China

Large language models (LLMs) have demonstrated impressive capabilities in reasoning with the emergence of reasoning models like OpenAI-o1 and DeepSeek-R1. Recent research focuses on integrating reasoning capabilities into the realm of retrieval-augmented generation (RAG) via outcome-supervised reinforcement learning (RL) approaches, while the correctness of intermediate think-and-search steps is usually neglected. To address this issue, we design a process-level reward module to mitigate the unawareness of intermediate reasoning steps in outcome-level supervision without additional annotation. Grounded on this, we propose **Le**arning to **T**hink-and-**S**earch (**LeTS**), a novel framework that hybridizes stepwise process reward and outcome-based reward to current RL methods for RAG. Extensive experiments demonstrate the generalization and inference efficiency of **LeTS** across various RAG benchmarks. In addition, these results reveal the potential of process- and outcome-level reward hybridization in boosting LLMs&#39; reasoning ability via RL under other scenarios.

EMNLP 2025

LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization

retrieval-augmented generation

reinforcement learning

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

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AAAI 2026

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

Large language models (LLMs) have demonstrated impressive capabilities in reasoning with the emergence of reasoning models like OpenAI-o1 and DeepSeek-R1. Recent research focuses on integrating reasoning capabilities into the realm of retrieval-augmented generation (RAG) via outcome-supervised reinforcement learning (RL) approaches, while the correctness of intermediate think-and-search steps is usually neglected. To address this issue, we design a process-level reward module to mitigate the unawareness of intermediate reasoning steps in outcome-level supervision without additional annotation. Grounded on this, we propose **Le**arning to **T**hink-and-**S**earch (**LeTS**), a novel framework that hybridizes stepwise process reward and outcome-based reward to current RL methods for RAG. Extensive experiments demonstrate the generalization and inference efficiency of **LeTS** across various RAG benchmarks. In addition, these results reveal the potential of process- and outcome-level reward hybridization in boosting LLMs' reasoning ability via RL under other scenarios.

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.

Large language models demonstrate strong problem-solving abilities through reasoning techniques such as chain-of-thought prompting and reflection. However, it remains unclear whether these reasoning capabilities extend to a form of social intelligence: making effective decisions in cooperative contexts. We examine this question using economic games that simulate social dilemmas. First, we apply chain-of-thought and reflection prompting to GPT-4o in a Public Goods Game. We then evaluate multiple off-the-shelf models across six cooperation and punishment games, comparing those with and without explicit reasoning mechanisms. We find that reasoning models consistently reduce cooperation and norm enforcement, favoring individual rationality. In repeated interactions, groups with more reasoning agents exhibit lower collective gains. These behaviors mirror human patterns of "spontaneous giving and calculated greed." Our findings underscore the need for LLM architectures that incorporate social intelligence alongside reasoning, to help address—rather than reinforce—the challenges of collective action.

Spontaneous Giving and Calculated Greed in Language Models

The rapid development of large language models (LLMs) gives rise to ethical concerns about their performance, while opening new avenues for developing toxic language detection techniques. However, LLMs’ unethical output and their capability of detecting toxicity have primarily been tested on language data that do not demand complex meaning inference, such as the biased associations of ‘he’ with programmer and ‘she’ with household. Nowadays toxic language adopts a much more creative range of implicit forms, thanks to advanced censorship. In this study, we collect authentic toxic interactions that evade online censorship and that are verified by human annotators as inference-intensive. To evaluate and improve LLMs' reasoning of the authentic implicit toxic language, we propose a new prompting method, Pragmatic Inference Chain (PIC), drawn on interdisciplinary findings from cognitive science and linguistics. The PIC prompting significantly improves the success rate of GPT-4o, Llama-3.1-70B-Instruct, DeepSeek-v2.5, and DeepSeek-v3 in identifying implicit toxic language, compared to five baseline prompts, such as CoT and rule-based baselines. In addition, it also facilitates the models to produce more explicit and coherent reasoning processes, hence can potentially be generalized to other inference-intensive tasks, e.g., understanding humour and metaphors.

