United States

The recently introduced Mojo programming language (PL) by Modular, has received significant attention in the scientific community due to its claimed significant speed boost over Python. Despite advancements in code Large Language Models (LLMs) across various PLs, Mojo remains unexplored in this context. To address this gap, we introduce MojoBench, the first framework for Mojo code generation. MojoBench includes HumanEval-Mojo, a benchmark dataset designed for evaluating code LLMs on Mojo, and Mojo-Coder, the first LLM pretrained and finetuned for Mojo code generation, which supports instructions in 5 natural languages (NLs). Our results show that Mojo-Coder achieves a 30-35% performance improvement over leading models like GPT-4o and Claude-3.5-Sonnet. Furthermore, we provide insights into LLM behavior with underrepresented and unseen PLs, offering potential strategies for enhancing model adaptability. MojoBench contributes to our understanding of LLM capabilities and limitations in emerging programming paradigms fostering more robust code generation systems.

NAACL 2025

MojoBench: Language Modeling and Benchmarks for Mojo

code generation

language modeling

benchmarking

### 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

poster

Membership inference attacks (MIA) attempt to verify the membership of a given data sample in the training set for a model. MIA has become relevant in recent years, following the rapid development of large language models (LLM). Many are concerned about the usage of copyrighted materials for training them and call for methods for detecting such usage. However, recent research has largely concluded that current MIA methods do not work on LLMs. Even when they seem to work, it is usually because of the ill-designed experimental setup where other shortcut features enable ''cheating.'' In this work, we argue that MIA still works on LLMs, but only when multiple documents are presented for testing. We construct new benchmarks that measure the MIA performances at a continuous scale of data samples, from sentences (n-grams) to a collection of documents (multiple chunks of tokens). To validate the efficacy of current MIA approaches at greater scales, we adapt a recent work on Dataset Inference (DI) for the task of binary membership detection that aggregates paragraph-level MIA features to enable document- and dataset-level MIA. This baseline achieves the first successful MIA on pre-trained and fine-tuned LLMs.

Scaling Up Membership Inference: When and How Attacks Succeed on Large Language Models

Question Answering (QA) is an important part of tasks like text classification through information gathering. These are finding increasing use in sectors like healthcare, customer support, legal services, etc., to collect and classify responses into actionable categories. LLMs, although can support QA systems, they face a significant challenge of insufficient or missing information for classification. Although LLMs excel in reasoning, the models rely on their parametric knowledge to answer. However, questioning the user requires domain-specific information aiding to collect accurate information. Our work, GUIDEQ, presents a novel framework for asking guided questions to further progress a partial information. We leverage the explainability derived from the classifier model along with LLMs to ask guided questions, further enhancing the information. This further information helps in more accurate classification of a text. GUIDEQ derives the most significant key-words representative of a label using occlusions. We develop GUIDEQ’s prompting strategy for guided questions based on the top-3 classifier label outputs and the significant words, to seek specific and relevant information, and classify in a targeted manner. Through our experimental results, we demonstrate that GUIDEQ outperforms other LLMbased baselines, yielding improved F1-Score through the accurate collection of relevant further information. We perform various analytical studies and also report better question quality compared to our method.

GuideQ: Framework for Guided Questioning for progressive informational collection and classification

Despite active research on Automated Essay Scoring (AES), there is a noticeable scarcity of studies focusing on predicting creativity scores for essays. In this study, we develop a new essay rubric specifically designed for assessing creativity in essays. Leveraging this rubric, we construct ground truth data consisting of 5,048 essays. Furthermore, we propose a novel self-supervised learning model that recognizes cluster patterns within the essay embedding space and leverages them for creativity scoring. This approach aims to automatically generate a high-quality training set, thereby facilitating the training of diverse language models. Our experimental findings indicated a substantial enhancement in the assessment of essay creativity, demonstrating an increase in F1-score up to 58% compared to the primary state-of-the-art models across the ASAP and AIHUB datasets.

Representation-to-Creativity (R2C): Automated Holistic Scoring Model for Essay Creativity

With the rapid growth of Large Language Models (LLMs), safeguarding textual content against unauthorized use is crucial. Watermarking offers a vital solution, protecting both - LLM-generated and plain text sources. This paper presents a unified overview of different perspectives behind designing watermarking techniques through a comprehensive survey of the research literature. Our work has two key advantages: (1) We analyze research based on the specific intentions behind different watermarking techniques, evaluation datasets used, and watermarking addition and removal methods to construct a cohesive taxonomy. (2) We highlight the gaps and open challenges in text watermarking to promote research protecting text authorship. This extensive coverage and detailed analysis sets our work apart, outlining the evolving landscape of text watermarking in Language Models.

From Intentions to Techniques: A Comprehensive Taxonomy and Challenges in Text Watermarking for Large Language Models

Code-LLMs, LLMs pre-trained on large code corpora, have shown great progress in learning rich representations of the structure and syntax of code, successfully using it to generate or classify code fragments. At the same time, understanding if they are able to do so because they capture code semantics, and how well, is still an open question. In this paper, we tackle this problem by introducing SeqCoBench, a benchmark for systematically assessing how Code-LLMs can capture code functional equivalence. SeqCoBench contains over 20 code transformations that either preserve or alter the semantics of Python programs. We conduct extensive evaluations in different settings, including zero-shot and parameter-efficient finetuning methods on state-of-the-art (Code)-LLMs to see if they can discern semantically equivalent or different pairs of programs in SeqCoBench. We find that the performance gap between these LLMs and classical match-based retrieval scores is minimal, with both approaches showing a concerning lack of depth in understanding code semantics.

