Morocco

Aligning Large Language Models (LLMs) with human preferences is crucial, but standard methods like Reinforcement Learning from Human Feedback (RLHF) are often complex and unstable. In this work, we propose a new, simpler approach that recasts alignment through the lens of Maximum Marginal Likelihood (MML) estimation. Our new MML-based Preference Optimization (MMPO) maximizes the marginal log-likelihood of a preferred text output, using the preference pair as samples for approximation, and forgoes the need for both an explicit reward model and entropy maximization. We theoretically demonstrate that MMPO implicitly performs preference optimization, producing a weighted gradient that naturally up-weights chosen responses over rejected ones. Across models ranging from 135M to 8B parameters, we empirically show that MMPO: 1) is more stable with respect to the hyperparameter compared to alternative baselines, and 2) achieves competitive or superior preference alignment while better preserving the base model&#39;s general language capabilities. Through a series of ablation experiments, we show that this improved performance is indeed attributable to MMPO&#39;s implicit preference optimization within the gradient updates.

EACL 2026

Offline Preference Optimization via Maximum Marginal Likelihood Estimation

maximum marginal likelihood

preference optimization

## Welcome to EMNLP 2024! 
We are excited to welcome you to one of the most prominent conferences in the field of Natural Language Processing. This year, EMNLP 2024 is being held in a hybrid format,
offering both virtual and in-person participation in beautiful Miami. Due to a record-breaking number of submissions, we've expanded the total number of accepted papers to accommodate more cutting-edge research from around the globe.
### [Conference Handbook](https://drive.google.com/file/d/1WPROgxjLAC96AJL7Ugy0tEnYm7dkrbHt/view?usp=sharing)

You are required to register for this event. **Please register [here](https://2024.emnlp.org/registration/).** The EMNLP 2024 event page on Underline will be open to public one week prior to the event.

Please register!

EMNLP 2024

EMNLP 2024 will take place in Miami, Florida from Nov 12th to Nov 16th, 2024, at the Hyatt Regency Miami Hote and on Underline for remote participants.

Aligning Large Language Models (LLMs) with human preferences is crucial, but standard methods like Reinforcement Learning from Human Feedback (RLHF) are often complex and unstable. In this work, we propose a new, simpler approach that recasts alignment through the lens of Maximum Marginal Likelihood (MML) estimation. Our new MML-based Preference Optimization (MMPO) maximizes the marginal log-likelihood of a preferred text output, using the preference pair as samples for approximation, and forgoes the need for both an explicit reward model and entropy maximization. We theoretically demonstrate that MMPO implicitly performs preference optimization, producing a weighted gradient that naturally up-weights chosen responses over rejected ones. Across models ranging from 135M to 8B parameters, we empirically show that MMPO: 1) is more stable with respect to the hyperparameter compared to alternative baselines, and 2) achieves competitive or superior preference alignment while better preserving the base model's general language capabilities. Through a series of ablation experiments, we show that this improved performance is indeed attributable to MMPO's implicit preference optimization within the gradient updates.

technical paper

#### *Message from the General Chair, Aline Villavicencio*
I’m delighted and honoured to welcome you to the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2026), taking place in the beautiful city of Rabat, in Morocco, in March 24-29, 2026. EACL is the flagship European conference of the Association and EACL 2026 proudly continues our field’s tradition of excellence in scholarship, innovation, and inclusivity. I am deeply grateful to the many volunteers whose dedication, generosity, and tireless efforts have made this conference possible.
For the first time EACL is being hosted in the African continent. This is an important milestone for our community, and we are grateful to our Moroccan hosts for enabling this historic moment by bringing this edition of EACL to Rabat. We are also delighted that the Second Arabic NLP School is co-located with EACL. We hope attendees enjoy this wonderful opportunity to strengthen ties with the Computational Linguistics communities across the African continent. *[Read full message](https://drive.google.com/file/d/14NlmHvwM6fPJuMmOvVh7K0vtQbEyv3SZ/view?usp=sharing)* 

