EACL 2026 Papers

Large Vision-Language Models (LVLMs), trained by aligning visual encoders to LLMs on extensive vision-language data, demonstrate impressive performance across a broad variety of tasks that require understanding of both visual and textual inputs. Acknowledging this, recent work proposed to post-hoc convert generative LVLMs into vision-language encoders (VLEs) via supervised contrastive learning objectives. This type of training enables LVLMs to produce better representations, i.e., embeddings for image and text input, used in retrieval and (semantic) similarity tasks. Having observed that this type of VLEs (i.e., LVLMs turned into encoders) commonly employ last-token pooling in downstream tasks, without using special sequence-end tokens, in this focused contribution, we study the effect of pooling strategies on VLEs’ downstream performance. We empirically show that, in contrast to mean pooling, last-token pooling (without special sequence-end tokens) makes VLEs highly sensitive to end-of-input artifacts in fine-tuning and inference data, e.g., whether input sequences end with punctuation or newline characters. Finally, we show that introducing the special end-of-sequence token removes this sensitivity and makes VLEs robust to formatting artifacts of input text.

Eliciting cooperation in multi-agent LLM systems is critical for AI alignment. We investigate two approaches: direct communication and curriculum learning. In a 4-player Stag Hunt, a one-word “cheap talk” channel increases cooperation from 0% to 48.3%, demonstrating communication as a robust coordination mechanism. In contrast, we find that curriculum learning is highly sensitive to design choices: our pedagogical curriculum through progressively complex games reduced agent payoffs by 27.4% in an Iterated Public Goods Game with Punishment. Qualitative analysis reveals that curricula emphasizing defection-equilibrium games can induce “learned pessimism” in agents. These findings suggest that for coordination problems, simple communication protocols may be more reliable than experience-based training, and that curriculum design for social dilemmas requires careful attention to the strategic lessons embedded in game sequences.

Tokenization plays a crucial role in the performance of language models. However, most existing tokenizers rely on frequency-based segmentation, which fails to capture the morphological structure of languages and often leads to inefficient token representations. In this study, we propose a novel tokenization method that emphasizes the importance of Korean morphological structures in eojeol (Korean spacing unit). This method is designed to accommodate both inter-eojeol segmentation and intra-eojeol segmentation, enabling the selection of subwords based on morphemes. We pretrained a language model using the proposed method and evaluated its performance on Korean benchmark tasks. Experimental results demonstrate that the proposed method generally outperforms existing approaches. Notably, it produces significantly fewer tokens per input sequence, indicating its effectiveness and efficiency for Korean language modeling. The code is available at https://github.com/Dohy-Lee/mob.

This paper presents an evaluation framework for probing large language models’ linguistic knowledge of Indigenous languages of the Americas using zero- and few-shot prompting. The framework consists of three tasks: (1) language identification, (2) cloze completion of Spanish sentences supported by Indigenous-language translations, and (3) grammatical feature classification. We evaluate models from five major families (GPT, Gemini, DeepSeek, Qwen, and LLaMA) on 13 Indigenous languages, including Bribri, Guarani, and Nahuatl. The results show substantial variation across both languages and model families. While a small number of model-language combinations demonstrate consistently stronger performance across tasks, many others perform near chance, highlighting persistent gaps in current models’ abilities on Indigenous languages.

Large Language Models (LLMs) are effective for data augmentation in classification tasks like intent detection. In some cases, they inadvertently produce examples that are ambiguous with regard to untargeted classes. We present DDAIR (Disambiguated Data Augmentation for Intent Recognition) to mitigate this problem. We use Sentence Transformers to detect ambiguous class-guided augmented examples generated by LLMs for intent recognition in low-resource scenarios. We identify synthetic examples that are semantically more similar to another intent than to their target one. We also provide an iterative re-generation method to mitigate such ambiguities. Our findings show that sentence embeddings effectively help to (re)generate less ambiguous examples, and suggest promising potential to improve classification performance in scenarios where intents are loosely or broadly defined.

As grammar books are increasingly used as additional reference resources specifically for very low-resource languages, a significant portion comes from scans and relies on the quality of the Optical Character Recognition (OCR) tool. We focus here on a particular script used in linguistics to transcribe sounds: the International Phonetic Alphabet (IPA). We consider two data sources: actual grammar book PDFs for two languages under documentation, Japhug and Kagayanen, and a synthetically generated dataset based on Wiktionary. We compare two neural OCR frameworks, Tesseract and Calamari, and a recent large vision-language model, Qwen2.5-VL-7B, all three in an off-the-shelf setting and with fine-tuning. While their zero-shot performance is relatively poor for IPA characters in general due to character set mismatch, fine-tuning with the synthetic dataset leads to notable improvements.

