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Data is the lifeblood of large language models (LLMs). While the quantity of open-source data available for training LLMs is substantial, its integrity often falls short. For instance, the open-source chat version of Yi-1.5-9B scores 5.20 on AlignBench, while the Chinese Alpaca-GPT4 version scores 4.12. This discrepancy makes it challenging for developers to create models that excel in downstream tasks and instruction following. Therefore, it is essential to improve data integrity. Currently, there are two mainstream methods for enhancing data integrity: data filtering and data augmentation. Due to the labor-intensive and time-consuming nature of performing these tasks manually, some of these efforts are now being undertaken by LLMs, owing to their high alignment with human preferences. However, we have found that performing data filtering or data augmentation with LLMs has limited effectiveness in improving data integrity. In this work, we propose FiNE (**F**iltering and **I**mproving **N**oisy data **E**laborately), a method that performs refined filtering and improvement of training data with LLMs. Using the data obtained through our method to train Yi-1.5-9B, the performance gap on AlignBench between our model and the open-source chat version is reduced from 1.08 to 0.35. Additionally, on HalluQA, our model surpasses the open-source chat version by 8.45.

NAACL 2025

FiNE: Filtering and Improving Noisy Data Elaborately with Large Language Models

data-efficient training

distillation

data augmentation

poster

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
**[Continue reading...](https://drive.google.com/file/d/1jX-qGhqVSZZCIrAnJaz798pflu5Irrdn/view?usp=sharing)**

*- Colin Cherry, Google, NAACL 2025 General Chair* 

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Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

The effectiveness of embedding methods is crucial for optimizing text classification performance in Automated Machine Learning (AutoML). However, selecting the most suitable pre-trained model for a given task remains challenging. This study introduces the Corpus-Driven Domain Mapping (CDDM) pipeline, which utilizes a domain-annotated corpus of pre-fine-tuned models from the Hugging Face Model Hub to improve model selection. Integrating these models into AutoML systems significantly boosts classification performance across multiple datasets compared to baseline methods. Despite some domain recognition inaccuracies, results demonstrate CDDM’s potential to enhance model selection, streamline AutoML workflows, and reduce computational costs.

AutoML Meets Hugging Face: Domain-Aware Pretrained Model Selection for Text Classification

Question Answering (QA) and Visual Question Answering (VQA) are well-studied problems in the language and vision domain. One challenging scenario involves multiple sources of information, each of a different modality, where the answer to the question may exist in one or more sources. This scenario contains richer information but is highly complex to handle. In this work, we formulate a novel question-answer generation (QAG) framework in an environment containing multi-source, multimodal information. The answer may belong to any or all sources; therefore, selecting the most prominent answer source or an optimal combination of all sources for a given question is challenging. To address this issue, we propose a question-guided attention mechanism that learns attention across multiple sources and decodes this information for robust and unbiased answer generation. To learn attention within each source, we introduce an explicit alignment between questions and various information sources, which facilitates identifying the most pertinent parts of the source information relative to the question. Scalability in handling diverse questions poses a challenge. We address this by extending our model to a sparse mixture-of-experts (sparse-MoE) framework, enabling it to handle thousands of question types. Experiments on T5 and Flan-T5 using three datasets demonstrate the model's efficacy, supported by ablation studies.

MoEMoE: Question Guided Dense and Scalable Sparse Mixture-of-Expert for Multi-source Multi-modal Answering

Open-source large language models (Os-LLMs) propel the democratization of natural language research by giving the flexibility to augment or update model parameters for performance improvement. 
Nevertheless, like proprietary LLMs, Os-LLMs offer poorer performance on low-resource languages (LRLs) than high-resource languages (HRLs), owing to smaller amounts of training data and underrepresented vocabulary. 
On the other hand, continual pre-training (CPT) with large amounts of language-specific data is a costly proposition in terms of data acquisition and computational resources. 
Our goal is to drastically reduce CPT cost.
To that end, we first develop a new algorithm to select a subset of texts from a larger corpus. We show the effectiveness of our technique using very little CPT data. 
In search of further improvement, we design a new algorithm to select tokens to include in the LLM vocabulary.
We experiment with the recent Llama-3 model and nine Indian languages with diverse scripts and extent of resource availability.
For evaluation, we use IndicGenBench, a generation task benchmark dataset for Indic languages. We experiment with various CPT corpora and augmented vocabulary size and offer insights across language families.

