AACL 2025 论文列表

Recent advances in AI-generated video have shown strong performance on text-to-video tasks, particularly for short clips depicting a single scene. However, current models struggle to generate longer videos with coherent scene transitions, primarily because they cannot infer when a transition is needed from the prompt. Most open-source models are trained on datasets consisting of single-scene video clips, which limits their capacity to learn and respond to prompts requiring multiple scenes. Developing scene transition awareness is essential for multi-scene generation, as it allows models to identify and segment videos into distinct clips by accurately detecting transitions. To address this, we introduce the Transition-Aware Video (TAV) dataset with multi-scene clips and captions that explicitly state scene segmentation and transition structure. Our focus is on how prompt semantics and dataset annotations about temporal context affect text-to-video generation. Post-training on TAV improves alignment between the scene count implied by prompt and the scene count produced by the model, while preserving visual quality.

Large language models (LLMs) hold promise for advancing patient–provider communication, yet a persistent gap remains between benchmark-driven model development and the realities of clinical practice. This work presents a systematic, clinically grounded review of text-based medical datasets for LLM training and evaluation. We propose a scenario-based taxonomy derived from established clinical frameworks to map major knowledge-based and conversation-based corpora against core communication scenarios. We further synthesize core communication skills from gold-standard clinical assessment instruments and meta-analyze state-of-the-art medical LLM performance, highlighting how dataset properties, fine-tuning strategies, and evaluation metrics shape both knowledge acquisition and communicative competence. To empirically validate these findings, we conducted controlled fine-tuning experiments across representative LLMs, demonstrating that data composition and scenario alignment critically affect model performance. Our findings highlight the urgent need for scenario-rich datasets and standardized, human-centered evaluation protocol to advance clinically relevant medical LLMs.

Although there has been growing interest in the self-correction capability of Large Language Models (LLMs), there are varying conclusions about its effectiveness.Prior research has largely concentrated on intrinsic self-correction, extrinsic self-correction, particularly the interplay between internal knowledge and external feedback, remains underexplored. In this paper, we aim to comprehensively investigate the underlying mechanism of moral self-correction by addressing a fundamental question: is moral self-correction an innate capability of LLMs? Specifically, we conduct: (1) a behavioral analysis of LLMs’ moral sensitivity based on a self-distinguishing task; and (2) a mechanistic analysis of the hidden states to examine how key components of self-correction, such as Chain-of-Thought (CoT) and external feedback, interact to facilitate moral self-correction. Drawing on empirical evidence from both behavioral and mechanistic analyses, we demonstrate that moral self-correction is not an inherent capability of LLMs, as they are neither morally sensitive nor able to effectively incorporate external feedback during the self-correction process.

Probing the multilingual knowledge of linguistic structure in LLMs, often characterized as sequence labeling, faces challenges with maintaining output templates in current text-to-text prompting strategies. To solve this, we introduce a decomposed prompting approach for sequence labeling tasks. Diverging from the single text-to-text prompt, our prompt method generates for each token of the input sentence an individual prompt which asks for its linguistic label. We test our method on the Universal Dependencies part-of-speech tagging dataset for 38 languages, using both English-centric and multilingual LLMs. Our findings show that decomposed prompting surpasses the iterative prompting baseline in efficacy and efficiency under zero- and few-shot settings. Moreover, our analysis of multilingual performance of English-centric LLMs yields insights into the transferability of linguistic knowledge via multilingual prompting.

In this work, we address the problem of fine-grained traceback of emotional and manipulation characteristics from synthetically manipu- lated speech. We hypothesize that combining semantic–prosodic cues captured by Speech Foundation Models (SFMs) with fine-grainedspectral dynamics from auditory representations can enable more precise tracing of both emotion and manipulation source. To validate this hypothesis, we introduce MiCuNet, a novel multitask framework for fine-grained tracing of emotional and manipulation attributes in synthetically generated speech. Our approach integrates SFM embeddings with spectrogram-based auditory features through a mixed-curvature projection mechanism that spans Hyperbolic, Euclidean, and Spherical spaces guided by a learnable temporal gating mechanism. Our proposed method adopts a multitask learning setup to simultaneously predict original emotions, manipulated emotions, and manipulation sources on the Emo-Fake dataset (EFD) across both English and Chinese subsets. MiCuNet yields consistent improvements, consistently surpassing conventional fusion strategies. To the best of our knowledge, this work presents the first study to explore a curvature-adaptive framework specifically tailored for multitask tracking in synthetic speech.