Large audio-language models (LALMs) benefit from Chain-of-Thought (CoT) prompting for audio question answering (AQA), but acquiring audio CoT examples is particularly challenging as it requires sequential listening and careful integration of acoustic and linguistic information. Surprisingly, our experiments reveal that standard few-shot prompting yields inconsistent results compared to zero-shot CoT, with several models showing degraded accuracy. Moreover, few-shot prompting incurs substantially higher inference costs by processing multiple audio demonstrations per inference. We propose Audio-Induct, which induces reusable textual task instructions from few audio examples once per task, requiring no additional demonstrations at inference. Evaluated on 9 LALMs across two benchmarks, Audio-Induct outperforms state-of-the-art prompting methods while maintaining low inference costs. Inducted Task Instructions transfer effectively across models, enabling scalable deployment.

We introduce the Korean Canonical Legal Benchmark (KCL), a benchmark designed to assess language models’ legal reasoning capabilities independently of domain-specific knowledge. KCL provides question-level supporting precedents, enabling a more faithful disentanglement of reasoning ability from parameterized knowledge. KCL consists of two components: (1) KCL-MCQA, multiple-choice problems of 283 questions with 1,103 aligned precedents, and (2) KCL-Essay, open-ended generation problems of 169 questions with 550 aligned precedents and 2,739 instance-level rubrics for automated evaluation. Our systematic evaluation of 30+ models shows large remaining gaps, particularly in KCL-Essay, and that reasoning-specialized models consistently outperform their general-purpose counterparts. We release all resources, including the benchmark dataset and evaluation code, at https://github.com/lbox-kr/kcl.

We describe and compare multiple approaches for using voice conversion techniques to mask speaker identities in low-resource text-to-speech. We build and evaluate speaker-anonymized text-to-speech systems for two Canadian Indigenous languages, nêhiyawêwin and SENĆOŦEN, and show that cross-lingual speaker transfer via multilingual training with English data produces the most consistent results across both languages. Our research also underscores the need for better evaluation metrics tailored to cross-lingual voice conversion. Our code can be found at https://github.com/EveryVoiceTTS/Speaker_Anonymization_StyleTTS2

Evaluating the naturalness of dialogue in language models (LMs) is not trivial: notions of naturalness vary, and scalable quantitative metrics remain limited. This study leverages the linguistic notion of at-issueness to assess dialogue naturalness and introduces a new method: Divide, Generate, Recombine, and Compare (DGRC). DGRC (i) divides a dialogue as a prompt, (ii) generates continuations for subparts using LMs, (iii) recombines the dialogue and continuations, and (iv) compares the likelihoods of the recombined sequences. This approach mitigates bias in linguistic analyses of LMs and enables systematic testing of discourse-sensitive behavior. Applying DGRC, we find that LMs prefer to continue dialogue on at-issue content, with this effect enhanced in instruct-tuned models. They also reduce their at-issue preference when relevant cues (e.g., “Hey, wait a minute”) are present. Although instruct-tuning does not further amplify this modulation, the pattern reflects a hallmark of successful dialogue dynamics.

Mitigating toxic content is critical for maintaining a healthy social platform, yet existing detoxification systems face significant limitations: overcorrection from uniformly processing all toxic comments, and parallel data scarcity in paraphrasing model training. To tackle these challenges, we propose Detoxifiability-Aware Detoxification (DID), a novel paradigm that adaptively conducts filtering or paraphrasing for each toxic comment based on its detoxifiability, namely whether it can be paraphrased into a benign comment in essence. Specifically, DID integrates three core modules: (1) an unsupervised detoxifiability discriminator, (2) a semantic purification module that extracts harmful intents and then performs targeted paraphrasing only on detoxifiable comments and (3) a feedback-adaptive refinement loop that processes remaining harmful contents only when they are detoxifiable. Experimental results demonstrate that DID significantly outperforms existing approaches on academic data and an industrial platform, establishing a novel and practical modeling paradigm for comment detoxification.

Strongly human-correlated evaluation metrics serve as an essential compass for the development and improvement of generation models and must be highly reliable and robust. Recent embedding-based neural text evaluation metrics, such as COMET for translation tasks, are widely used in both research and development fields. However, there is no guarantee that they yield reliable evaluation results due to the black-box nature of neural networks. To raise concerns about the reliability and safety of such metrics, we propose a method for finding a single adversarial text in the discrete space that is consistently evaluated as high-quality, regardless of the test cases, to identify the vulnerabilities in evaluation metrics. The single hub text found with our method achieved 79.1 COMET% and 67.8 COMET% in the WMT’24 English-to-Japanese (En–Ja) and English-to-German (En–De) translation tasks, respectively, outperforming translations generated individually for each source sentence by using M2M100, a general translation model. Furthermore, we also confirmed that the hub text found with our method generalizes across multiple language pairs such as Ja–En and De–En.

