reasoning tasks, they enhance mathematical problem-solving; and (3) using an instruction-tuned model improves truthfulness. These insights highlight the conditions under which alignment methods excel, as well as their limitations.

ACL 2025

Insights into Alignment: Evaluating DPO and its Variants Across Multiple Tasks

preference optimization

post-training

reinforcement learning

### 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)

workshop paper

As the use of Large Language Models (LLMs) expands, so do concerns about their vulnerability to jailbreak attacks. We introduce SequentialBreak, a novel single-query jailbreak technique that arranges multiple benign prompts in sequence with a hidden malicious instruction among them to bypass safety mechanisms. Sequential prompt chains in a single query can lead LLMs to focus on certain prompts while ignoring others. By embedding a malicious prompt within a prompt chain, we show that LLMs tend to ignore the harmful context and respond to all prompts including the harmful one. We demonstrate the effectiveness of our attack across diverse scenarios—including Q\&A systems, dialogue completion tasks, and levelwise gaming scenario—highlighting its adaptability to varied prompt structures. The variability of prompt structures shows that SequentialBreak is adaptable to formats beyond those discussed here. Experiments show that SequentialBreak only uses a single query to significantly outperform existing baselines on both open-source and closed-source models. These findings underline the urgent need for more robust defenses against prompt-based attacks. The Results and website are available on https://anonymous.4open.science/r/JailBreakAttack-4F3B/.

SequentialBreak: Large Language Models Can be Fooled by Embedding Jailbreak Prompts into Sequential Prompt Chains

As large language models (LLMs) are increasingly deployed across diverse linguistic and cultural contexts, understanding their behavior in both factual and disputable scenarios is essential—especially when their outputs may shape public opinion or reinforce dominant narratives. This study investigates two types of bias in LLMs: model bias (bias stemming from model training) and inference bias (bias induced by the language of the query), through a two-phase evaluation. 
The first phase evaluates LLMs on factual questions where a single verifiable answer exists, assessing whether models maintain consistency across different query languages. The second phase expands the scope by probing geopolitically sensitive disputes, where responses may reflect culturally embedded or ideologically aligned perspectives. We construct a manually curated dataset spanning both factual and disputable QA, across four languages and question types. Our findings show that while Phase 1 reveals primarily language-induced alignment, Phase 2 reveals entangled effects of both language and model training. This study offers a structured framework for evaluating LLM behavior across neutral and sensitive topics, providing insights for future LLM deployment and culturally-aware evaluation practices in multilingual contexts.

A Dual-Layered Evaluation of Geopolitical and Cultural Bias in LLMs

Search engines increasingly use large language models (LLMs) to generate direct answers, while AI chatbots retrieve updated information from the
Internet. As information curators for billions of users, LLMs must evaluate the accuracy and reliability of sources. This study audits nine LLMs
from OpenAI, Google, and Meta to assess their ability to evaluate the credibility and quality of the top 20 most popular Bangladeshi news outlets.
While LLMs rate most tested outlets, larger models more often refuse to rate sources due to insufficient information, while smaller models are
prone to hallucinations. When ratings are provided, LLMs show strong internal consistency with an average correlation coefficient (𝜌) of 0.72, but
their alignment with human expert evaluations is moderate, with an average 𝜌 of 0.45. We introduce a dataset of expert opinions (journalism and
media studies students) on the credibility and political bias of Bangladeshi news outlets to evaluate LLMs’ political bias and credibility assessments.
Our analysis reveals that LLMs in default configurations favor the Bangladesh Awami League-affiliated sources in credibility ratings. Assigning
partisan identities to LLMs further amplifies politically congruent biases in their assessments. These findings highlight the need to address political
bias and improve credibility evaluations as LLMs increasingly shape how news and political information are curated worldwide.

Evaluating Credibility and Political Bias in LLMs for News Outlets in Bangladesh

Chinese idioms (成语, Chengyu) are concise four-character expressions steeped in history and culture, whose literal translations often fail to capture their full meaning. This complexity makes them challenging for language models to interpret and use correctly. Existing benchmarks focus on narrow tasks—multiple-choice cloze tests, isolated translation, or simple paraphrasing. We introduce CHENGYU-BENCH, a comprehensive benchmark featuring three tasks: (1) Evaluative Connotation, classifying idioms as positive or negative; (2) Appropriateness, detecting incorrect idiom usage in context; and (3) Open Cloze, filling blanks in longer passages without options. CHENGYU-BENCH comprises 2,937 human-verified examples covering 1,765 common idioms sourced from diverse corpora. We evaluate leading LLMs and find they achieve over 95\% accuracy on Evaluative Connotation, but only ~85% on Appropriateness and ~40% top-1 accuracy in Open Cloze. Error analysis reveals that most mistakes arise from fundamental misunderstandings of idiom meanings. CHENGYU-BENCH demonstrates that while LLMs can reliably gauge idiom sentiment, they still struggle to grasp the cultural and contextual nuances essential for proper usage. The benchmark and code will be released upon paper acceptance.

CHENGYU-BENCH: Benchmarking Large Language Models for Chinese Idiom Understanding and Use

While large language models (LLMs) have demonstrated impressive capabilities across diverse tasks, their ability to generate valid graph structures remains underexplored. We evaluate fifteen state-of-the-art LLMs on five specialized graph generation tasks spanning delivery networks, social networks, quantum circuits, gene-disease networks, and transportation systems. We also test the LLMs using 3 different prompt types: direct, iterative feedback, and program-augmented. Models supported with explicit reasoning modules (o3-mini-high, o1, Claude 3.7 Sonnet, DeepSeek-R1) solve more than twice as many tasks as their general-purpose peers, independent of parameter count. Error forensics reveals two recurring failure modes: Llama-family models often violate basic structural constraints, whereas Claude models respect topology but mismanage higher-order logical rules. Allowing models to iteratively refine their answers yields uneven gains, underscoring fundamental differences in error-correction capacity. This work demonstrates that graph competence stems from specialized architectural design rather than scale, establishing a framework for developing truly graph-savvy language models. Results and verification scripts available at https://github.com/anonymized-for-the-blind-review.

