China

Cross-lingual consistency should be considered to assess cross-lingual transferability, maintain the factuality of the model knowledge across languages, and preserve the parity of language model performance. We are thus interested in analyzing, evaluating, and interpreting cross-lingual consistency for factual knowledge. We examine code-mixed coreferential statements conveyed identical knowledge across languages to study cross-lingual knowledge consistency. We use some interpretability approaches to analyze the behavior of a model in cross-lingual contexts, discovering that multilingual models show different levels of consistency, subject to language families, linguistic factors, and a bottleneck in cross-lingual consistency on a particular layer. In addition, we evaluate common strategies aimed at improving multilingual performance to observe whether these strategies can improve knowledge consistency at the same time. While knowledge is not cross-lingual consistent in many cases, code-switching training and cross-lingual word alignment objectives show the most promising results, emphasizing the noteworthiness of cross-lingual alignment supervision and code-switching training for both multilingual performance and cross-lingual consistency enhancement.

EMNLP 2025

Are Knowledge and Reference in Multilingual Language Models Cross-Lingually Consistent?

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.

Listwise ranking based on Large Language Models (LLMs) has achieved state-of-the-art performance in Information Retrieval (IR). However, their effectiveness often depends on LLMs with massive parameter scale (e.g., GPT-4) and computationally expensive sliding window processing, leading to substantial efficiency bottlenecks. In this paper, we propose a Collaborative Ranking framework (CoRanking) for LLM-based listwise ranking. Specifically, we strategically combine an efficient small reranker and an effective large reranker, and jointly optimize with a novel reinforcement learning method (RL). The small reranker performs initial passage ranking, effectively filtering the candidate set to a condensed top-k list (e.g., top-20 passages), and the large reranker (with stronger ranking capability) then reranks only this condensed subset rather than the full list, significantly improving efficiency. We further address that directly passing the top-ranked passages from the small reranker to the large reranker is suboptimal because of the LLM's strong positional bias in processing input sequences. To resolve this issue, we propose a passage order adjuster learned by RL that dynamically reorders the top passages returned by the small reranker to better align with the large LLM's input preferences. Our extensive experiments across three IR benchmarks demonstrate that CoRanking achieves superior efficiency, reducing ranking latency by approximately 70% while simultaneously improving effectiveness, compared to standalone large listwise reranker.

CoRanking: Collaborative Ranking with Small and Large Ranking Agents

Temporal reasoning in legal texts is important for applications like case law analysis and compliance monitoring. However, existing datasets lack expert language evaluation, leaving a gap in understanding how LLMs manage event ordering in legal contexts. We introduce LexTime, the first dataset designed to evaluate LLMs' event ordering capabilities in legal language, consisting of 512 instances from U.S. Federal Complaints with annotated event pairs and their temporal relations. Our findings show that (1) LLMs are more accurate on legal event ordering than on narrative (up to +10.5%); (2) longer input contexts and implicit events boost accuracy, reaching 80.8% for implicit-explicit event pairs; (3) legal linguistic complexities and nested clauses remain a challenge. We investigate how context length, explicit vs implicit event pairs, and legal language features affect model performance, demonstrating the need for specific modeling strategies to enhance temporal event reasoning.

LexTime: A Benchmark for Temporal Ordering of Legal Events

Media framing refers to the emphasis on specific aspects of perceived reality to shape how an issue is defined and understood. Its primary purpose is to shape public perceptions often in alignment with the authors' opinions and stances. However, the interaction between stance and media frame remains largely unexplored. In this work, we apply an interdisciplinary approach to conceptualize and computationally explore this interaction with internet memes on climate change. We curate CLIMATEMEMES, the first dataset of climate-change memes annotated with both stance and media frames, inspired by research in communication science. CLIMATEMEMES includes 1,184 memes sourced from 47 subreddits, enabling analysis of frame prominence over time and communities, and sheds light on the framing preferences of different stance holders. We propose two meme understanding tasks: stance detection and media frame detection. We evaluate LLaVA-NeXT and Molmo in various setups, and report the corresponding results on their LLM backbone. Human captions consistently enhance performance. Synthetic captions and human-corrected OCR also help occasionally. Our findings highlight that VLMs perform well on stance, but struggle on frames, where LLMs outperform VLMs. Finally, we analyze VLMs’ limitations in handling nuanced frames and stance expressions on climate change internet memes.

What Media Frames Reveal About Stance: A Dataset and Study about Memes in Climate Change Discourse

The task of Critical Questions Generation (CQs-Gen) aims to foster critical thinking by enabling systems to generate questions that expose underlying assumptions and challenge the validity of argumentative reasoning structures. Despite growing interest in this area, progress has been hindered by the lack of suitable datasets and automatic evaluation standards. This paper presents a comprehensive approach to support the development and benchmarking of systems for this task. We construct the first large-scale dataset including 5K manually annotated questions. We also investigate automatic evaluation methods and propose a reference-based technique using large language models (LLMs) as the strategy that best correlates with human judgments. Our zero-shot evaluation of 11 LLMs establishes a strong baseline while showcasing the difficulty of the task. Data and code plus a public leaderboard are provided to encourage further research not only in terms of model performance, but also to explore the practical benefits of CQs-Gen for both automated reasoning and human critical thinking.