Efficient Continual Pre-training of LLMs for Low-resource Languages

This work presents a speech-to-text system for scientists and journalists whish is based on three-component architecture aimed at improving speech recognition accuracy while minimizing errors and hallucinations associated with the Whisper model. The architecture comprises primary recognition using Wav2Vec2, false positive filtering via the Audio Spectrogram Transformer (AST), and final speech recognition through Whisper. The implementation of curriculum learning methods and the utilization of diverse Russian-language speech corpora significantly enhanced the system's effectiveness. Additionally, advanced uncertainty modeling techniques were introduced, contributing to further improvements in transcription quality. The proposed approaches ensure robust transcribing of long audio data across various acoustic conditions compared to WhisperX and usual Whisper.

Pisets: A Robust Speech Recognition System for Lectures and Interviews

Large language model (LLM) safety is a critical issue, with numerous studies employing red team testing to enhance model security. Among these, jailbreak methods explore potential vulnerabilities by crafting malicious prompts that induce model outputs contrary to safety alignments. Existing black-box jailbreak methods often rely on model feedback, repeatedly submitting queries with detectable malicious instructions during the attack search process. Although these approaches are effective, the attacks may be intercepted by content moderators during the search process. We propose an improved transfer attack method that guides malicious prompt construction by locally training a mirror model of the target black-box model through benign data distillation. This method offers enhanced stealth, as it does not involve submitting identifiable malicious instructions to the target model during the search phase. Our approach achieved a maximum attack success rate of 92%, or a balanced value of 80% with an average of 1.5 detectable jailbreak queries per sample against GPT-3.5 Turbo on a subset of AdvBench. These results underscore the need for more robust defense mechanisms.

Stealthy Jailbreak Attacks on Large Language Models via Benign Data Mirroring

Recent advancements in Large Language Models (LLMs) have sparked widespread concerns about their safety. Recent work demonstrates that safety alignment of LLMs can be easily removed by fine-tuning with a few adversarially chosen instruction-following examples, i.e., fine-tuning attacks. We take a further step to understand fine-tuning attacks in multilingual LLMs. We first discover cross-lingual generalization of fine-tuning attacks: using a few adversarially chosen instruction-following examples in one language, multilingual LLMs can also be easily compromised (e.g., multilingual LLMs fail to refuse harmful prompts in other languages). Motivated by this finding, we hypothesize that safety-related information is language-agnostic and propose a new method termed Safety Information Localization (SIL) to identify the safety-related information in the model parameter space. Through SIL, we validate this hypothesis and find that only changing 20% of weight parameters in fine-tuning attacks can break safety alignment across all languages. Furthermore, we provide evidence to the alternative pathways hypothesis for why freezing safety-related parameters does not prevent fine-tuning attacks, and we demonstrate that our attack vector can still jailbreak LLMs adapted to new languages.

Towards Understanding the Fragility of Multilingual LLMs against Fine-Tuning Attacks

Moderation is essential for maintaining and improving the quality of online discussions. This involves: (1) countering negativity, e.g. hate speech and toxicity, and (2)~promoting positive discourse, e.g. broadening the discussion to involve other users and perspectives. While significant efforts have focused on addressing negativity, driven by an urgency to address such issues, this left moderation promoting positive discourse (henceforth PositiveModeration}) under-studied. With the recent advancements in LLMs, Positive Moderation can potentially be scaled to vast conversations, fostering more thoughtful discussions and bridging the increasing divide in online interactions. We advance the understanding of Positive Moderation by annotating a dataset on 13 moderation properties, e.g. neutrality, clarity and curiosity. We extract instructions from professional moderation guidelines and use them to prompt LLaMA to generate such moderation. This is followed by extensive evaluation showing that (1) annotators rate generated higher than professional moderation, but still slightly prefer professional moderation in pairwise comparison, and (2) LLMs can be used to estimate human evaluation as an efficient alternative.

