Austria

As large language models (LLMs) grow in parameter size and context length, computation precision has been reduced from 16-bit to 4-bit to improve inference efficiency. However, this reduction causes accuracy degradation due to activation outliers. Recent rotation-based INT4 quantization attempts to address this through rotation matrix calibration, but they require hours of overhead per model deployment and leave significant computation unquantized in long-context scenarios. Microscaling (MX) floating-point (FP) formats offer fine-grained representation with a shared scale, enabling fully quantized matrix multiplications through direct casting without calibration. However, existing research shows unsatisfactory empirical results for MXFP4 inference, and the robustness of MX formats remains largely unexplored.

In this work, we uncover the fundamental tradeoffs of the MX format: while it effectively suppresses activation outliers, it does so at the cost of increased group-wise asymmetry. To address this, we propose an asymmetric MX format for a 4-bit floating point (AMXFP4), which employs asymmetric shared scales to handle both outliers and group-wise asymmetry without requiring calibration. Our custom compute-engine implementation shows that the AMXFP4-based Multiply-Accumulate (MAC) design adds marginal resource overhead while delivering substantial accuracy improvements. Extensive experiments across benchmarks demonstrate that AMXFP4 outperforms MXFP4 in visual question answering (VQA) by 3% and surpasses rotation-based techniques on CSQA by 1.6%. Additionally, AMXFP4 shows superior performance compared to the recently deployed commercial MXFP4 format.

ACL 2025

AMXFP4: Taming Activation Outliers with Asymmetric Microscaling Floating-Point for 4-bit LLM Inference

numerical formats

quantization

large language models

**Welcome to EMNLP 2023!**

On behalf of the EMNLP 2023 Organizing Committee, I extend a warm and heartfelt welcome to all of you to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). It is with immense pleasure and excitement that we gather here in Singapore, a vibrant hub of innovation and technological advancement.

The conference program is packed with insightful presentations, thought-provoking workshops, and engaging networking opportunities. In addition to the technical sessions, we have also planned several social events that will provide you with the opportunity to connect with your colleagues.

The conference is held in person at the Resorts World Convention Centre in Singapore, and available on-line with the help of Underline.

For those in Singapore, I hope that you will find time to explore the exciting Sentosa Island, Gardens by the Bay, Singapore Botanic Gardens and the many other attractions unique to Singapore.

Of course, we are grateful to our sponsors and partners for their generous support of EMNLP 2023, whose contributions make it possible for us to host this world-class event.

Yuji Matsumoto (RIKEN AIP) 
EMNLP 2023 General Chair

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EMNLP 2023

EMNLP 2023 took place in Singapore from Dec 6th to Dec 10th, 2023.

poster

### 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.* 
[Read more](https://drive.google.com/file/d/1GI_hvOpjswAuYdUTromfeDiPpCcqidwg/view?usp=sharing)

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

There have been attempts to utilize linear probe for detoxification, with existing studies relying on a single toxicity probe vector to reduce toxicity. However, toxicity can be fine-grained into various subcategories, making it difficult to remove certain types of toxicity by using a single toxicity probe vector. To address this limitation, we propose a category-specific toxicity probe vector approach. First, we train multiple toxicity probe vectors for different toxicity categories. During generation, we dynamically select the most relevant toxicity probe vector based on the current context. Finally, the selected vector is dynamically scaled and subtracted from model. Our method successfully mitigated toxicity from categories that the single probe vector approach failed to detoxify. Experiments demonstrate that our approach achieves up to a 78.52% reduction in toxicity on the evaluation dataset, while fluency remains nearly unchanged, with only a 0.052% drop compared to the unsteered model.

