United States

The growing adoption of large language models (LLMs) in business applications has amplified interest in Natural Language to SQL (NL2SQL) solutions, in which there is competing demand for high performance and efficiency. Domain- and customer-specific requirements further complicate the problem. To address this conundrum, we introduce Distill-C, a distilled customization framework tailored for NL2SQL tasks. Distill-C utilizes large teacher LLMs to produce high-quality synthetic data through a robust and scalable pipeline. Finetuning smaller and open-source LLMs on this synthesized data enables them to rival or outperform teacher models an order of magnitude larger. 
Evaluated on multiple challenging benchmarks, Distill-C achieves an average improvement of 36% in execution accuracy compared to the base models from three distinct LLM families. Additionally, on three internal customer benchmarks, Distill-C demonstrates a 22.6% performance improvement over the base models. Our results demonstrate that Distill-C is an effective, high-performing and generalizable approach for deploying lightweight yet powerful NL2SQL models, delivering exceptional accuracies while maintaining low computational cost.

NAACL 2025

Distill-C: Enhanced NL2SQL via Distilled Customization with LLMs

customization

llms

nl2sql

distillation

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* 

[![](https://assets.underline.io/markdown_image/1/image/b087f8a4dc5816d6e1a6514e59c59ac3.png)](https://drive.google.com/file/d/1T96GzPqObXrMln2BMByCSXTSizjTg69P/view?usp=sharing)

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Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Maintaining persona consistency is paramount in the application of open-domain dialogue systems, as exemplified by models like ChatGPT. Despite significant advancements, the limited scale and diversity of current persona dialogue datasets remain challenges to achieving robust persona-consistent dialogue models. In this study, drawing inspiration from the success of large-scale pre-training, we introduce PPDS, an open-domain persona dialogue system that employs extensive generative pre-training on a persona dialogue dataset to enhance persona consistency. Specifically, we present a persona extraction model designed to autonomously and precisely generate vast persona dialogue datasets. Additionally, we unveil a pioneering persona augmentation technique to address the invalid persona bias inherent in the constructed dataset. Both quantitative and human evaluations consistently highlight the superior response quality and persona consistency of our proposed model, underscoring its effectiveness.

Dialogue Language Model with Large-Scale Persona Data Engineering

News aggregators play a key role in the rapidly evolving digital landscape by providing comprehensive and timely news stories aggregated from diverse sources into one feed. As these articles are sourced from different outlets, they often end up covering the same underlying event but differ in phrasing, formatting or supplemented with additional details. It is crucial for the news aggregators to identify these near-duplicates, improving the content quality and user engagement by steering away from redundant information. The problem of near-duplicate news detection has become harder with increasing use of paywalls by the news websites resulting in restricted access to the content. It is now common to get only the headline and a short snippet from the article. Previous works have concentrated on full length versions of documents such as webpages. There is very little work that focuses on this variation of the near-duplicate detection problem in which only headline and a small text blurb is available for each news article. We propose Near-Duplicate Detection Using Metadata Augmented Communities (NDD-MAC) approach that combines embeddings from pretrained language model (PLM) and latent metadata of a news article followed by community detection to identify clusters of near-duplicates. We show the efficacy of proposed approach using 2 different real-world datasets. By integrating metadata with community detection, NDD-MAC is able to detect nuanced similarities and differences in news snippets and offers an industrial scale solution for the near-duplicate detection in scenarios with restricted content availability.

Improved Near-Duplicate Detection for Aggregated and Paywalled News-Feeds

In human cognitive memory psychology, context-dependent effect helps retrieve key memory cues that is essential for recalling relevant knowledge in problem-solving. Inspired by this, we introduce the context-dependent memory framework (CDMem), an efficient architecture that mimics human memory processes through multistage encoding, context-aware storage, and retrieval strategies for LLM-centric agents.
We propose multistage memory encoding strategies for acquiring high-quality multi-level knowledge: expert encoding compresses raw trajectories from a domain-expert perspective, short-term encoding consolidates experiences from current tasks, and long-term encoding reflects insights from past tasks.
For memory storage and retrieval, we design a semi-structured, context-dependent indexing mechanism that allows agents to efficiently and accurately recall the most relevant multi-level knowledge, tailored to the current task and environmental context.
Furthermore, the proposed CDMem framework is an online learning architecture, enabling agents to efficiently learn and update memory while adapting to novel environments and tasks in real-world applications.
We conduct extensive experiments on two interactive decision-making benchmarks in the navigation and manipulation domain, ALFWorld and ScienceWorld. Using GPT-4o-mini, a more efficient variant of LLMs, our method surpasses state-of-the-art online LLM-centric approaches, achieving success rates of 85.8% and 56.0%, respectively.
We hope that this work will serve as a valuable reference for both academic and industrial communities in advancing agent-based applications.

An Efficient Context-Dependent Memory Framework for LLM-Centric Agents

Large language models (LLMs) have excelled in various NLP tasks, including machine translation (MT), yet most studies focus on sentence-level translation. This work investigates the inherent capability of instruction-tuned LLMs for document-level translation (docMT). Unlike prior approaches that require specialized techniques, we evaluate LLMs by directly prompting them to translate entire documents in a single pass. Our results show that this method improves translation quality compared to translating sentences separately, even without document-level fine-tuning. However, this advantage is not reflected in BLEU scores, which often favor sentence-based translations. We propose using the LLM-as-a-judge paradigm for evaluation, where GPT-4 is used to assess document coherence, accuracy, and fluency in a more nuanced way than n-gram-based metrics. Overall, our work demonstrates that instruction-tuned LLMs can effectively leverage document context for translation. However, we caution against using BLEU scores for evaluating docMT, as they often provide misleading outcomes, failing to capture the quality of document-level translation.

