Mexico

Spurred by recent advances in Large Language Models (LLMs), virtual assistants are poised to take a leap forward in terms of their dialogue capabilities. Yet a major bottleneck to achieving genuinely transformative task-oriented dialogue capabilities remains the scarcity of high quality data. Existing datasets, while impressive in scale, have limited domain coverage and contain few genuinely challenging conversational phenomena; those which are present are typically unlabelled, making it difficult to assess the strengths and weaknesses of models without time-consuming and costly human evaluation. Moreover, creating high quality dialogue data has until now required considerable human input, limiting both the scale of these datasets and the ability to rapidly bootstrap data for a new target domain. We aim to overcome these issues with LUCID, a modularised and highly automated LLM-driven data generation system that produces realistic, diverse and challenging dialogues. We use LUCID to generate a seed dataset of 4,277 conversations across 100 intents to demonstrate its capabilities, with a human review finding consistently high quality labels in the generated data.

NAACL 2024

LUCID: LLM-Generated Utterances for Complex and Interesting Dialogues

ai virtual assistant

llm data generation

task-oriented dialogue

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.

poster

## Welcome to 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics!
This year, the conference is in Mexico City. NAACL was actually already planned for Mexico City in 2021, but due to the pandemic the entire conference was moved online. This year, finally, we get to go! So it is my sincere pleasure to welcome you to Mexico City, whether in person or virtually. Having the conference in Mexico City is a good opportunity to emphasize that NAACL is our flagship conference for ACL members not only in North America but also in Central and South America, even though NAACL has been bearing “North” in its name. At this year’s conference, we have a theme to match, with a theme track on the Languages of Latin America to showcase the linguistic diversity of the region.
 
The opportunity to present at NAACL should not depend on a researcher’s travel budget, or their family status. This is why it is so important to make virtual participation at NAACL as good an experience as possible – but we want to also provide a good experience for in-person participants. As a community, we are still working out the best way to do that. This year at NAACL, we are trying out a big virtual poster session ahead of the conference, with the hope that this will make make for a lively and interactive experience. At the same time, we are reducing virtual oral presentations, which seem to be particularly tricky to make to work well. A big thanks to the NAACL program chairs and to Luciana Benotti for all their ideas and work to improve the virtual experience. And participants, virtual as well as in-person: Please let us know what worked for you and what didn’t, so we can continue to improve hybrid conferences. 
 
I have been lucky to work with many amazing people. Without their insight, dedication and patience, and without the many hours of work they put in, NAACL would not have been possible. A huge thank you to the program chairs Helena Gomez, Kevin Duh, and Steve Bethard – you are the best! 
 
Finally, I would like to thank all authors, invited speakers and panelists, area chairs and reviewers, the volunteers organizing and chairing sessions, and all attendees, in-person and virtual. Thank you for helping us make NAACL 2024 come to life. 
 
Welcome and hope you all enjoy the conference! 
Katrin Erk 
The University of Texas at Austin 
NAACL-2024 General Chair 
*You can read the full Welcome message in the [Conference Handbook (downloadable)](https://drive.google.com/file/d/1H1NvW0VASQjkSCYgw3mr-3l4yYDSxz6B/view?usp=sharing)*

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2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics

Emotion Detection (ED) is a process that aims to identify and analyze human emotions, such as happiness, anger, and sadness. Nowadays, there is a huge interest in identifying the causes behind different emotions in written text, Emotion-Cause Pair Extraction (ECPE). ECPE involves discovering triggers or motivations that lead to certain emotions. Large Language Models (LLMs) are the new era that has greatly improved Natural Language Processing (NLP) tasks. Since ECPE requires a high level of context, this proposed work aims to leverage and evaluate LLMs for emotion-cause pair extraction from text.

Leveraging and Evaluating Large Language Models for Emotion-Cause Pair Extraction

We create an adaptive conversational agent that assesses the quality of its knowledge and is driven to become more knowledgeable. Unlike agents with predefined tasks, ours can leverage people as diverse sources to meet its knowledge needs. We test the agent in social contexts, where personal and subjective information can be obtained through dialogue. We provide the agent both with generic methods for assessing its knowledge quality (e.g. correctness, completeness, redundancy, interconnectedness, and diversity), as well as with generic capabilities to improve its knowledge by leveraging external sources. We demonstrate that the agent can learn effective policies to acquire the knowledge needed by assessing the efficiency of these capabilities during interaction. Our framework enables on-the-fly learning, offering a dynamic and adaptive approach to shaping conversational interactions.

Knowledge-centered conversational agents with a drive to learn

Language models (LMs) encode world knowledge in their internal parameters through training. However, LMs may learn personal and confidential information from the training data, leading to privacy concerns such as data leakage. Therefore, research on knowledge deletion from LMs is essential. This study focuses on the knowledge stored in LMs and analyzes the relationship between the side effects of knowledge deletion and the entities related to the knowledge. Our findings reveal that deleting knowledge related to popular entities can have catastrophic side effects. Furthermore, this research is the first to analyze knowledge deletion in models trained on synthetic knowledge graphs, indicating a new direction for controlled experiments.

