China

Multi-digit addition is a clear probe of the computational power of large language models. To dissect the internal arithmetic processes in LLaMA-3-8B-Instruct, we combine linear probing with logit-lens inspection. Inspired by the step-by-step manner in which humans perform addition, we propose and analyze a coherent four-stage trajectory in the forward pass: Formula-structure representations become linearly decodable first, while the answer token is still far down the candidate list. Core computational features then emerge prominently. At deeper activation layers, numerical abstractions of the result become clearer, enabling near-perfect detection and decoding of the individual digits in the sum. Near the output, the model organizes and generates the final content, with the correct token reliably occupying the top rank. This trajectory suggests a hierarchical process that favors internal computation over rote memorization. We release our code and data to facilitate reproducibility.

EMNLP 2025

Addition in Four Movements: Mapping Layer-wise Information Trajectories in LLMs

poster

## Welcome!
"I am excited to welcome you to this year’s edition of the Conference on Empirical Methods in Natural Language Processing! Importantly, it marks the 30th edition of EMNLP. With over 8,000 submissions, more than 3,000 accepted papers, and thousands of attendees, we have come a long way from that first
workshop, which had 14 accepted papers. As the field looks ahead, Suzhou is the fitting location for celebrating this milestone: rooted in a long literary tradition, yet modern and forward-looking, and home to a large share of the NLP community."<br>

*Message from the General Chair, Dirk Hovy*

[**Link to Conference Handbook**](https://drive.google.com/file/d/1johU5QqVVYO4RfH7QcIORr7qrVBdzdwC/view?usp=sharing)





<br>

Celebrate 30 Years of EMNLP! 
EMNLP 2025 will be held in Suzhou, China from November 5th to November 9th, 2025.

Recent studies have highlighted the remarkable knowledge retention capabilities of Large Language Models (LLMs) like GPT-4, while simultaneously revealing critical limitations in maintaining knowledge currency and accuracy. Existing knowledge editing methodologies, designed to update specific factual information without compromising general model performance, often encounter two fundamental challenges: parameter conflict during knowledge overwriting and excessive computational overhead. In this paper, we introduce ForGet (Forget for Get), a novel approach grounded in the principle of "forgetting before learning". By pinpointing the location within the LLM that corresponds to the target knowledge, we first erase the outdated knowledge and then insert the new knowledge at this precise spot. ForGet is the first work to leverage a two-phase gradient-based process for knowledge editing, offering a lightweight solution that also delivers superior results. Experimental findings show that our method achieves more effective knowledge editing at a lower cost compared to previous techniques across various base models.

Forget for Get: A Lightweight Two-phase Gradient Method for Knowledge Editing in Large Language Models

Large Language Models (LLMs) are trained on diverse and often conflicting knowledge spanning multiple domains and time periods. Some of this knowledge is only valid within specific temporal contexts, such as answering the question, ``Who is the President of the United States in 2022?'' Ensuring LLMs generate time-appropriate responses is crucial for maintaining relevance and accuracy. In this work we explore activation engineering as a method for temporally aligning LLMs to improve factual recall without any training. Activation engineering has predominantly been used to steer subjective and qualitative outcomes such as toxicity or behavior. Our research is one of few that uncovers the bounds of activation engineering on objective outcomes. We explore an activation engineering technique to anchor LLaMA 2, LLaMA 3.1, Qwen 2 and Gemma 2 to specific points in time and examine the effects of varying injection layers and prompting strategies. Our experiments demonstrate up to a 44\% and 16\% improvement in relative and explicit prompting respectively, achieving comparable performance to the fine-tuning method proposed by Zhao et al. (2024). Notably, for LLaMA 2 and LLaMA 3.1 our approach achieves similar results to the fine-tuning baseline while being significantly more computationally efficient and requiring no pre-aligned datasets. Both datasets and code will be released publicly upon paper acceptance.