With recent advancements in large language models, web agents have been greatly improved. However, dealing with complex and dynamic web environments requires more advanced planning and search abilities. Previous studies usually adopt a greedy one-way search strategy, which may struggle to recover from erroneous states. In this work, we enhance web agents with an explicit rollback mechanism, enabling the agent to revert back to a previous state in its navigation trajectory. This mechanism gives models the flexibility to directly control the search process, leading to an effective and efficient web navigation method. We conduct experiments on two live web navigation benchmarks with zero-shot and fine-tuning settings. The results demonstrate the effectiveness of our proposed approach.

There has been considerable interest in using surprisal from Transformer-based language models (LMs) as predictors of human sentence processing difficulty. Recent work has observed an inverse scaling relationship between Transformers’ per-word estimated probability and the predictive power of their surprisal estimates on reading times, showing that LMs with more parameters and trained on more data are less predictive of human reading times. However, these studies focused on predicting latency-based measures. Tests on brain imaging data have not shown a trend in any direction when using a relatively small set of LMs, leaving open the possibility that the inverse scaling phenomenon is constrained to latency data. This study therefore conducted a more comprehensive evaluation using surprisal estimates from 17 pre-trained LMs across three different LM families on two functional magnetic resonance imaging (fMRI) datasets. Results show that the inverse scaling relationship between models’ per-word estimated probability and model fit on both datasets still obtains, resolving the inconclusive results of previous work and indicating that this trend is not specific to latency-based measures.

When learning to code, students often develop misconceptions about various programming language concepts. These can not only lead to bugs or inefficient code, but also slow down the learning of related concepts. In this paper, we introduce McMining, the task of mining programming misconceptions from samples of code from a student. To enable the training and evaluation of McMining systems, we develop an extensible benchmark dataset of misconceptions, together with a large set of code samples where these misconceptions are manifested. We then introduce two LLM-based McMiner approaches and, through extensive evaluations, show that models from the Gemini, Claude, and GPT families are effective at discovering misconceptions in student code.

The rapid advancement of multilingual pre-trained transformers has fueled significant progress in natural language understanding across diverse languages. Yet, their inner workings remain opaque, especially with regard to how individual neurons encode and generalize semantic and affective features across languages. This paper presents an interpretability study of a fine-tuned XLM-R model for multilingual emotion classification. Using neuron-level activation analysis, we investigate the variance of neurons across labels, cross-lingual alignment of activations, and the existence of “polyglot” versus language-specific neurons. Our results reveal that while certain neurons consistently encode emotion-related concepts across languages, others show strong monolingual specialization.

Hate speech on social media poses significant challenges for content moderation and user safety. While various datasets exist for hate speech detection, existing approaches treat hate speech as a monolithic phenomenon, detecting hateful content by using simple categorical labels such as hate, offensive, or toxic. This approach fails to distinguish between the speaker’s underlying motivations and the content’s potential societal consequences. This paper introduces I2-Hate, a novel dataset with a dual taxonomy that separately captures Intent (why the speaker produced hate speech) and Impact (what harm it may cause to individuals and communities) of online hateful posts. This dual-taxonomy approach enables moderation systems to differentiate hateful content based on underlying motivation and potential harm, supporting more nuanced intervention strategies. We release the I2-Hate dataset and code publicly.

Large reasoning models are trained to solve problems by decomposing them into steps.While they show impressive progress on reasoning tasks, “reasoning” here is typically limited to formal reasoning, i.e., math, code, and logic.An open question is whether these abilities transfer to pronominal reasoning, where step-by-step thinking in non-reasoning models worsens performance, but code pre-training may help.I answer this question by evaluating six pairs of original and DeepSeek-distilled models (1.5B-70B parameters) on six challenging datasets for English pronoun resolution (identifying whom a pronoun refers to) and pronoun fidelity (learning and applying a pronoun mapping correctly).Performance improves statistically significantly on all datasets (31% relative increase), indicating that distilling step-by-step formal reasoning does in fact help with pronominal reasoning, in part by improving instruction-following.With a qualitative evaluation of 720 generations, I show that improvements occur across granular error types, and come from plausible-looking reasoning chains employing a variety of reasoning strategies.However, the gains put models just above random performance on these datasets, leaving plenty of room for improvement.

Temporal reasoning is fundamental to human cognition and is crucial for various real-world applications. While recent advances in Large Language Models have demonstrated promising capabilities in temporal reasoning, existing benchmarks primarily rely on rule-based construction, lack contextual depth, and involve a limited range of temporal entities. To address these limitations, we introduce Chinese Time Reasoning (CTM), a benchmark designed to evaluate LLMs on temporal reasoning within the extensive scope of Chinese dynastic chronology. CTM emphasizes cross-entity relationships, pairwise temporal alignment, and contextualized and culturally-grounded reasoning, providing a comprehensive evaluation. Extensive experimental results reveal the challenges posed by CTM and highlight potential avenues for improvement.