<html><button style="display: inline-flex; align-items: center; justify-content: center; white-space: nowrap; border-radius: 9999px; font-weight: bold; background: #7c3aed; color: white; font-family: 'Space Grotesk', sans-serif; height: 40px; font-size: 16px; padding: 0 20px; border: none; cursor: pointer" onclick="window.open('https://underline.io/events/522/reception','_blank')">Go to Workshops and Tutorials Program</button></html>

To access this event page, you need to log in with the **email address you registered with**. Access credentials will be sent to your email from Underline - subject line "Welcome to EACL 2026". Please be sure to check your spam email folder if you do not see an email confirmation right away.

Please log in

To access this event page, you are required to register.
Please complete your registration to continue.

We recommend reading [**the registration information**](https://2026.eacl.org/registration/) first.

**Online Registration Form**: https://acl.swoogo.com/eacl2026

Registration Required

Welcome to the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL). EACL 2026 will be held in Rabat, Morocco, from March 24–29, 2026. 

Multi-hop reasoning over long contexts remains challenging, as it requires integrating relevant contexts scattered across distant sources while resisting semantic drift and noise from distracting content. While retrieval-augmented generation (RAG) has emerged as the prevailing solution, most RAG approaches encode and store context in monolithic memory representations, resulting in noisy retrieval and brittle reasoning. To overcome these limitations, we introduce TAG (Tailoring Memory Granularity), a framework that prestructures memory into diverse granularities and employs a reward-guided navigator to adaptively compose hybrid memory tailored to each query. The navigator is trained with a multi-objective Bradley–Terry loss that learns the relative utility of different memory types, enabling dynamic routing across granularities. This design allows RAG systems to balance fine-grained detail with high-level abstraction, yielding more reliable reasoning. Extensive experiments on long-context multi-hop question answering (QA) benchmarks show that TAG achieves state-of-the-art performance. With only 0.033% additional parameters, it remains lightweight, highlighting its practicality as a scalable and effective solution for enhancing language model agents in complex, real-world scenarios.

Tailoring Memory Granularity for Multi-Hop Reasoning over Long Contexts

Social engineering scams increasingly employ personalized, multi-turn deception, exposing the limits of traditional detection methods. While Large Language Models (LLMs) show promise in identifying deception, their cognitive assistance potential remains underexplored. We propose ScriptMind, an integrated framework for LLM-based scam detection that bridges automated reasoning and human cognition. It comprises three components: the Crime Script Inference Task (CSIT) for scam reasoning, the Crime Script–Aware Inference Dataset (CSID) for fine-tuning small LLMs, and the Cognitive Simulation-based Evaluation of Social Engineering Defense (CSED) for assessing real-time cognitive impact. Using 571 Korean scam cases, we built 22,712 structured scammer-sequence training instances. Experimental results show that the 11B small LLM fine-tuned with ScriptMind outperformed GPT-4o by 13%, achieving superior performance over commercial models in detection accuracy, false-positive reduction, scammer utterance prediction, and rationale quality. Moreover, in phone scam simulation experiments, it significantly enhanced and sustained users’ suspicion levels, improving their cognitive awareness of scams. ScriptMind represents a step toward human-centered, cognitively adaptive LLMs for scam defense.

SCRIPTMIND: Crime Script Inference and Cognitive Evaluation for LLM-based Social Engineering Scam Detection System

High-quality search is essential for the success of online platforms, spanning e-commerce, social media, shopping-focused applications, and broader search systems such as content discovery and enterprise web search. To ensure optimal user experience and drive business growth, continuous evaluation and improvement of search systems is crucial. This paper introduces PROBES, a novel multi-task system powered by Large Language Models (LLMs) designed for end-to-end evaluation of semantic search systems. PROBES identifies context-aware relevance using a fine-grained scale (exact, substitute, complement, irrelevant) by leveraging the query category, feature-level intent, and category-aware feature importance, enabling more precise and consistent judgments than relying solely on raw query text. This allows PROBES to provide differentiated relevance assessment across a diverse range of query categories. PROBES then dives deeper to understand the reason behind irrelevant results (Precision issues) by checking product content conflicts and inaccuracies. It also analyzes Missed Recall by leveraging retrieval and relevance models to determine whether a missed recall was due to a selection issue or a ranking/retrieval system issue. To evaluate PROBES, we introduce a new metric, the Actionable Error Rate (AER), defined as the proportion of actionable errors over all flagged errors. We observe that PROBES operates at an AER of 76%, generating actionable insights across 100 product categories.