It Is Not Only the Negative that Deserves Attention! Understanding, Generation & Evaluation of (Positive) Moderation

Recent advancements in Vision Language Models (VLMs) have demonstrated remarkable promise in generating visually grounded responses. However, their application in the medical domain is hindered by unique challenges. For instance, most VLMs rely on a single method of visual grounding, whereas complex medical tasks demand more versatile approaches. Additionally, while most VLMs process only 2D images, a large portion of medical images are 3D. The lack of medical data further compounds these obstacles. To address these challenges, we present VividMed, a vision language model with versatile visual grounding for medicine. Our model supports generating both semantic segmentation masks and instance-level bounding boxes, and accommodates various imaging modalities, including both 2D and 3D data. We design a three-stage training procedure and an automatic data synthesis pipeline based on open datasets and models. Besides visual grounding tasks, VividMed also excels in other common downstream tasks, including Visual Question Answering (VQA) and report generation. Ablation studies empirically show that the integration of visual grounding ability leads to improved performance on these tasks. Our code is publicly available at https://github.com/function2-llx/MMMM.

VividMed: Vision Language Model with Versatile Visual Grounding for Medicine

3D facial emotion modeling has important applications in areas such as animation design, virtual reality, and emotional human-computer interaction (HCI). However, existing models are constrained by limited emotion classes and insufficient datasets. To address this, we introduce Emo3D, an extensive ''Text-Image-Expression dataset'' that spans a wide spectrum of human emotions, each paired with images and 3D blendshapes. Leveraging Large Language Models (LLMs), we generate a diverse array of textual descriptions, enabling the capture of a broad range of emotional expressions. Using this unique dataset, we perform a comprehensive evaluation of fine-tuned language-based models and vision-language models, such as Contrastive Language-Image Pretraining (CLIP), for 3D facial expression synthesis. To better assess conveyed emotions, we introduce Emo3D metric, a new evaluation metric that aligns more closely with human perception than traditional Mean Squared Error (MSE). Unlike MSE, which focuses on numerical differences, Emo3D captures emotional nuances in visual-text alignment and semantic richness. Emo3D dataset and metric hold great potential for advancing applications in animation and virtual reality.

Emo3D: Metric and Benchmarking Dataset for 3D Facial Expression Generation from Emotion Description

With the rapid development of large language models (LLMs), due to their strong performance across various fields, LLM-based evaluation methods (LLM-as-a-Judge) have become widely used in natural language generation (NLG) evaluation. However, these methods encounter the following challenges: (1) distinguishing instruction-following ability, (2) being applicable across diverse NLG tasks, and (3) identifying low-quality outputs. To address these issues, we propose CAMIEval, a multidimensional comparative evaluation method based on instruction-following. Specifically, we define three fundamental dimensions of instruction-following: relevance, factuality, and adherence. Subsequently, we introduce a concrete Chain-of-Thoughts (ConcreteCoT) process to enhance the accuracy of evaluations. In addition, we trained a ''regrettable model'' RegretLM to generate low-quality outputs, which helps the evaluator better identify the potential shortcomings of the candidate output by comparing low-quality outputs with reference outputs. Through this comparison, the evaluator can generate instruction-specific dimensions that complement the fundamental dimensions, forming a more comprehensive evaluation metric system. Experiments on two NLG evaluation benchmarks demonstrate that CAMIEval consistently outperforms existing methods in terms of correlation with human evaluations, providing a general and accurate framework for evaluating the outputs of LLMs.

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AutoML Meets Hugging Face: Domain-Aware Pretrained Model Selection for Text Classification

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