DAPI: Domain Adaptive Toxicity Probe Vector Intervention, for Fine-Grained Detoxification

Large language models have shown tremendous potential across various NLP tasks, and instruction tuning has been widely adopted to elicit their superior performance. However, instruction tuning may overly tailor the models to task-specific formats, potentially compromising their generalization on unseen tasks. 
We attribute the issue to the spurious correlations learned between inputs and targets. We propose explicit task knowledge injection to mitigate these shortcuts with latent task adaptation and knowledge reinstatement. Latent tasks serve as interpolations between new tasks and facilitate knowledge sharing with joint adaptation enabling the model to build task knowledge more smoothly. Knowledge reinstatement helps optimize building new knowledge with prior knowledge. Specifically, we retrieve input-relevant latent tasks and jointly learn the task and the relevant latent tasks. Moreover, we prompt the model to recall the forms of inputs corresponding to the target and build the task knowledge through the reinstatement of prior knowledge while learning the new task.
We conduct extensive experiments on state-of-the-art large language models including Llama3.1-8B and Vicuna-13B across 1000+ instruction-following tasks to demonstrate the effectiveness of our method. The results demonstrate our method improves generalization on both in-domain and out-of-domain unseen tasks.

Task Knowledge Injection via Interpolations and Reinstatement for Large Language Model Generalization

Memorization is a fundamental ability of Transformer-based Large Language Models, achieved through learning. In this paper, we propose a paradigm shift by designing an architecture to memorize text directly, bearing in mind the principle that memorization precedes learning. We introduce MeMo, a novel architecture for language modeling that explicitly memorizes sequences of tokens in layered associative memories. By design, MeMo offers transparency and the possibility of model editing, including forgetting texts. We experimented with the MeMo architecture, showing the memorization power of the one-layer and the multi-layer configurations.

Position Paper: MeMo: Towards Language Models with Associative Memory Mechanisms

We present DeRAGEC, a method for improving Named Entity (NE) correction in Automatic Speech Recognition (ASR) systems. By extending the Retrieval-Augmented Generative Error Correction (RAGEC) framework, DeRAGEC employs synthetic denoising rationales to filter out noisy NE candidates before correction. By leveraging phonetic similarity and augmented definitions, it refines noisy retrieved NEs using in-context learning, requiring no additional training. Experimental results on CommonVoice and STOP datasets show significant improvements in Word Error Rate (WER) and NE hit ratio, outperforming baseline ASR and RAGEC methods. Specifically, we achieved a 28% relative reduction in WER compared to ASR without postprocessing.

DeRAGEC: Denoising Named Entity Candidates with Synthetic Rationale for ASR Error Correction

Personalized opinion summarization is crucial as it considers individual user interests while generating product summaries. Recent studies show that although large language models demonstrate powerful text summarization and evaluation capabilities without the need for training data, they face difficulties in personalized tasks involving long texts. To address this, Rehearsal, a personalized opinion summarization framework via LLMs-based role-playing, is proposed. Having the model act as the user, the model can better understand the user's personalized needs. Additionally, a role-playing supervisor and practice process are introduced to improve the role-playing ability of the LLMs, leading to a better expression of user needs. Furthermore, through suggestions from virtual users, the summary generation is intervened, ensuring that the generated summary includes information of interest to the user, thus achieving personalized summary generation.
Experiment results demonstrate that our method can effectively improve the level of personalization in large model-generated summaries.

Rehearse With User: Personalized Opinion Summarization via Role-Playing based on Large Language Models

Focus of this work is the prediction of reading times as the task is customarily dealt with in literature: that is, by collecting eye-tracking data that are averaged and employed to train learning models. We start by observing that systems trained on average values are ill-suited for the prediction of the reading times for specific subjects, as they fail to account for individual variability and accurately analyze the reading gestures of specific reader groups, or to target specific user needs. To overcome such limitation, that is to predict the reading times for a specific subject, we propose a novel approach based on creating an embedding to compactly describe her/his fixations. Embeddings are used to individuate readers that share same or similar reading behavior from a reference corpus. Models are then trained on values averaged over this subset of similar readers. Experimental results indicate that the proposed approach consistently outperforms its corresponding variants, in which predictions of reading times for specific readers are based on data from all subjects rather than from the most similar ones.

Beyond the Average Reader: the Reader Embedding Approach

Large Language Models (LLMs) can generate high-quality arguments, yet their ability to engage in *nuanced and persuasive communicative actions* remains largely unexplored. This work explores the persuasive potential of LLMs through the framework of Jürgen Habermas’ Theory of Communicative Action. It examines whether LLMs express illocutionary intent (i.e., pragmatic functions of language such as conveying knowledge, building trust, or signaling similarity) in ways that are comparable to human communication.