Large language models (LLMs) are increasingly integrated into academic workflows, with many conferences and journals permitting their use for tasks such as language refinement and literature summarization. However, their use in peer review remains prohibited due to concerns around confidentiality breaches, hallucinated content, and inconsistent evaluations. As LLM-generated text becomes more indistinguishable from human writing, there is a growing need for reliable attribution mechanisms to preserve the integrity of the review process. In this work, we evaluate topic-based watermarking (TBW), a semantic-aware technique designed to embed detectable signals into LLM-generated text. We conduct a systematic assessment across multiple LLM configurations, including base, few-shot, and fine-tuned variants, using authentic peer review data from academic conferences. Our results show that TBW maintains review quality relative to non-watermarked outputs, while demonstrating robust detection performance under paraphrasing. These findings highlight the viability of TBW as a minimally intrusive and practical solution for LLM attribution in peer review settings.

There is growing research interest in Questions Under Discussion (QUD), a linguistic framework for representing discourse in the form of natural language question-answer pairs, which are more easily understandable and have been found useful in several applications. Our goal in this work is to improve on the quality of automatic QUD generation. As a way to sidestep the paucity of data currently, we propose a reinforcement learning-based approach using the Group Relative Policy Optimisation (GRPO) objective for LLM post-training on the task. To get there, we: (i) carefully investigated five promising methods for reference-free automatic QUD evaluation, (ii) proposed a novel prompting strategy, SCRS, involving ranking and scoring with structured outputs that enables QUD evaluation close to the human upperbound, (iii) leveraged findings from (i) with (ii) for the knowledge distillation from a very large LLM to obtain a more resource-efficient reward model, and which (iv) we then used in the GRPO post-training for 3B LLMs on the QUD generation task. Our QUD generators give overall higher-quality QUDs compared to the SOTA which is based on supervised fine-tuning; all of these are achieved using only three annotated exemplars in the few-shot prompting for evaluation, and without the use of any other annotated questions for training the QUD generators. Our code, models, and annotated examples can be found at https://github.com/hankelvin/grpo_qud_generation.

Natural Language Processing is a branch of artificial intelligence that enables man- machine interactions through regional languages. In Kannada, there are two types of Sandhi: Kannada Sandhi and Sanskrit Sandhi. A morph-phonemic word “Sandhi” is created when two words or distinct morphemes are joined or combined. Conversely, Sandhi word splitting reverses this process. Rules governing Sandhi exist across all the Dravidian languages. A rule-based method has been developed to split Sanskrit Sandhi words into their components within Kannada sentences. Once the Sanskrit Sandhi (SS) words are split, the type of Sandhi is also identified, facilitating accurate translation of the Sanskrit Sandhi words into English. This paper discusses seven types of SS words: SavarNadeergha, YaN, GuNa, Vruddhi, Jatva, Shchutva and Anunasika Sandhi. The identified split points adhere precisely to Sandhi rules. A dataset of 4900 SanskritSandhi words found in Kannada sentences was used to evaluate the proposed method, which achieved an accuracy of 90.03% for Sanskrit Sandhi Identification and 85.87% for reliable English Translation. This work has potential applications in other Dravidian languages.

Training effective text rerankers is crucial for information retrieval. Two strategies are widely used: contrastive learning (optimizing directly on ground-truth labels) and knowledge distillation (transferring knowledge from a larger reranker). While both have been studied extensively, a clear comparison of their effectiveness for training cross-encoder rerankers under practical conditions is needed.This paper empirically compares these strategies by training rerankers of different sizes (0.5B, 1.5B, 3B, 7B) and architectures (Transformer, Recurrent) using both methods on the same data, with a strong contrastive learning model acting as the distillation teacher. Our results show that knowledge distillation generally yields better in-domain and out-of-domain ranking performance than contrastive learning when distilling from a more performant teacher model. This finding is consistent across student model sizes and architectures. However, distilling from a teacher of the same capacity does not provide the same advantage, particularly for out-of-domain tasks. These findings offer practical guidance for choosing a training strategy based on available teacher models. We recommend using knowledge distillation to train smaller rerankers if a larger, more performant teacher is accessible; in its absence, contrastive learning remains a robust baseline. Our code implementation is made available to facilitate reproducbility.

