China

Effective human communication in social settings is contingent on recognizing subtle cues, such as intents or implications. Without such cues, NLP models risk missing social signals, instead relying on surface patterns. We introduce SOCIAL SCAFFOLDS, an automated framework for facilitating generalization across social reasoning tasks by generating rationales that make these social cues explicit. Grounded in narrative modeling principles, we generate task-agnostic rationales that capture different perspectives, i.e., that of the speaker, the listener, and the general world-view. Our experimental suite showcases that providing rationales as augmentations aids task performance for both supervised fine-tuning and in-context learning paradigms. Notably, providing all three rationale types significantly improves cross-task performance in 44% of cases, and inferred speaker intent in 31.3% of cases. We conduct statistical and ablation analyses that show how rationales complement the input text and are used effectively by models.

EMNLP 2025

SOCIAL SCAFFOLDS: A Generalization Framework for Social Understanding Tasks

rationales

dialogue understanding

generalizability

### Welcome!
The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. Our Virtual Poster Sessions will take place online Thursday, August 22, 2024.

You are required to register for this event. **Please register [here](https://2024.aclweb.org/registration). **

If you have already registered, please check your inbox for an email from Underline granting you access to ACL 2024 content.

Please register!

ACL 2024

The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

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.

Model merging enables powerful capabilities in neural networks without requiring additional training. In this paper, we introduce a novel perspective on model merging by leveraging the fundamental mechanisms of neural network representation. Our approach is motivated by the linear representation hypothesis, which states that neural networks encode information through linear combinations of feature vectors. We propose a method that superposes task-specific features from individual models into a merged model. Our approach specifically targets linear transformation matrices, which are crucial for feature activation and extraction in deep networks. By formulating the merging process as a linear system, we can preserve output feature directions from individual models and create merged models that effectively maintain multi-task capabilities compared to existing methods. Extensive experiments across diverse benchmarks and models demonstrate that our method outperforms existing techniques.

Superpose Task-specific Features for Model Merging

Language Model (LM)-based Text-to-Speech (TTS) systems often generate hallucinated speech that deviates from input text. Existing mitigation strategies either demand excessive training resources or introduce significant inference latency. In this paper, we propose GFlOwNet-guided distribution AlignmenT (GOAT) for LM-based TTS, a post-training framework that mitigates hallucinations without relying on massive resources or inference cost. Specifically, we first conduct an uncertainty analysis, revealing a strong positive correlation between hallucination and model uncertainty. Based on this, we reformulate TTS generation as a trajectory flow optimization problem and introduce an enhanced Subtrajectory Balance objective together with a sharpened internal reward as target distribution. We further integrate reward temperature decay and learning rate optimization for stability and performance balance. Extensive experiments show that GOAT reduce over 50\% character error rates on challenging test cases and lowering uncertainty by up to 58\%, demonstrating its strong generalization ability and effectiveness.

Mitigating Hallucinations in LM-Based TTS Models via Distribution Alignment Using GFlowNets

Mathematical reasoning has been challenging for large language models (LLMs). However, the introduction of step-by-step Chain-of-Thought (CoT) inference has significantly advanced the mathematical capabilities of LLMs. Despite this progress, current approaches either necessitate extensive inference datasets for training or depend on few-shot methods that frequently compromise computational accuracy. To address these fundamental limitations, we propose Step Guided Reasoning (SGR), a novel training-free adaptation framework that efficiently equips general-purpose pre-trained language models, such as Qwen2-72B-Instruct, with enhanced mathematical reasoning capabilities. In this approach, LLMs reflect on small reasoning steps, similar to how humans deliberate and focus attention on what to do next. By incorporating this reflective process into the inference stage, LLMs can effectively guide their reasoning from one step to the next. Through extensive experiments, we demonstrate the significant effect of \ourmethod in enhancing mathematical performance in state-of-the-art language models. Qwen2-72B-Instruct outperforms its math-specific counterpart, Qwen2.5-72B-Math-Instruct, on MMLU-STEM with a score of 90.9%, compared to 87.3%. The average scores of Qwen2-7B-Instruct and Qwen2-72B-Instruct increase from 27. 1% to 36. 3% and from 36. 5% to 47. 4% in the math domain, respectively.

Step Guided Reasoning: Improving Mathematical Reasoning using Guidance Generation and Step Reasoning

The widespread adoption of large language models (LLMs) necessitates reliable methods to detect LLM-generated text. We introduce SimMark, a robust sentence-level watermarking algorithm that makes LLMs' outputs traceable without requiring access to model internals, making it compatible with both open and API-based LLMs. By leveraging the similarity of semantic sentence embeddings combined with rejection sampling to embed detectable statistical patterns imperceptible to humans, and employing a soft counting mechanism, SimMark achieves robustness against paraphrasing attacks. Experimental results demonstrate that SimMark sets a new benchmark for robust watermarking of LLM-generated content, surpassing prior sentence-level watermarking techniques in robustness, sampling efficiency, and applicability across diverse domains, all while maintaining the text quality and fluency.

SimMark: A Robust Sentence-Level Similarity-Based Watermarking Algorithm for Large Language Models

Clinical trials are critical for advancing medical treatments but remain prohibitively expensive and time-consuming. Accurate prediction of clinical trial outcomes can significantly reduce research and development costs and accelerate drug discovery. While recent deep learning models have shown promise by leveraging unstructured data, their black-box nature, lack of interpretability, and vulnerability to label leakage limit their practical use in high-stakes biomedical contexts. In this work, we propose AutoCT, a novel framework that combines the reasoning capabilities of large language models with the explainability of classical machine learning. AutoCT autonomously generates, evaluates, and refines tabular features based on public information without human input. Our method uses Monte Carlo Tree Search to iteratively optimize predictive performance. Experimental results show that AutoCT performs on par with or better than SOTA methods on clinical trial prediction tasks within only a limited number of self-refinement iterations, establishing a new paradigm for scalable, interpretable, and cost-efficient clinical trial prediction.