What can Large Language Models Capture about Code Functional Equivalence?

The rapid evolution of Large Language Models (LLMs) presents a promising solution to the global shortage of mental health professionals. However, their alignment with essential counseling competencies remains underexplored. We introduce CounselingBench, a novel NCMHCE-based benchmark evaluating 22 general-purpose and medical-finetuned LLMs across five key competencies. While frontier models surpass minimum aptitude thresholds, they fall short of expert-level performance, excelling in Intake, Assessment & Diagnosis but struggling with Core Counseling Attributes and Professional Practice & Ethics. Surprisingly, medical LLMs do not outperform generalist models in accuracy, though they provide slightly better justifications while making more context-related errors. These findings highlight the challenges of developing AI for mental health counseling, particularly in competencies requiring empathy and nuanced reasoning. Our results underscore the need for specialized, fine-tuned models aligned with core mental health counseling competencies and supported by human oversight before real-world deployment. Code and data associated with this manuscript can be found at: https://github.com/cuongnguyenx/CounselingBench

Do Large Language Models Align with Core Mental Health Counseling Competencies?

Large Language Models (LLMs) have demonstrated emergent common-sense reasoning and Theory of Mind (ToM) capabilities, making them promising candidates for developing coordination agents. This study introduces the LLM-Coordination Benchmark, a novel benchmark for analyzing LLMs in the context of Pure Coordination Settings, where agents must cooperate to maximize gains. Our benchmark evaluates LLMs through two distinct tasks. The first is Agentic Coordination, where LLMs act as proactive participants in four pure coordination games. The second is Coordination Question Answering (CoordQA), which tests LLMs on 198 multiple-choice questions across these games to evaluate three key abilities: Environment Comprehension, ToM Reasoning, and Joint Planning. Results from Agentic Coordination experiments reveal that LLM-Agents excel in multi-agent coordination settings where decision-making primarily relies on environmental variables but face challenges in scenarios requiring active consideration of partners' beliefs and intentions. The CoordQA experiments further highlight significant room for improvement in LLMs' Theory of Mind reasoning and joint planning capabilities. Zero-Shot Coordination (ZSC) experiments in the Agentic Coordination setting demonstrate that LLM agents, unlike RL methods, exhibit robustness to unseen partners. These findings indicate the potential of LLMs as Agents in pure coordination setups and underscore areas for improvement.

LLM-Coordination: Evaluating and Analyzing Multi-agent Coordination Abilities in Large Language Models

Assertions have been the de facto collateral for hardware for over a decade. The verification quality, i.e., detection and diagnosis of corner-case design bugs, is critically dependent on the assertion quality. There has been a considerable amount of research to generate high-quality assertions from hardware design source code and design execution trace data. With recent advent of generative AI techniques such as Large-Language Models (LLMs), there has been a renewed interest in deploying LLMs for assertion generation. However, there is little effort to quantitatively establish the effectiveness and suitability of various LLMs for assertion generation. In this paper, we present AssertionBench, a novel benchmark to evaluate LLMs' effectiveness for assertion generation quantitatively. AssertionBench contains 100 curated Verilog hardware designs from OpenCores and formally verified assertions for each design, generated from GoldMine and HARM. We use AssertionBench to compare state-of-the-art LLMs, e.g., GPT-3.5, GPT-4o, CodeLLaMa-2, and LLaMa3-70B, to assess their effectiveness in inferring functionally correct assertions for hardware designs. Our experiments comprehensively demonstrate how LLMs perform relative to each other, the benefits of using more in-context exemplars in generating a higher fraction of functionally correct assertions, and the significant room for improvement for LLM-based assertion generators.

AssertionBench: A Benchmark to Evaluate Large-Language Models for Assertion Generation

Large Language Models (LLM) have become a popular approach for implementing Retrieval Augmented Generation (RAG) systems, and a significant amount of effort has been spent on building good models and metrics. In spite of increased recognition of the need for rigorous evaluation of RAG systems, few tools exist that go beyond the creation of model output and automatic calculation. We present InspectorRAGet, an introspection platform for performing a comprehensive analysis of the quality of RAG system output. InspectorRAGet allows the user to analyze aggregate and instance-level performance of RAG systems, using both human and algorithmic
metrics as well as annotator quality. InspectorRAGet is suitable for multiple use cases and is available publicly to the community.
A live instance of the platform is available at https://ibm.biz/InspectorRAGet

InspectorRAGet: An Introspection Platform for RAG Evaluation

While much work on web agents emphasizes the promise of autonomously performing tasks on behalf of users, in reality, agents often fall
short on complex tasks in real-world contexts and modeling user preference. This presents an opportunity for humans to collaborate with the agent and leverage the agent’s capabilities effectively. We propose CowPilot, a frame- work supporting autonomous as well as human-agent co llaborative w eb navigation, and evaluation across task success and task efficiency. CowPilot reduces the number of steps humans need to perform by allowing agents to propose next steps, while users are able to pause, reject, or take alternative actions. During execution, users can interleave their actions with the agent’s by overriding suggestions or resuming agent control when needed. We conducted case studies on five common websites and found that the human-agent collaborative mode achieves the highest success rate of 95% while requiring humans to perform only 15.2% of the total steps. Even with human interventions during task execution, the agent successfully drives up to half of task success on its own. CowPilot can serve as a useful tool for data collection and agent evaluation across websites, which we believe will enable research in how users and agents can work together. Video demonstrations are available at https://oaishi.github.io/cowpilot.html

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Scaling Up Membership Inference: When and How Attacks Succeed on Large Language Models

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