China

Since their emergence, large language models (LLMs) have rapidly advanced, exerting substantial influence across various domains. Consequently, the importance of model editing techniques, aimed at locally correcting outdated or incorrect knowledge within language models, has grown significantly. However, traditional model editing methods face limitations: they cannot guarantee that highly related knowledge will transfer to the post-edited model, and they often rely on external knowledge bases to address this issue. In this paper, we propose a novel approach that leverages the internal knowledge of the language model to overcome the shortcomings of existing methods. First, we explore how to recall indirect associated knowledge from the model itself, which can be utilized in the editing process. Building on this, we propose MAKE (Memory-Associated Knowledge Editing), an editing method that takes into account the transfer of associated knowledge. As a result, MAKE successfully updates associated knowledge and achieves state-of-the-art performance in experiments conducted on the zsRE+, CounterFact+ and MQuAKE datasets.

EMNLP 2025

MAKE: Memory-Associated Knowledge Editing

make

knowledge editing

model editing

## 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.
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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.

technical paper

## 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." 

*Message from the General Chair, Dirk Hovy*

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

The rapid advancement of Large Language Models (LLMs) has revolutionized the generation of emotional support conversations (ESC), offering scalable solutions with reduced costs and enhanced data privacy. This paper explores the role of personas in the creation of ESC by LLMs. Our research utilizes established psychological frameworks to measure and infuse persona traits into LLMs, which then generate dialogues in the emotional support scenario. We conduct extensive evaluations to understand the stability of persona traits in dialogues, examining shifts in traits post-generation and their impact on dialogue quality and strategy distribution. Experimental results reveal several notable findings: 1) LLMs can infer core persona traits, 2) subtle shifts in emotionality and extraversion occur, influencing the dialogue dynamics, and 3) the application of persona traits modifies the distribution of emotional support strategies, enhancing the relevance and empathetic quality of the responses. These findings highlight the potential of persona-driven LLMs in crafting more personalized, empathetic, and effective emotional support dialogues, which has significant implications for the future design of AI-driven emotional support systems.

From Personas to Talks: Revisiting the Impact of Personas on LLM-Synthesized Emotional Support Conversations

Query generation is a critical task for web search engines (e.g. Google, Bing) and recommendation systems. Recently, state-of-the-art query generation methods leverage Large Language Models (LLMs) for their strong capabilities in context understanding and text generation. However, they still face challenges in generating high-quality queries in terms of inferring user intent based on their web search interaction history. In this paper, we propose Token-level Proximal Policy Optimization (TPPO), a noval approach designed to empower LLMs perform better in query generation through fine-tuning. TPPO is based on the Reinforcement Learning from AI Feedback (RLAIF) paradigm, consisting of a token-level reward model and a token-level proximal policy optimization module to address the sparse reward challenge in traditional RLAIF frameworks. We conducted experiments on both open-source dataset and an industrial dataset that was collected from a globally-used search engine, demonstrating that TPPO significantly improves the performance of query generation for LLMs and outperforms its existing competitors.

Token-level Proximal Policy Optimization for Query Generation

While Vision Language Models (VLMs) are trained to learn conceptual representations (generalized knowledge across many instances), they are typically used to analyze individual instances. When evaluation instances are atypical, this paradigm results in tension between two priors in the model. The first is a pragmatic prior that the textual and visual input are both relevant, arising from VLM finetuning on congruent inputs; the second is a semantic prior that the conceptual representation is generally true for instances of the category. In order to understand how VLMs trade-off these priors, we introduce a new evaluation dataset, VISaGE, consisting of both typical and exceptional images. In carefully balanced experiments, we show that VLMs are typically dominated by the semantic prior, which arises from the language modality, when answering queries about instances. In contrast, conceptual understanding degrades when the assumption of congruency underlying the pragmatic prior is violated with incongruent images.

VISaGE: Understanding Visual Generics and Exceptions

Although transformers are widely used for various code-specific tasks, they have some significant limitations. In this paper, we investigate State Space Models (SSMs) as a potential alternative to transformers for code understanding tasks, such as code retrieval, classification, and clone detection. Previous research has already demonstrated that SSMs are more compute-efficient than transformers. In our work, we show that SSMs are also more sample-efficient and can effectively extrapolate to longer contexts (beyond the pretraining context) during fine-tuning. Through comprehensive experiments, we demonstrate that SSMs could serve as a viable alternative to transformers for code understanding tasks, while addressing some of the major limitations associated with transformers.

CodeSSM: Towards State Space Models for Code Understanding

Reasoning models have demonstrated remarkable performance on complex tasks by generating long reasoning traces prior to producing final answers. However, previous research on long-context scaling in language models has generally focused on managing lengthy input prompts instead of producing long outputs. To leverage the strong long context understanding abilities of current models, we introduce Understanding-to-Reasoning Transition (URT) fine-tuning, a sequence-level curriculum learning framework that gradually shifts a model’s focus from interpreting long chain-of-thoughts to generating them. By incorporating partial reasoning steps in the input context, URT naturally exposes the model to diverse prompt lengths during training, preserving its performance on long-context comprehension while developing advanced reasoning capabilities. Experiments on rigorous reasoning benchmarks, including AIME24 and GPQA Diamond, reveal that our approach surpasses standard fine-tuning by over 10%, while maintaining robust performance on the understanding tasks in RULER.