Pragmatic Inference Chain (PIC) Improving LLMs' Reasoning of Authentic Implicit Toxic Language

Stance detection aims to identify the viewpoint (favor, against, or neutral) expressed in text towards a specific target. Recent studies on zero-shot and few-shot stance detection focus primarily on learning generalized representations from explicit targets. However, these methods often neglect **implicit yet semantically important targets** and fail to sufficiently **exploit fine-grained contextual cues**, limiting model performance in nuanced scenarios. To overcome these limitations, we propose a novel two-stage framework: First, a data augmentation framework named **Hierarchical Collaborative Target Augmentation** (HCTA) employs Large Language Models (LLMs) to identify and annotate implicit targets via Chain-of-Thought (CoT) prompting and multi-model voting, significantly enriching training data with latent semantic relations. Second, we introduce **FiCAN, a Fine-grained Context-aware Attention Network**, integrating a joint text-target encoding and a sparse cross-attention mechanism to selectively capture critical fine-grained contextual clues. Experiments on the VAST benchmark dataset demonstrate that our approach achieves state-of-the-art results, confirming the effectiveness of implicit target augmentation and fine-grained contextual modeling.

LLM-Driven Implicit Target Augmentation and Fine-Grained Contextual Modeling for Zero-Shot and Few-Shot Stance Detection

Discovering customer intentions in dialogue conversations is crucial for automated service agents. However, existing intent clustering methods often fail to align with human perceptions due to a heavy reliance on embedding distance metrics and a tendency to overlook underlying semantic structures. This paper proposes an **LLM-in-the-loop (LLM-ITL)** intent clustering framework, integrating the semantic understanding capabilities of LLMs into conventional clustering algorithms. Specifically, this paper (1) investigates the effectiveness of fine-tuned LLMs in semantic coherence evaluation and intent cluster naming, achieving over 95\% accuracy aligned with human judgments; (2) designs an LLM-ITL framework that facilitates the iterative discovery of coherent intent clusters and the optimal number of clusters; and (3) proposes context-aware techniques tailored for customer service dialogue. As existing English benchmarks offer limited semantic diversity and intent groups, we introduce a comprehensive Chinese dialogue intent dataset, comprising over 100k real customer service calls and 1,507 human-annotated intent clusters. The proposed approaches significantly outperform LLM-guided baselines, achieving notable enhancements in clustering quality and lower computational cost. Combined with several best practices, our findings highlight the potential of LLM-in-the-loop techniques for scalable and human-aligned intent clustering.

Dial-In LLM: Human-Aligned LLM-in-the-loop Intent Clustering for Customer Service Dialogues

The empathy dialogue system requires understanding emotions and their underlying causes. However, existing datasets mainly focus on emotion labels, while cause annotations are added post hoc through costly and subjective manual processes. This leads to three limitations: subjective bias in cause labels, weak rationality due to ambiguous cause-emotion relationships, and high annotation costs that hinder scalability. To address these challenges, we propose ECC (Emotion-Cause Conversation Dataset), a scalable dataset with 2.4K dialogues, which is also the first dialogue dataset where conversations and their emotion-cause labels are automatically generated synergistically during creation. We create an automatic extension framework EC-DD for ECC that utilizes knowledge and large language models (LLMs) to automatically generate conversations, and train a causality-aware empathetic response model CAER on this dataset. Experimental results show that ECC can achieve comparable or even superior performance to artificially constructed empathy dialogue datasets. All code, data, and models will be publicly released to advance empathetic AI research.