Temporal Alignment of Time Sensitive Facts with Activation Engineering

In this paper, we propose a novel benchmark, ChronoBias, for evaluating group bias in retrieval-augmented language models over time. Our benchmark is built upon a template-based semi-automated generation method, which effectively balances the quality and quantity tradeoff of existing benchmark curation methods. We show that for the type of knowledge that changes over time, group bias must be measured by jointly considering the time-varying nature of the knowledge for each group. Specifically, even a majority group of high parametric knowledge is susceptible to severe performance degradation given incorrect information, especially when the knowledge itself is volatile. Additionally, we show that high-capacity LLMs (e.g., ChatGPT-4o) show knowledge conflict-like patterns even after their knowledge cutoff due to their forecasting capability, which we call forecasting conflict. The forecasting conflict is more prominent in the majority groups with high knowledge volatility. We then propose a fairness metric conditioned on the time interval, and show that time-dependent notion of group fairness is necessary to maintain the group fairness over all time steps. We conclude that an adaptive retrieval for each group and time interval is necessary, to both enhance the group fairness and prevent unnecessary performance degradation.

ChronoBias: A Benchmark for Evaluating Temporal Group Bias in the Time-sensitive Knowledge of Large Language Models

Graphical user interface (GUI) agents powered by multimodal large language models (MLLMs) have shown greater promise for human-interaction. However, due to the high tuning cost, users often rely on open-source GUI agents or APIs offered by AI providers, which introduces a critical but underexplored supply chain threat: backdoor attacks. In this work, we first unveil that MLLM-powered GUI agents naturally expose multiple interaction-level triggers, such as historical steps, environment states, and task progress. Based on this observation, we introduce AgentGhost, an effective and stealthy framework for red-teaming backdoor attacks. Specifically, we first construct composite triggers by combining goal and interaction levels, allowing GUI agents to unintentionally activate backdoors while ensuring task utility. Then, we formulate backdoor injection as a Min-Max optimization problem that uses supervised contrastive learning to maximize the feature difference across sample classes at the representation space, improving flexibility of the backdoor. Meanwhile, it adopts supervised fine-tuning to minimize the discrepancy between backdoor and clean behavior generation, enhancing effectiveness and utility. Extensive evaluations of various agent models in two established mobile benchmarks show that AgentGhost is effective and generic, with attack accuracy that reaches 99.7% on three attack objectives, and shows stealthiness with only 1% utility degradation. Furthermore, we tailor a defense method against AgentGhost that reduces the attack accuracy to 22.1%. Our code is available at anonymous.

Hidden Ghost Hand: Unveiling Backdoor Vulnerabilities in MLLM-Powered Mobile GUI Agents

Recent efforts in LLM alignment have focused on constructing large-scale preference datasets via human or Artificial Intelligence(AI) annotators. However, such approaches rely on instance-wise supervision, incurring substantial annotation cost and limited interpretability. In this paper, we propose **ZEBRA**—a model behavior-wise zero-annotation framework that constructs preference data by leveraging model behavior knowledge derived from benchmark performances. ZEBRA binarizes response pairs by evaluating the quality and similarity of their origin models, entirely bypassing instance-level annotation. This allows scalable, controllable, and cost-effective alignment data generation. Empirical results show that ZEBRA achieves alignment performance comparable to instance-supervised methods, despite requiring no manual or model-based labeling.

ZEBRA: Leveraging Model-Behavioral Knowledge for Zero-Annotation Preference Dataset Construction

Currently, leveraging large language models (LLMs) for autism intervention is a significant yet challenging task, particularly when directly employing LLMs as an intervention doctor. Researchers have mainly focused on using prompt engineering for role play as an intervention doctor and integrating auxiliary elements such as visual stimuli to enhance the sensory experience of the intervention, while neglecting the challenge that LLMs' inherent dialogue style and intervention strategies do not meet the requirements of clinical dialogue interventions. To fill the gap, we propose a comprehensive framework for training LLMs to conduct dialogue interventions in accordance with the principles of Applied Behavior Analysis (ABA) which is commonly used by clinicians. Specifically, we collected real clinical recordings of dialogue interventions for autistic children and constructed the topic dialogue dataset ASD-iLLM-8k. By incorporating the system prompt based on the ABA and ASD-iLLM-8k dataset, we fine-tuned LLMs to develop ASD-iLLM. We also proposed a role-play strategy in which LLMs act as autistic children to comprehensively evaluate the doctor model’s capabilities at the dialogue level. Extensive experiments demonstrate that ASD-iLLM outperforms existing models in both automatic and human evaluation, with intervention strategies and dialogue style more closely resembling those of real clinical intervention doctors. Our dataset, model, and code will be available on our GitHub.