We simulate online discussions between opinion holders and LLMs using conversations from the persuasive subreddit *ChangeMyView*. We then compare the likelihood of illocutionary intents in human-written and LLM-generated counter-arguments, specifically those that successfully changed the original poster’s view. We find that all three LLMs effectively convey illocutionary intent — often more so than humans — potentially increasing their anthropomorphism. Further, LLMs craft responses that closely align with the opinion holder’s intent, a strategy strongly associated with opinion change. Finally, crowd-sourced workers find LLM-generated counter-arguments more *agreeable* and consistently prefer them over human-written ones. These findings suggest that LLMs’ persuasive power extends beyond merely generating high-quality arguments. On the contrary, training LLMs with human preferences effectively tunes them to mirror human communication patterns, particularly nuanced communicative actions, potentially increasing individuals’ susceptibility to their influence.

“I understand your perspective”: LLM Persuasion through the Lens of Communicative Action Theory

As large language models (LLMs) become key advisors in various domains, their cultural sensitivity and reasoning skills are crucial in multicultural environments. We introduce Nunchi-Bench, a benchmark designed to evaluate LLMs' cultural understanding, with a focus on Korean superstitions. The benchmark consists of 247 questions spanning 31 topics, assessing factual knowledge, culturally appropriate advice, and situational interpretation. We evaluate multilingual LLMs in both Korean and English to analyze their ability to reason about Korean cultural contexts and how language variations affect performance. To systematically assess cultural reasoning, we propose a novel verification strategy with customized scoring metrics that capture the extent to which models recognize cultural nuances and respond appropriately. Our findings highlight significant challenges in LLMs' cultural reasoning. While models generally recognize factual information, they struggle to apply it in practical scenarios. Furthermore, explicit cultural framing enhances performance more effectively than relying solely on the language of the prompt. To support further research, we publicly release Nunchi-Bench alongside a leaderboard.

Nunchi-Bench: Benchmarking Language Models on Cultural Reasoning with a Focus on Korean Superstition

The synergistic mechanism based on Speculative Decoding (SD) has garnered considerable attention as a simple yet effective approach for accelerating the inference of large language models (LLMs). Nonetheless, the high rejection rates require repeated LLMs calls to validate draft tokens, undermining the overall efficiency gain of SD.In this work, we revisit existing verification mechanisms and propose a novel synergetic mechanism Consultant Decoding (CD). CD achieves up to a 2.5-fold increase in inference speed compared to the target model, while maintaining comparable generation quality (~100% of the target model’s performance). Interestingly, this is achieved by combining models whose parameter sizes differ by two orders of magnitude.In addition, CD reduces the call frequency of the large target model to below 10%, particularly in more demanding tasks.CD’s performance was even found to surpass that of the large target model, which theoretically represents the upper bound for speculative decoding.

Consultant Decoding: Yet Another Synergistic Mechanism

Implicit content plays a crucial role in political discourse, where systematically employ pragmatic strategies such as implicatures and presuppositions to influence their audiences. Large Language Models (LLMs) have demonstrated strong performance in tasks requiring complex semantic and pragmatic understanding, highlighting their potential for detecting and explaining the meaning of implicit content. However, their ability to do this within political discourse remains largely underexplored. Leveraging, for the very first time, the large IMPAQTS corpus comprising transcribed Italian political speeches with expert annotations of various types of implicit content, we propose methods to test the effectiveness of LLMs in this challenging problem. Through a multiple-choice task and an open-ended generation task, we demonstrate that all tested models struggle to interpret presuppositions and implicatures. To illustrate, the best-performing model provides a fully correct explanation in only one-fourth of cases in the open-ended generation setup. We conclude that current LLMs lack the key pragmatic capabilities necessary for accurately interpreting highly implicit language, such as that found in political discourse. At the same time, we highlight promising trends and future directions for enhancing model performance. We release our data and code at: https://github.com/WalterPaci/IMPAQTS-PID

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DAPI: Domain Adaptive Toxicity Probe Vector Intervention, for Fine-Grained Detoxification

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LLM-FP4: 4-Bit Floating-Point Quantized Transformers

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