AutoCT: Automating Interpretable Clinical Trial Prediction with LLM Agents

Which components in transformer language models are responsible for discourse understanding? We hypothesize that sparse computational graphs, termed as *discursive circuits* control how models process discourse relations. Unlike simpler tasks, discourse relations involve longer spans and complex reasoning. To make circuit discovery feasible, we introduce a task called Completion under Discourse Relation (CuDR), where a model completes a discourse given a specified relation. To support this task, we construct a corpus of minimal contrastive pairs tailored for activation patching in circuit discovery. Experiments show that sparse circuits (approx 0.2\% of a full GPT-2 model) recover discourse understanding in the English PDTB-based CuDR task. These circuits generalize well to unseen discourse frameworks such as RST and SDRT. Further analysis shows lower layers capture linguistic features such as lexical semantics and coreference, while upper layers encode discourse-level abstractions. Feature utility is consistent across frameworks (e.g., coreference supports Expansion-like relations).

Discursive Circuits: How Do Language Models Understand Discourse Relations?

Conversational agents powered by large language models (LLMs) are rapidly becoming integral to our daily interactions, generating unprecedented amounts of conversational data. Such datasets offer a powerful lens into societal interests, trending topics, and collective concerns. Yet existing approaches typically treat these interactions as independent, missing critical insights that could emerge from aggregating and reasoning across large-scale conversation logs. In this paper, we introduce Aggregative Question Answering, a novel task requiring models to reason explicitly over thousands of user-chatbot interactions to answer aggregational queries, such as identifying emerging concerns among specific demographics. To enable research in this direction, we construct a benchmark, WildChat-AQA, comprising 6,027 aggregative questions derived from 182,330 real-world chatbot conversations. Experiments show that existing methods either struggle to reason effectively or incur prohibitive computational costs, underscoring the need for new approaches capable of extracting collective insights from large-scale conversational data.

From Chat Logs to Collective Insights: Aggregative Question Answering

Large language models (LLMs) are transforming education by answering questions, explaining complex concepts, and generating content across a wide range of subjects. However, despite strong performance on academic benchmarks, they often fail to adapt responses to students’ grade levels. This is a critical need in K–12 education, where age-appropriate vocabulary and explanation are essential for effective learning. Existing models frequently produce outputs that are too advanced or vague for younger learners, and there are no standardized benchmarks to evaluate their ability to adjust across cognitive and developmental stages. To address this gap, we introduce a benchmark of nearly 48k grade-labeled QA pairs across 9 science subjects, spanning Grades 1–12 and grouped into four grade levels. We evaluate a diverse set of open-source LLMs and find that while larger models generally perform better, they still struggle with generating suitable responses for early-grade students (Grades 1–5). Our work presents the first dataset and evaluation framework for assessing grade-level adaptability in LLMs, aiming to foster more developmentally aligned educational AI systems through better training and prompting strategies. EduAdapt's code and datasets are open-sourced and publicly available at [URL redacted].

EduAdapt: A Question Answer Benchmark Dataset for Evaluating Grade-Level Adaptability in LLMs

Current coreference resolution systems are typically tailored for short- or medium-sized texts and struggle to scale to very long documents due to design choices and the implied memory costs. However, a few available solutions overcome this problem by splitting documents into smaller windows. This is inherently similar to what happens in the cross-document setting, in which systems infer coreference relations between mentions that are found in separate documents. In this paper, we unify these two challenging settings under the general framework of cross-context coreference, and introduce xCoRe, a novel unified formulation designed to efficiently handle short-, long-, and cross-document coreference resolution. xCoRe adopts a three-step pipeline that, first, identifies mentions, then creates clusters within individual contexts, and finally merges clusters across contexts. In our experiments, we show that our approach enables joint training on shared long- and cross-context resources, increasing data availability and particularly benefiting the low-resource cross-document task. Our model achieves new state-of-the-art scores on both cross- and long-document benchmarks, maintaining strong performance on traditional benchmarks and positioning it as an optimal, versatile solution that can be applied across all coreference settings. We release our models and code at omitted.link.

xCoRe: Cross-context Coreference Resolution

Large Language Models (LLMs) excel at countless tasks, yet struggle with creativity. In this paper, we introduce a novel approach that couples LLMs with structured representations to generate more creative and diverse ideas. Our notion of creativity goes beyond mere token-level variability; rather, we apply cognitively inspired manipulations to the structured representations, allowing our algorithm to effectively explore the more abstract landscape of ideas. We demonstrate our approach in the culinary domain with DishCOVER, a model that recombines recipes to generate creative ones. Experiments comparing our model’s results to those of GPT-4o show greater diversity. Domain expert evaluation reveals that our outputs, which are mostly appropriate, significantly surpass GPT-4o in terms of novelty, thus outperforming it in creative generation. We hope our work inspires further research into structured creativity in AI.

Downloads

Next from EMNLP 2025

Superpose Task-specific Features for Model Merging

Similar lecture

Leveraging Machine-Generated Rationales to Facilitate Social Meaning Detection in Conversations