Benchmarking Critical Questions Generation: A Challenging Reasoning Task for Large Language Models

Large Language Models (LLMs) have transformed listwise document reranking by enabling global reasoning over candidate sets, yet single models often struggle to balance fine-grained relevance scoring with holistic cross-document analysis. We propose DeepAgentRank (DeAR), an open-source framework that decouples these tasks through a dual-stage approach, achieving superior accuracy and interpretability. In Stage 1, we distill token-level relevance signals from a frozen 13B LLaMA teacher into a compact {3, 8}B student model using a hybrid of cross-entropy, RankNet, and KL divergence losses, ensuring robust pointwise scoring. In Stage 2, we attach a second LoRA adapter and fine-tune on 20K GPT-4o-generated chain-of-thought permutations, enabling listwise reasoning with natural-language justifications. Evaluated on TREC-DL19/20, eight BEIR datasets, and NovelEval-2306, DeAR surpasses open-source baselines by +5.1 nDCG@5 on DL20 and achieves 90.97 nDCG@10 on NovelEval, outperforming GPT-4 by +3.09. Without fine-tuning on Wikipedia, DeAR also excels in open-domain QA, achieving 54.29 Top-1 accuracy on Natural Questions, surpassing baselines like MonoT5, UPR, and RankGPT. Ablations confirm that dual-loss distillation ensures stable calibration, making DeAR a highly effective and interpretable solution for modern reranking systems.

DeAR: Dual-Stage Document Reranking with Reasoning Agents via LLM Distillation

State-of-the-art neural machine translation (NMT) models deliver high-quality translations at the expense of large inference latency and energy consumption, requiring vast GPU fleets and contributing significantly to carbon emissions. To democratize and ``green'' NMT, we introduce the Green KNIGHT, a hardware-agnostic collection of recipes to optimize model performance in terms of speed and energy consumption, with only a minor trade-off in quality. On two high-resource benchmarks we show up to 91× CPU speedup and 94% energy savings for En→De, and 65× speedup and 10% energy usage for En→Ko; while incurring only minor losses of 9% relative BLEU. Our results prove that efficient and environmentally conscious NMT can be realized through optimizations build on well-understood, off-the-shelf techniques with no custom low-level code required, making our approach immediately deployable in real-world translation pipelines.

The Green KNIGHT: Green Machine Translation with Knowledge-Distilled, Narrow, Inexpensive, Greedy, Hybrid Transformers

Autoregressive Transformer (AT) dominates sequence-to-sequence generation tasks but suffers from high inference latency due to sequential token generation. Non-Autoregressive Transformer (NAT) improves inference efficiency by parallelizing token prediction, yet degrades generation quality. To address these limitations, we propose Tree-structured Non-Autoregressive Decoding (TNAD), a novel paradigm that bridges autoregressive and non-autoregressive decoding. TNAD generates a sentence through a top-down, layer-wise expansion of its constituency parse tree, enabling parallel generation within each layer while preserving contextual dependencies across layers. Experimental results on machine translation and paraphrase generation demonstrate that TNAD outperforms AT in efficiency and NAT in generation quality, thus offering a new alternative to AT and NAT in the trade-off between efficiency and quality.

Tree-Structured Non-Autoregressive Decoding for Sequence-to-Sequence Text Generation

We introduce Fourier Domain Adapter (FDA), a novel and parameter-efficient framework for fine-tuning large-scale pre-trained language models. FDA reparameterizes the core projection operation of the adapter module directly in the Fourier domain. This involves transforming the input features via discrete Fourier transform (DFT), applying sparse learnable complex modulations in frequency space, and then back-transforming via inverse DFT, supplemented by highly compact auxiliary linear layers. This approach significantly reduces the number of trainable parameters while enhancing the model's ability to capture salient frequency-based semantic information. Comprehensive experiments on GLUE, E2E NLG, and instruction tuning benchmarks show that our FDA consistently outperforms existing parameter-efficient fine-tuning (PEFT) methods. It can achieve better performance with nearly 100x fewer training parameters than traditional fine-tuning methods such as LoRA and AdapterH. Our results demonstrate that FDA is a robust and efficient solution for developing efficient and powerful language models.

Towards More Efficient Post-training via Fourier Domain Adapter Framework

We propose EditID, a training-free approach based on the DiT architecture, which achieves highly editable customized IDs for text to image generation. Existing text-to-image models for customized IDs typically focus more on ID consistency while neglecting editability. It is challenging to alter facial orientation, character attributes, and other features through prompts. EditID addresses this by deconstructing the text-to-image model for customized IDs into an image generation branch and a character feature branch. The character feature branch is further decoupled into three modules: feature extraction, feature fusion, and feature integration. By introducing a combination of mapping features and shift features, along with controlling the intensity of ID feature integration, EditID achieves semantic compression of local features across network depths, forming an editable feature space. This enables the successful generation of high-quality images with editable IDs while maintaining ID consistency, achieving excellent results in the IBench evaluation, which is an editability evaluation framework for the field of customized ID text-to-image generation that quantitatively demonstrates the superior performance of EditID. EditID is the first text-to-image solution to propose customizable ID editability on the DiT architecture, meeting the demands of long prompts and high-quality image generation.

EditID: Training-Free Editable ID Customization for Text-to-Image Generation

In Embedding Based Retrieval (EBR), Approximate Nearest Neighbor (ANN) algorithms are widely adopted for efficient large-scale search. However, recent studies reveal a query out-of-distribution (OOD) issue, where query and base embeddings follow mismatched distributions, significantly degrading ANN performance. In this work, we empirically verify the generality of this phenomenon and provide a quantitative analysis. To mitigate the distributional gap, we introduce a distribution regularizer into the encoder training objective, encouraging alignment between query and base embeddings. Extensive experiments across multiple datasets, encoders, and ANN indices show that our method consistently improves retrieval performance.

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CoRanking: Collaborative Ranking with Small and Large Ranking Agents

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