ASD-iLLM:An Intervention Large Language Model for Autistic Children based on Real Clinical Dialogue Intervention Dataset

Direct Preference Optimization (DPO) is a simple and efficient framework that has attracted substantial attention. However, it often struggles to meet its primary objectives---increasing the generation probability of chosen responses while reducing that of rejected responses---due to the dominant influence of rejected responses on the loss function. This imbalance leads to suboptimal performance in promoting preferred responses. In this work, we systematically analyze the limitations of DPO and existing algorithms designed to achieve the objectives stated above. To address these limitations, we propose Bounded-DPO (BDPO), a novel method that bounds the influence of rejected responses while maintaining the original optimization structure of DPO. Through theoretical analysis and empirical evaluations, we demonstrate that BDPO achieves a balanced optimization of the chosen and rejected responses, outperforming existing algorithms.

Rethinking DPO: The Role of Rejected Responses in Preference Misalignment

Retrieval-augmented generation (RAG) mitigates the hallucination problem in large language models (LLMs) and has proven effective for specific, personalized applications. However, passing private retrieved documents directly to LLMs introduces vulnerability to membership inference attacks (MIAs), which try to determine whether the target datum exists in the private external database or not. Based on the insight that MIA queries typically exhibit high similarity to only one target document, we introduce Mirabel, a similarity-based MIA detection framework designed for the RAG system. With the proposed Mirabel, we show that simple detect-and-hide strategies can successfully obfuscate attackers, maintain data utility, and remain system-agnostic. We experimentally prove its detection and defense against various state-of-the-art MIA methods and its adaptability to existing private RAG systems.

Safeguarding Privacy of Retrieval Data against Membership Inference Attacks: Is This Query Too Close to Home?

Current causal discovery methods using Large Language Models (LLMs) often rely on pairwise or iterative strategies, which fail to capture global dependencies, amplify local biases, and reduce overall accuracy. This work introduces a unified framework for one-step full causal graph discovery through: (1) \textbf{Prompt-based discovery} with in-context learning when node metadata is available, and (2) \textbf{Causal_llm}, a data-driven method for settings without metadata. Empirical results demonstrate that the prompt-based approach outperforms state-of-the-art models (GranDAG, GES, ICA-LiNGAM) by approximately 40% in edge accuracy on datasets like Asia and Sachs, while maintaining strong performance on more complex graphs (ALARM, HEPAR2). Causal_llm consistently excels across all benchmarks, achieving 50% faster inference than reinforcement learning-based methods and improving precision by 25% in fairness-sensitive domains such as legal decision-making. We also introduce two domain-specific DAGs—one for bias propagation and another for legal reasoning under the Bhartiya Nyaya Sanhita—demonstrating LLMs' capability for systemic, real-world causal discovery.

Causal-LLM: A Unified One-Shot Framework for Prompt- and Data-Driven Causal Graph Discovery

Large Language Models (LLMs) have emerged as a transformative force in artificial intelligence, demonstrating exceptional proficiency across various tasks. However, their deployment in resource-constrained environments and concerns over user data privacy pose significant challenges. In contrast, Small Language Models (SLMs) offer computational efficiency but often lag in performance. To address these issues, we propose FedCoT, a federated framework designed for the Chain-of-Thought (CoT) distillation of knowledge from LLMs to SLMs, while ensuring the preservation of clients' data privacy. FedCoT ensures secure and efficient knowledge transfer from an LLM on a high-powered server to an SLM on a resource-constrained client, while adhering to privacy requirements. Leveraging perturbed prompts and rationales generated through the CoT approach, the framework enhances the performance of the client's SLM without compromising user data privacy within a multi-task learning framework. We propose two privacy protection strategies: the Exponential Mechanism Strategy and the Adaptive Exponential Mechanism Strategy, which balance user prompt privacy and the usability of rationales. Empirical evaluation on various text generation tasks demonstrates the effectiveness of FedCoT in training task-specific SLMs with enhanced performance while prioritizing data privacy protection.

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Forget for Get: A Lightweight Two-phase Gradient Method for Knowledge Editing in Large Language Models

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