Recent progress in Large Language Model (LLM) technology has changed our role in interacting with these models. Instead of primarily testing these models with questions we already know answers to, we are now using them for queries where the answers are unknown to us, driven by human curiosity. This shift highlights the growing need to understand curiosity-driven human questions – those that are more complex, open-ended, and reflective of real-world needs. To this end, we present Quriosity, a collection of 13K naturally occurring questions from three diverse sources: human-to-search-engine queries, human-to-human interactions, and human-to-LLM conversations. Our comprehensive collection enables a rich understanding of human curiosity across various domains and contexts. Our analysis reveals a significant presence of causal questions (up to 42%) in the dataset, for which we develop an iterative prompt improvement framework to identify all causal queries and examine their unique linguistic properties, cognitive complexity and source distribution. We also lay the groundwork for exploring efficient identifiers of causal questions, providing six efficient classification models.

Business process modeling has traditionally depended on manual efforts or rigid rule-based techniques, limiting scalability and flexibility. Recent progress in Large Language Models (LLMs) enables automatic generation of process models from text, yet a systematic evaluation remains lacking. This paper explores the ability of LLMs to produce structurally and semantically valid business process workflows using five approaches: zero-shot, zero-shot CoT, few-shot, few-shot CoT, and fine-tuning. We assess performance under increasing control-flow complexity (e.g., nested gateways, parallel branches) using the MaD dataset, and introduce a masked-input setting to test semantic robustness. Results show that while fine-tuning achieves the best accuracy, few-shot CoT excels in handling complex logic and incomplete inputs. These findings reveal the strengths and limits of LLMs in process modeling and offer practical guidance for enterprise Business Process Management (BPM) automation.

As an effective way to address data scarcity problem, data augmentation has received significant interest in low-resource neural machine translation, while the latter has the potential to reduce digital divide and benefit out of domain translation. However, the existing works mainly focus on how to generate the synthetic data, while the synthetic data quality and the way we use the synthetic data also matter. In this paper, we give a systematic analysis of data augmentation for low-resource neural machine translation that encompasses all the three aspects. We show that with careful control of the synthetic data quality and the way we use the synthetic data, the performance can be greatly boosted even with the same method to generate the synthetic data.

Recent studies have combined Large Language Models (LLMs) with Knowledge Graphs (KGs) to enhance reasoning, improving inference accuracy without additional training while mitigating hallucination. However, existing frameworks still suffer two practical drawbacks: they must be re-tuned whenever the KG or reasoning task changes, and they depend on a single, high-capacity LLM for reliable (i.e., trustworthy) reasoning. To address this, we introduce R2-KG, a plug-and-play, dual-agent framework that separates reasoning into two roles: an Operator (a low-capacity LLM) that gathers evidence and a Supervisor (a high-capacity LLM) that makes final judgments. This design is cost-efficient for LLM inference while still maintaining strong reasoning accuracy. Additionally, R2-KG employs an Abstention mechanism, generating answers only when sufficient evidence is collected from KG, which significantly enhances reliability. Experiments across five diverse benchmarks show that R2-KG consistently outperforms baselines in both accuracy and reliability, regardless of the inherent capability of LLMs used as the operator. Further experiments reveal that the single-agent version of R2-KG, equipped with a strict self-consistency strategy, achieves significantly higher-than-baseline reliability with reduced inference cost but increased abstention rate in complex KGs. Our findings establish R2-KG as a flexible and cost-effective solution for KG-based reasoning, reducing reliance on high-capacity LLMs while ensuring trustworthy inference.

Grammatical Error Correction (GEC) is a fundamental task in Natural Language Processing that focuses on automatically detecting and correcting grammatical errors in text. In this paper, we present a novel approach for GEC for Gujarati. Gujarati is an Indian language spoken by over 55 million people worldwide. Our approach combines a large language model with non-parametric memory modules to address the low-resource challenge. We have evaluated our system on human-annotated and synthetic datasets. The overall result indicates promising results for Gujarati. The proposed approach is generic enough to be adopted by other languages. Furthermore, we release a publicly available evaluation dataset for Gujarati GEC along with an adapted version of the ERRANT framework to enable error-type-wise evaluation in Gujarati.

Large Language Models (LLMs) have shown strong capabilities in Question Answering (QA), but their effectiveness in high-stakes, closed-domain settings is often constrained by hallucinations and limited handling of vague or underspecified queries. These challenges are especially pronounced in Vietnamese, a low-resource language with complex syntax and strong contextual dependence, where user questions are often short, informal, and ambiguous. We introduce the Unified Retrieval Agent-Based System (URASys), a QA framework that combines agent-based reasoning with dual retrieval under the Just Enough principle to address standard, ambiguous, and unanswerable questions in a unified manner. URASys performs lightweight query decomposition and integrates document retrieval with a question–answer layer via a two-phase indexing pipeline, engaging in interactive clarification when intent is uncertain and explicitly signaling unanswerable cases to avoid hallucination. We evaluate URASys on Vietnamese and English QA benchmarks spanning single-hop, multi-hop, and real-world academic advising tasks, and release new dual-language ambiguous subsets for benchmarking interactive clarification. Results show that URASys outperforms strong retrieval-based baselines in factual accuracy, improves unanswerable handling, and achieves statistically significant gains in human evaluations for clarity and trustworthiness. All code and datasets are publicly available at https://github.com/ura-hcmut/URASys.