PROBES : Performance and Relevance Observation for BEtter Search

Extracting actionable suggestions from customer reviews is essential for operational decision-making, yet these directives are often embedded within mixed-intent, unstructured text. Existing approaches either classify suggestion-bearing sentences or generate high-level summaries, but rarely isolate the precise improvement instructions businesses need. We evaluate a hybrid pipeline combining a high-recall RoBERTa classifier trained with a precision–recall surrogate to reduce unrecoverable false negatives with a controlled, instruction-tuned LLM for suggestion extraction, categorization, clustering, and summarization. Across real-world hospitality and food datasets, the hybrid system outperforms prompt-only, rule-based, and classifier-only baselines in extraction accuracy and cluster coherence. Human evaluations further confirm that the resulting suggestions and summaries are clear, faithful, and interpretable. Overall, our results show that hybrid reasoning architectures achieve meaningful improvements fine-grained actionable suggestion mining while highlighting challenges in domain adaptation and efficient local deployment.

A Hybrid Supervised-LLM Pipeline for Actionable Suggestion Mining in Unstructured Customer Reviews

Event argument extraction (EAE) is a crucial task in information extraction. However, its performance heavily depends on expensive annotated data, making data scarcity a persistent challenge. Data augmentation serves as an effective approach to improving model performance in low-resource settings, yet research on applying LLMs for EAE augmentation remains preliminary. In this study, we pay attention to the boundary sensitivity of EAE and investigate four LLM-based augmentation strategies: argument replacement, adjunction rewriting, their combination, and annotation generation. We conduct comprehensive experiments across four benchmark datasets, employing GPT-4o-Mini and DeepSeek-R1-7B as data generators. Our results show that boundary-aware augmentation consistently leads to greater performance improvements over boundary-agnostic methods. In addition to performance gains, we provide a detailed analysis of augmentation quality from multiple perspectives, including uncertainty reduction, error types, data quality, and data scale. This work offers both empirical evidence and practical guidance for leveraging LLMs to enhance event argument extraction under low-resource conditions.

Boundary-Aware LLM Augmentation for Low-Resource Event Argument Extraction

We introduce LM-Lexicon, an innovative definition modeling approach that incorporates data clustering, semantic expert learning, and model merging using a sparse mixture-of-experts architecture. By decomposing the definition modeling task into specialized semantic domains, where small language models are trained as domain experts, LM-Lexicon achieves substantial improvements (+7% BLEU score compared with the prior state-of-the-art model) over existing methods on five widely used benchmarks. Empirically, we demonstrate that 1) the clustering strategy enables fine-grained expert specialization with nearly 10% improvement in definition quality; 2) the semantic-aware domain-level routing mechanism achieves higher expert efficacy (+1%) than conventional token-level routing; and 3) further performance gains can be obtained through test-time compute and semantic expert scaling. Our work advances definition modeling while providing insights into the development of efficient language models for semantic-intensive applications. The code, data, and models will be made publicly available upon completion of the review process.