Fine-Grained and Multi-Dimensional Metrics for Document-Level Machine Translation

Vision-Language Models (VLMs) generate contextually rich text from visual data but are prone to "hallucinations"—content not supported by visuals. This undermines user trust and limits the applicability of VLMs in critical domains like healthcare and law, either by directly interacting with images or by accessing token probabilities assigned by vision-language models (VLMs). We propose a language-only black-box hallucination detection approach for VLMs. We evaluate three methods: (1) shallow classifiers using text statistics like TF-IDF and TTP, (2) transformer-based classifiers capturing semantic nuances, and (3) an ensemble model combining strengths of multiple architectures. By analyzing prompts and outputs, we identify patterns enabling classifiers to distinguish between supported and unsupported content. These findings show the effectiveness of language-only classifiers in detecting hallucinations and offer insights into the textual features aiding this process.

Can You See the Lie Without Looking? Language-Only Approach to Hallucination Detection in VLMs

Quizzes play a crucial role in education by reinforcing students' understanding of key concepts and encouraging self-directed exploration. However, compiling high-quality quizzes can be challenging and require deep expertise and insight into specific subject matter. Although LLMs have greatly enhanced the efficiency of quiz generation, concerns remain regarding the quality of these AI-generated quizzes and their educational impact on students. To address these issues, we introduce ConQuer, a concept-based quiz generation framework that leverages external knowledge sources. We employ comprehensive evaluation dimensions to assess the quality of the generated quizzes, using LLMs as judges. Our experiment results demonstrate a 4.8% improvement in evaluation scores and a 77.52% win rate in pairwise comparisons against baseline quiz sets. Ablation studies further underscore the effectiveness of each component in our framework.

ConQuer: A Framework for Concept-Based Quiz Generation

The emergence of large language models (LLMs) has significantly influenced numerous fields, including healthcare, by enhancing the capabilities of automated systems to process and generate human-like text. However, despite their advancements, the reliability and accuracy of LLMs in medical contexts remain critical concerns. Current evaluation methods often lack robustness and fail to provide a comprehensive assessment of LLM performance, leading to potential risks in clinical settings. In this work, we propose Med-CoDE, a specifically designed evaluation framework for medical LLMs to address these challenges. The framework leverages a critique-based approach to quantitatively measure the degree of disagreement between model-generated responses and established medical ground truths. This framework captures both accuracy and reliability in medical settings. The proposed evaluation framework aims to fill the existing gap in LLM assessment by offering a systematic method to evaluate the quality and trustworthiness of medical LLMs. Through extensive experiments and case studies, we illustrate the practicality of our framework in providing a comprehensive and reliable evaluation of medical LLMs.

Med-CoDE: Medical Critique based Disagreement Evaluation Framework

Distinguishing vulnerable code from non-vulnerable code is challenging due to high inter-class similarity. Supervised contrastive learning (SCL) improves embedding separation but struggles with intra-class clustering, especially when variations within the same class are subtle. We propose Cluster-Enhanced Supervised Contrastive Loss (CESCL), an extension of SCL with a distance-based regularization term that tightens intra-class clustering while maintaining inter-class separation. Evaluating on CodeBERT and GraphCodeBERT with Binary Cross Entropy (BCE), BCE + SCL, and BCE + CE-SCL, our method improves F1 score by 1.76% on CodeBERT and 4.1% on GraphCodeBERT, demonstrating its effectiveness in code vulnerability detection and broader applicability to high-similarity classification tasks.

Tighter Clusters, Safer Code? Improving Vulnerability Detection with Enhanced Contrastive Loss

Large Language Models (LLMs) excel in a wide range of tasks across natural language processing, multimodal applications, and other domains but are computationally expensive to train and fine-tune. Model merging is an efficient alternative that requires minimal computational resources, yet existing methods overlook the structural importance of individual weights within an LLM. We first investigate the concept of superweights, analyzing their significance in LLMs and their stability across domains. Based on these insights, we develop a superweight-based model merging strategy that selectively prioritizes critical parameters. Building upon this merging framework, we introduce SNTIES, a superweight-normalized TIES approach that enhances existing merging techniques through targeted normalization. By refining model representations and amplifying the importance of superweights, SNTIES improves performance, achieving a margin over TIES merging and demonstrating its effectiveness as a computationally efficient enhancement to model merging strategies.

Are a Handful of Weights Enough? Superweight-based Merging for Efficient Knowledge Transfer in Large Language Models

Mental manipulation is a subtle yet pervasive form of abuse in interpersonal communication, making its detection critical for safeguarding potential victims. However, due to manipulation's nuanced and context-specific nature, identifying manipulative language in complex, multi-turn, and multi-person conversations remains a significant challenge for large language models (LLMs). To address this gap, we introduce the MultiManip dataset, comprising 220 multi-turn, multi-person dialogues balanced between manipulative and non-manipulative interactions, all drawn from reality shows that mimic real-world scenarios. The dataset includes 11 distinct manipulations depicting real-life scenarios for manipulative interactions. We conduct extensive evaluations of state-of-the-art LLMs, such as GPT-4o and Llama-3.1-8B, employing various prompting strategies. Despite their advanced capabilities, these models often struggle to detect manipulation effectively. To overcome this limitation, we propose SELF-PERCEPT, a novel, two-stage prompting framework inspired by Self-Perception Theory, demonstrating strong performance in detecting multi-person, multi-turn mental manipulation. Our code and data are publicly available at (https://anonymous.4open.science/r/SELF-PERCEPT-B2FF).

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Dialogue Language Model with Large-Scale Persona Data Engineering

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