Long Chain-of-Thought Fine-tuning via Understanding-to-Reasoning Transition

Standard Byte-Pair Encoding (BPE) tokeniza- tion compresses text by pairing a learned token vocabulary with a detailed merge list. Recent work has shown that this merge list exposes a potential attack surface for extracting informa- tion about language model’s training data. In this paper, we explore the downstream impact of BPE inference algorithms that do not rely on this merge list at all, and hence differ from the encoding process during the BPE training. To address this question, we investigate two broad classes of BPE inference schemes that differ from BPE appliction during training: a) targetted deviation from merge-lists including random merge orders, and various corruptions of merge list involving deletion/truncation, and b) non-targetted BPE inference algorithms that do not depend on the merge list but focus on compressing the text either greedily or exactly. Extensive experiments across diverse language modeling tasks like accuracy-based QA bench- marks, machine translation, and open-ended generation reveal that while the targetted devi- ation from the merge lists exhibit significant degradation in language model performance, the non-targetted merge-list free inference algo- rithms result in minimal impact on downstream performance that is often much smaller than expected. These findings pave way for simpler and potentially more privacy-preserving tok- enization schemes that do not catastrophically compromise model performance.

Train It and Forget It: Merge Lists are Unnecessary for BPE Inference in Language Models

Growing literature explores toxicity and polarization in discourse, with comparatively less work on characterizing what makes dialogue prosocial and constructive. We explore conversational discourse and investigate a method for characterizing its quality built upon the notion of ``responsivity''---whether one person's conversational turn is responding to a preceding turn. We develop and evaluate methods for quantifying responsivity---first through semantic similarity of speaker turns, and second by leveraging state-of-the-art large language models (LLMs) to identify the relation between two speaker turns. We evaluate both methods against a ground truth set of human-annotated conversations. Furthermore, selecting the better performing LLM-based approach, we characterize the nature of the response---whether it responded to that preceding turn in a substantive way or not. We view these responsivity links as a fundamental aspect of dialogue but note that conversations can exhibit significantly different responsivity structures. Accordingly, we then develop conversation-level derived metrics to address various aspects of conversational discourse. We use these derived metrics to explore other conversations and show that they support meaningful characterizations and differentiations across a diverse collection of conversations.

Computational Analysis of Conversation Dynamics through Participant Responsivity

Word-level quality estimation (QE) methods aim to detect erroneous spans in machine translations, which can direct and facilitate human post-editing.
While the accuracy of word-level QE systems has been assessed extensively, their usability and downstream influence on the speed, quality and editing choices of human post-editing remain understudied. In this study, we investigate the impact of word-level QE on machine translation (MT) post-editing in a realistic setting involving 42 professional post-editors across two translation directions. We compare four error-span highlight modalities, including supervised and uncertainty-based word-level QE methods, for identifying potential errors in the outputs of a state-of-the-art neural MT model. Post-editing effort and productivity are estimated from behavioral logs, while quality improvements are assessed by word- and segment-level human annotation. We find that domain, language and editors' speed are critical factors in determining highlights' effectiveness, with modest differences between human-made and automated QE highlights underlining a gap between accuracy and usability in professional workflows.

QE4PE: Word-level Quality Estimation for Human Post-Editing

Recent diffusion models have demonstrated remarkable capabilities in text-to-image generation.
However, their stochastic denoising process often causes semantic image editing (SIE) models to misapply textual instructions.
That is, models often leave the source object unchanged or erroneously alter the background.
We refer to this challenge as source object grounding.
To address this challenge, we introduce R-SIE, a region-wise SIE framework.
During the inference, R-SIE models noise separately for distinct image regions, enabling precise control over the transformed areas.
To reinforce the inference, we devise an automatic pipeline leveraging bounding boxes to generate unambiguous training data.
Additionally, we propose two region-focused metrics, CLIP-Region Class (CLIP-RC) and CLIP-Global Context (CLIP-GC), to independently assess how well the source object is edited and the background is preserved, respectively.
Experimental results demonstrate that region-wise diffusion improves existing baselines, and our data generation pipeline further enhances these improvements.

Overcoming Source Object Grounding for Semantic Image Editing.

We introduce MultiBLiMP 1.0, a massively multilingual benchmark of linguistic minimal pairs, covering 101 languages and 2 types of subject-verb agreement, containing more than 128,000 minimal pairs. Our minimal pairs are created using a fully automated pipeline, leveraging the large-scale linguistic resources of Universal Dependencies and UniMorph. MultiBLiMP 1.0 evaluates abilities of LLMs at an unprecedented multilingual scale, and highlights the shortcomings of the current state-of-the-art in modelling low-resource languages.

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Next from EMNLP 2025

From Personas to Talks: Revisiting the Impact of Personas on LLM-Synthesized Emotional Support Conversations

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