ECC: An Emotion-Cause Conversation Dataset for Empathy Response

The emergence of Multimodal Large Reasoning Models (MLRMs) has enabled sophisticated visual reasoning capabilities by integrating reinforcement learning and Chain-of-Thought (CoT) supervision. However, while these enhanced reasoning capabilities improve performance, they also introduce new and underexplored safety risks. In this work, we systematically investigate the security implications of advanced visual reasoning in MLRMs. Our analysis reveals a fundamental trade-off: as visual reasoning improves, models become more vulnerable to jailbreak attacks. Motivated by this critical finding, we introduce VisCRA (Visual Chain Reasoning Attack), a novel jailbreak framework that exploits the visual reasoning chains to bypass safety mechanisms. VisCRA combines targeted visual attention masking with a two-stage reasoning induction strategy to precisely control harmful outputs. Extensive experiments demonstrate VisCRA's significant effectiveness, achieving high attack success rates on leading closed-source MLRMs: 76.48% on Gemini 2.0 Flash Thinking, 68.56% on QvQ-Max, and 56.60% on GPT-4o. Our findings highlight a critical insight: the very capability that empowers MLRMs — their visual reasoning — can also serve as an attack vector, posing significant security risks. Warning: This paper contains unsafe examples.

VisCRA: A Visual Chain Reasoning Attack for Jailbreaking Multimodal Large Language Models

This paper investigates how LLMs encode inputs with typos. We hypothesize that specific neurons and attention heads recognize typos and fix them internally using local and global contexts. We introduce a method to identify typo neurons and typo heads that work actively when inputs contain typos. Our experimental results suggest the following: 1) LLMs can fix typos with local contexts when the typo neurons in either the early or late layers are activated, even if those in the other are not. 2) Typo neurons in the middle layers are the core of typo-fixing with global contexts. 3) Typo heads fix typos by widely considering the context not focusing on specific tokens. 4) Typo neurons and typo heads work not only for typo-fixing but also for understanding general contexts.

Investigating Neurons and Heads in Transformer-based LLMs for Typographical Errors

We propose new static word embeddings optimised for sentence semantic representation. We first extract word embeddings from a pre-trained Sentence Transformer, and improve them with sentence-level principal component analysis, followed by either knowledge distillation or contrastive learning. During inference, we represent sentences by simply averaging word embeddings, which requires little computational cost. We evaluate models on both monolingual and cross-lingual tasks and show that our model substantially outperforms existing static models on sentence semantic tasks, and even rivals a basic Sentence Transformer model (SimCSE) on some data sets. Lastly, we perform a variety of analyses and show that our method successfully removes word embedding components that are irrelevant to sentence semantics, and adjusts the vector norms based on the influence of words on sentence semantics.

Static Word Embeddings for Sentence Semantic Representation

Multimodal Large Language Models (MLLMs) are increasingly used in Personalized Image Aesthetic Assessment (PIAA), offering a scalable alternative to expert evaluation. However, their outputs may reflect subtle biases shaped by demographic cues such as gender, age, or education. In this work, we introduce **AesBiasBench**, a benchmark designed to evaluate MLLMs along two complementary axes: (1) the presence of *stereotype bias*, measured by how aesthetic evaluations vary across demographic groups; and (2) the *alignment* between model outputs and real human aesthetic preferences. Our benchmark spans three subtasks, Aesthetic Perception, Assessment, and Empathy, and introduces structured metrics (IFD, NRD, AAS) to quantify both bias and alignment. We evaluate 19 MLLMs, including proprietary models (e.g., GPT-4o, Claude-3.5-Sonnet) and open-source models (e.g., InternVL-2.5, Qwen2.5-VL). Results show that smaller models exhibit stronger stereotype bias, while larger models better align with human preferences. Adding identity information often amplifies bias, particularly in emotional judgment. These findings highlight the need for identity-aware evaluation frameworks for subjective vision-language tasks.

AesBiasBench: Evaluating Bias and Alignment in Multimodal Language Models for Personalized Image Aesthetic Assessment

Large Language Models (LLMs) often don't perform as expected under Domain Shift or after Instruct-tuning. A reliable indicator of LLM performance in these settings could assist in decision-making. We present a method that uses the known performance in high-resource domains and fine-tuning settings to predict performance in low-resource domains or base models respectively. In our paper, we formulate the task of performance prediction, construct a dataset for and train regression models to predict the said change in performance. Our proposed methodology is lightweight and, in practice, can help researchers & practitioners decide if resources should be allocated for data labeling and LLM Instruct-tuning.

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Spontaneous Giving and Calculated Greed in Language Models

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