LM-Lexicon: Improving Definition Modeling via Harmonizing Semantic Experts

The promise of LLM watermarking rests on a core assumption that a specific watermark proves authorship by a specific model. We demonstrate that this assumption is dangerously flawed. We introduce the threat of watermark spoofing, a sophisticated attack that allows a malicious model to generate text containing the authentic-looking watermark of a trusted, victim model. This enables the seamless misattribution of harmful content, such as disinformation, to reputable sources. The key to our attack is repurposing watermark radioactivity, the unintended inheritance of data patterns during fine-tuning, from a discoverable trait into an attack vector. By distilling knowledge from a watermarked teacher model, our framework allows an attacker to steal and replicate the watermarking signal of the victim model. This work reveals a critical security gap in text authorship verification and calls for a paradigm shift towards technologies capable of distinguishing authentic watermarks from expertly imitated ones. Our code is available at https://anonymous.4open.science/r/ditto-8838/.

DITTO: A Spoofing Attack Framework on Watermarked LLMs via Knowledge Distillation

Search-augmented large language models (LLMs) excel at knowledge-intensive tasks by integrating external retrieval. However, they often over-search – unnecessarily invoking search tool even when it does not improve response quality, which leads to computational inefficiency and hallucinations by incorporating irrelevant context. In this work, we conduct a systematic evaluation of over-searching across multiple dimensions, including query types, model categories, retrieval conditions, and multi-turn conversations. Our findings show: (i) search generally improves answer accuracy on answerable queries but harms abstention on unanswerable ones; (ii) over-searching is more pronounced in complex reasoning models and deep research systems, is exacerbated by noisy retrieval, and compounds across turns in multi-turn conversations; and (iii) the composition of retrieved evidence is crucial, as the presence of negative evidence improves abstention. To quantify over-searching, we introduce Tokens Per Correctness (TPC), an evaluation metric that captures the performance-cost trade-off for search-augmented LLMs. Lastly, we investigate mitigation approaches at both the query and retrieval levels and release the OverSearchQA benchmark to foster continued research into efficient search-augmented LLMs.

Over-Searching in Retrieval-Augmented Large Language Models

Multi-LLM systems enhance the creativity of large language models by simulating human collective intelligence but suffer from significant drawbacks, such as high computational costs and inference latency. To address these limitations, we propose BILLY (BlendIng persona vectors for Large Language model creativitY), a training-free framework that captures the benefits of multi-LLM collaboration, i.e. inducing diverse perspectives and specialized expertise, within a single model. BILLY operates by extracting and blending multiple distinct persona vectors directly in the model's activation space. We steer the model's generation process with this merged vector while inference, enabling multi-perspective output without explicit multi-LLM communication. Our experiments across creativity-oriented benchmarks demonstrate that BILLY surpasses single model prompting and traditional multi-LLM approaches, while substantially reducing inference time and computational costs. Our analyses further reveal that distinct persona vectors can be blended to achieve both effective control over complementary aspects of generation and greater interpretability.

BILLY: Steering Large Language Models via Merging Persona Vectors for Creative Generation

Novel metaphor comprehension involves complex semantic processes and linguistic creativity, making it an interesting task for studying language models (LMs). This study investigates whether surprisal, a probabilistic measure of predictability in LMs, correlates with annotations of metaphor novelty in different datasets. We analyse the surprisal of metaphoric words in corpus-based and synthetic metaphor datasets using 16 causal LM variants. We propose a cloze-style surprisal method that conditions on full-sentence context. Results show that LM surprisal yields significant moderate correlations with scores/labels of metaphor novelty. We further identify divergent scaling patterns: on corpus-based data, correlation strength decreases with model size (inverse scaling effect), whereas on synthetic data it increases (quality–power hypothesis). We conclude that while surprisal can partially account for annotations of metaphor novelty, it remains limited as a metric of linguistic creativity. Code and data are publicly available: https://github.com/OmarMomen14/surprisal-metaphor-novelty

Downloads

Next from EACL 2026

Tailoring Memory Granularity for Multi-Hop Reasoning over Long Contexts

Similar lecture

Margin Matching Preference Optimization: Enhanced Model Alignment with Granular Feedback

.css-70qvj9{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}Downloads

Next from EACL 2026

Tailoring Memory Granularity for Multi-Hop Reasoning over Long Contexts

Similar lecture

Margin Matching Preference Optimization: Enhanced Model Alignment with Granular Feedback

Downloads