Dominant sequence models like the Transformer represent structure implicitly through dense attention weights, incurring quadratic complexity. We propose RewriteNets, a novel neural architecture built on an alternative paradigm: explicit, parallel string rewriting. Each layer in a RewriteNet contains a set of learnable rules. For each position in an input sequence, the layer performs four operations: (1) fuzzy matching of rule patterns, (2) conflict resolution via a differentiable assignment operator to select non-overlapping rewrites, (3) application of the chosen rules to replace input segments with output segments of potentially different lengths, and (4) propagation of untouched tokens. While the discrete assignment of rules is non-differentiable, we employ a straight-through Gumbel-Sinkhorn estimator, enabling stable end-to-end training. We evaluate RewriteNets on algorithmic, compositional, and string manipulation tasks, comparing them against strong LSTM and Transformer baselines. Results show that RewriteNets excel at tasks requiring systematic generalization (achieving 98.7% accuracy on the SCAN benchmark’s length split) and are computationally more efficient than Transformers. We also provide an analysis of learned rules and an extensive ablation study, demonstrating that this architecture presents a promising direction for sequence modeling with explicit structural inductive biases.

As large language models (LLMs) continue to scale, the memory footprint of Key-Value (KV) caches during inference has become a significant bottleneck. Existing approaches primarily focus on compressing KV caches within a single prompt or reusing shared prefixes or frequently occurred text segments across prompts. However, such strategies are limited in scenarios where prompts are semantically similar but lexically different, which frequently occurs in tasks such as multi-document summarization and conversational agents. We propose SemShareKV, a KV cache sharing and compression framework that accelerates LLM inference by reusing KV cache in semantically similar prompts. Instead of relying on exact token matches, SemShareKV applies fuzzy token matching using Locality-Sensitive Hashing (LSH) on token embeddings and incorporates Rotary Position Embedding (RoPE) to better preserve positional information. By selectively reusing relevant KV pairs from a reference prompt’s cache, SemShareKV reduces redundant computation while maintaining output quality. Experiments on diverse summarization datasets show up to 6.25\times speedup and 42% lower GPU memory usage with 5k tokens input, with negligible quality degradation. These results highlight the potential of semantic-aware cache sharing for efficient LLM inference.

Transformer models often exhibit brittle extrapolation, failing on inputs that are longer or structurally more complex than those seen during training. We introduce Counter-Example–Driven Curricula (CEDC), an automated framework that improves model robustness by iteratively focusing on its own failures. At each step, CEDC uses the current model to generate a diverse set of candidate problems, employs a fast, executable verifier to identify incorrect predictions (counter‐examples), and then fine‐tunes the model on a dataset enriched with these discovered failures. We evaluate CEDC on a suite of algorithmic and natural language tasks, including integer addition, sorting, Dyck‐2 language recognition, and three text classification benchmarks. Compared to static training and standard curriculum learning baselines, CEDC achieves up to 30× greater length extrapolation, is 3.75× more computationally efficient than uniform data augmentation, and requires no manual difficulty heuristics. We provide a detailed analysis of the counter‐examples, showing how the curriculum naturally adapts to target progressively more complex error modes. Our findings establish verifier‐guided, failure‐driven learning as a simple, powerful, and efficient paradigm for enhancing the generalization capabilities of Transformer models.

Large language models can be quantized to reduce inference time latency, model size, and energy consumption, thereby delivering a better user experience at lower cost. A challenge exists to deliver quantized models with minimal loss of accuracy in reasonable time, and in particular to do so without requiring mechanisms incompatible with specialized inference accelerators. Here, we demonstrate a simple, end-to-end quantization-aware training approach that, with an increase in total model training budget of less than 0.1%, outperforms the leading published quantization methods by large margins on several modern benchmarks, with both base and instruct model variants. The approach easily generalizes across different model architectures, can be applied to activations, cache, and weights, and requires the introduction of no additional operations to the model other than the quantization itself.