The Curse of Popularity: Popular Entities have Catastrophic Side Effects when Deleting Knowledge from Language Models

With the remarkable development of large language models (LLMs), ensuring the factuality of output has become a challenge.
However, having all the contents of the response with given knowledge or facts is not necessarily a good thing in dialogues.
This study aimed to achieve both attractiveness and factuality in a dialogue response for which a task was set to predict sentences that do not require factual correctness judgment such as agreeing, or personal opinions/feelings.
We created a dataset, dialogue dataset annotated with fact-check-needed label (DDFC), for this task via crowdsourcing, and classification tasks were performed on several models using this dataset.
The model with the highest classification accuracy could yield about $88$\% accurate classification results.

Detecting Response Generation Not Requiring Factual Judgment

The ability to generate code using large language models (LLMs) has been increasing year by year. However, studies on code generation at the repository level are not very active. In repository-level code generation, it is necessary to refer to related code snippets among multiple files. By taking the similarity between code snippets, related files are searched and input into an LLM, and generation is performed. This paper proposes a method to search for related files (code search) by taking similarities not between code snippets but between the texts converted from the code snippets by the LLM. We confirmed that converting to text improves the accuracy of code search.

Improving Repository-level Code Search with Text Conversion

Few-shot learning techniques for Event Extraction are developed to alleviate the cost of data annotation. However, most studies on few-shot event extraction only focus on event trigger detection and no study has been proposed on argument extraction in a meta-learning context. In this paper, we investigate few-shot event argument extraction using prototypical networks, casting the task as a relation classification problem. Furthermore, we propose to enhance the relation embeddings by injecting syntactic knowledge into the model using graph convolutional networks. Our experimental results show that our proposed approach achieves strong performance on ACE 2005 in several few-shot configurations, and highlight the importance of syntactic knowledge for this task. More generally, our paper provides a unified evaluation framework for meta-learning approaches for argument extraction.

Few-Shot Event Argument Extraction Based on a Meta-Learning Approach

The development of large language models (LLMs) is becoming increasingly significant, and there is a demand for high-quality, large-scale corpora for their pretraining.
The quality of a web corpus is especially essential to improve the performance of LLMs because it accounts for a large proportion of the whole corpus. However, filtering methods for Web corpora have yet to be established.
In this paper, we present empirical studies to reveal which filtering methods are indeed effective and analyze why they are.
We build classifiers and language models in Japanese that can process large amounts of corpora rapidly enough for pretraining LLMs in limited computational resources. By evaluating these filtering methods based on a Web corpus quality evaluation benchmark, we reveal that the most accurate method is the N-gram language model. Indeed, we empirically present that strong filtering methods can rather lead to lesser performance in downstream tasks.
We also report that the proportion of some specific topics in the processed documents decreases significantly during the filtering process.

Investigating Web Corpus Filtering Methods for Language Model Development in Japanese

The fill-in-the-blank prompts are widely used to evaluate how well pre-trained language models (PLMs) capture real-world factual knowledge. 
However, the prompt-based evaluation results vary significantly depending on the linguistic expressions of the prompts, even for the same knowledge. 
To evaluate the ability of PLMs to understand facts more fairly and from diverse perspectives, we propose a new factual knowledge probing dataset - MyriadLAMA, and the evaluation benchmark, BELIEF. 
MyriadLAMA is a fill-in-the-blank dataset that contains numerous lexically, syntactically, and semantically diverse prompts for each fact. 
BELIEF is a method to mitigate prompt bias in evaluating knowledge in PLMs by aggregating results from multiple prompts for each fact.
Based on MyriadLAMA, BELIEF enables the comprehensive evaluation of factual knowledge in PLMs from multiple perspectives, covering the perspective of consistency and reliability.
Our experiments confirm the effectiveness of BELIEF via the evaluation of factual knowledge across multiple PLMs such as BERT.

BELIEF: A Bias-free Estimation of Language Models in Factual Knowledge Understanding

We explore Visual Prompt Injection (VPI) that maliciously exploits the ability of Large Vision-Language Models (LVLMs) to follow instructions drawn onto the input image.
We propose a new VPI method, Goal Hijacking via Visual Prompt Injection (GHVPI), that swaps the execution task of LVLMs from an original task to an alternative task designated by an attacker.
The quantitative analysis indicates that GPT-4V is vulnerable to GHVPI and demonstrates a notable attack success rate of 15.8%, which is a significant security risk.
Our analysis also shows that successful GHVPI requires high character recognition capability and instruction-following ability in LVLMs.

Empirical Analysis of Large Vision Language Models against Goal Hijacking via Visual Prompt Injection

Non-autoregressive (NAR) language models are known for their low latency in neural machine translation (NMT). However, a performance gap exists between NAR and autoregressive models due to the large decoding space and difficulty in capturing dependency between target words accurately. Compounding this, preparing appropriate training data for NAR models is a non-trivial task, often exacerbating exposure bias. To address these challenges, we apply reinforcement learning (RL) to Levenshtein Transformer, a representative edit-based NAR model, demonstrating that RL with self-generated data can enhance the performance of edit-based NAR models. We explore two RL approaches: stepwise reward maximization and episodic reward maximization. We discuss the respective pros and cons of these two approaches and empirically verify them. Moreover, we experimentally investigate the impact of temperature setting on performance, confirming the importance of proper temperature setting for NAR models' training.

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Leveraging and Evaluating Large Language Models for Emotion-Cause Pair Extraction

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