Are LLMs Truly Graph-Savvy? A Comprehensive Evaluation of Graph Generation

This paper quantifies the "embodiment gap" between disembodied language models and embodied agricultural knowledge communication through mixed-methods analysis with 78 farmers. Our key contributions include: (1) the Embodied Knowledge Representation Framework (EKRF), a novel computational architecture with specialized lexical mapping that incorporates embodied linguistic patterns from five identified domains of agricultural expertise; (2) the Embodied Prompt Engineering Protocol (EPEP), which reduced the embodiment gap by 47.3\% through systematic linguistic scaffolding techniques; and (3) the Embodied Knowledge Representation Index (EKRI), a new metric for evaluating embodied knowledge representation in language models. Implementation results show substantial improvements across agricultural domains, with particularly strong gains in tool usage discourse (58.7\%) and soil assessment terminology (67\% reduction in embodiment gap). This research advances both theoretical understanding of embodied cognition in AI and practical methodologies to enhance LLM performance in domains requiring embodied expertise.

Bridging the Embodiment Gap in Agricultural Knowledge Representation for Language Models

Effective communication of health information requires adapting complexity to match the target audience's literacy level. However, manually simplifying medical content is both time-consuming and difficult to scale. To address this challenge, we developed a new framework for automatically generating health answers at multiple complexity levels.

We began by collecting 166 linguistic features to quantify text complexity, including traditional readability metrics (e.g., Flesch-Kincaid, SMOG), medical terminology usage (e.g., UMLS coverage, medical entity recognition), syntactic complexity, semantic coherence, and LLM-based measures (e.g., masked language modeling, LLM-as-a-judge). Applying these features to a custom dataset of parallel health texts and external medical benchmarks, we used feature selection to identify 13 key metrics that reliably distinguish between simple and complex text pairs. From these, we derived a complexity scoring formula by combining the metrics with weights learned from a logistic regression model.

Using this formula, we created a large multi-level dataset of health question-answer pairs, ranging from elementary patient-friendly explanations to advanced technical summaries.
The initial QA pairs came from established datasets including LiveQA, MedicationQA, and MEDIQA-AnS. We then used LLaMA-based language models with carefully engineered prompts to transform the original answers into five different versions ordered by complexity.
Finally, we fine-tuned a large language model on this dataset, incorporating special tokens to control the complexity of the generated text. The resulting model can generate health answers at fine-grained complexity levels, allowing users to select the desired level of detail and technicality.

A Framework for Fine-Grained Complexity Control in Health Answer Generation

This paper represents an implementation of an approach rather similar to that of (Zhu et al., 2024), but for the Russian-language data. We introduce a RusConText Benchmark for evaluating short context understanding in Russian, comprising four distinct yet interrelated tasks: ellipsis resolution, coreference resolution, and idiom interpretation, and discourse understanding. Each task targets a specific aspect of linguistic processing, challenging large language model to recover omitted information, resolve referential dependencies, interpret idioms and discourse. RusConText Benchmark serves as an additional resource beyond standard benchmarks, designed to assess model performance from a specific perspective. In addition, we present the results of scoring 4 models on our benchmark.

RusConText Benchmark: A Russian Language Evaluation Benchmark for Understanding Context

Neural topic modeling is a scalable automated technique for text data mining. In various downstream tasks of topic modeling, it is prefered that the discovered topics well align with labels. However, due to the lack of guidance from labels, unsupervised neural topic models are less powerful in this situation. Existing supervised neural topic models often adopt a label-free prior to generate the latent document-topic distributions and use them to predict the labels and thus achieve label-topic alignment indirectly. Such a mechanism faces the following issues: 1) The label-free prior leads to topics blending the latent patterns of multiple labels; 2) One is unable to intuitively identify the explicit relationships between labels and the discovered topics. To tackle these problems, we develop a novel supervised neural topic model which utilizes a chain-structured graphical model that emphasizes direct constraints of labels on topics. Soft indicators are introduced to explicitly construct the label-topic relationships. To obtain well-organized label-topic relationships, we formalize an entropy-regularized optimal transport problem on the embedding space and model them as the transport plan. Moreover, our proposed method can be seamlessly integrated with most existing unsupervised neural topic models in a flexible manner. Experiment results on multiple datasets demonstrate that our model can greatly enhance the alignment between labels and topics while maintaining good topic quality.

Supervised Neural Topic Modeling with Label Alignment

Amidst the rapid advancement of artificial intelligence, research on large vision-language models (LVLMs) has emerged as a pivotal area. However, understanding their internal mechanisms remains challenging due to the limitations of existing interpretability methods, especially regarding faithfulness and plausibility. To address this, we first construct a human response interpretability dataset that evaluates the plausibility of model explanations by comparing the attention regions between the model and humans when answering the same questions. We then propose a patchwise cooperative game-based interpretability method for LVLMs, which employs Shapley values to quantify the impact of individual image patches on generation likelihood and enhances computational efficiency through a single input approximation approach. Experimental results demonstrate our method's faithfulness, plausibility, and robustness. Our method provides researchers with deeper insights into model behavior, allowing for an examination of the specific image regions each layer relies on during response generation, ultimately enhancing model reliability. Our codes are available at https://github.com/ZY123-GOOD/Patchwise_Cooperative.

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