India

These findings reveal a presentation bias in LLM-based peer review and suggest the need for better methods to reduce such bias and support fairer automated evaluation.Code documentation plays a vital role in enhancing collaboration, maintainability, and comprehension throughout the software development lifecycle. This becomes especially critical in legacy codebases, where missing or outdated comments hinder effective debugging and onboarding. Among documentation types, inline comments are particularly valuable for conveying program logic and supporting code reuse. With the growing capabilities of large language models (LLMs), their application to tasks such as code understanding and summarization has gained significant attention in the NLP community. However, the specific task of generating high-quality inline code comments using LLMs remains relatively under-explored. In this work, we conduct a systematic evaluation of several state-of-the-art LLMs to assess their effectiveness in producing meaningful and context-aware inline documentation. To this end, we curate a dataset of well-documented code snippets and propose a fine-grained evaluation framework that assesses both the quality and sufficiency of generated comments at the statement level. We further investigate the impact of prompting strategies and offer a comparative analysis across a range of models, including large foundational LLMs to smaller, code-specialized variants, within the domain of inline code documentation. Our findings offer actionable insights that can guide the development of effective and scalable systems for automated inline code documentation.

IJCNLP-AACL 2025

Reliable Inline Code Documentation with LLMs: Fine-Grained Evaluation of Comment Quality and Coverage

inline comments

code documentation

llm evaluation

prompting techniques

## 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)*

To access the event page you need to register [**here.**](http://acl.swoogo.com/naacl2024) 
Your access to the event page is limited based on your registration type. If you registered for workshops only, you will gain access to full workshops content on the day of the workshops program.

Please register!

NAACL 2024

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

workshop paper

### Welcome to IJCNLP-AACL 2025! 
 It is a great honor to host this joint conference in Mumbai, India, from December 20 to 24, 2025. The joint conferences of IJCNLP and AACL are organized with alternating leadership in the Asia-Pacific region. The event is run by the Asian Federation of Natural Language Processing (AFNLP) in odd years, and by AACL in even years, while it is organized solely by ACL when the annual ACL meeting is held in the region. This year, the conference is primarily organized by AFNLP. 
*Kentaro Inui
MBZUAI, UAE
General Chair, IJCNLP-AACL 2025* 
Read full message and download the Conference Handbook [**here**](https://drive.google.com/file/d/1UTwxkAqSqI-GAoJC3wE1zZt5VP1Y8GX0/view?usp=sharing).

The 14th IJCNLP & 4th AACL will be held in Mumbai, India from December 20th to December 24th, 2025.

The performance of large language models (LLMs) continues to improve, as reflected in rising scores on standard benchmarks. However, the lack of transparency around training data raises concerns about potential overlap with evaluation sets and the fairness of reported results. Although prior work has proposed methods for detecting data leakage, these approaches primarily focus on identifying outliers and have not been evaluated under controlled simulated leakage conditions. In this work, we compare existing leakage detection techniques, namely permutation and n-gram-based methods, under a continual pretraining setup that simulates real-world leakage scenarios, and additionally explore a lightweight method we call semi-half question. We further introduce two efficient extensions, permutation-R and permutation-Q. While semi-half offers a low-cost alternative, our analysis shows that the n-gram method consistently achieves the highest F1-Score, performing competitively with permutation-Q. We also refine these techniques to support instance-level detection and reduce computational overhead. Leveraging the best-performing method, we create cleaned versions of MMLU and HellaSwag, and re-evaluate several LLMs. Our findings present a practical path toward more reliable and transparent evaluations, and we recommend contamination checks as a standard practice before releasing benchmark results.

Simulating Training Data Leakage in Multiple-Choice Benchmarks for LLM Evaluation

Telecom domain 3GPP documents are replete with images containing sequence diagrams. Advances in Vision-Language Large Models (VLMs) have eased conversion of such images to machine-readable PlantUML (puml) formats. However, there is a gap in evaluation of such conversions - existing works do not compare puml scripts for various components. In this work, we propose performance metrics to measure the effectiveness of such conversions. A subset of sequence diagrams from 3GPP documents is chosen to be representative of domain-specific actual scenarios. We compare puml outputs from two VLMs - Claude Sonnet and GPT-4V - against manually created ground truth representations. We use version control tools to capture differences and introduce standard performance metrics to measure accuracies along various components: participant identification, message flow accuracy, sequence ordering, and grouping construct preservation. We demonstrate effectiveness of proposed metrics in quantifying conversion errors across various components. The results show that nodes, edges and messages are accurately captured. However, we observe that VLMs do not necessarily perform well on complex structures such as notes, box, groups. Our experiments and performance metrics indicates a need for better representation of these components in training data for fine-tuned VLMs.

Measuring Visual Understanding in Telecom domain: Performance Metrics for Image-to-UML conversion using VLMs

Several multilingual benchmark datasets have been developed in a semi-automatic manner in the recent past to measure progress and understand the state-of-the-art in the multilingual capabilities of Large Language Models (LLM). However, there is not a lot of attention paid to the quality of the datasets themselves, despite the existence of previous work in identifying errors in even fully human-annotated test sets. In this paper, we manually analyze recent multilingual evaluation sets in two languages -- French and Telugu, identifying several errors in the datasets during the process. We compare the performance difference across several LLMs with the original and revised versions of the datasets and identify large differences (almost 10% in some cases) in both languages. Based on these results, we argue that test sets should not be considered immutable and should be revisited, checked for correctness, and potentially versioned. We end with some recommendations for both the dataset creators as well as consumers on addressing the dataset quality issues.

Test Set Quality in Multilingual LLM Evaluation

This paper introduces Indian Financial Narrative Inference Tasks and Evaluations (InFiNITE), a comprehensive framework for analyzing India's financial narratives through three novel inference tasks. Firstly, we present multi-modal earnings call analysis by integrating transcripts, presentation visuals, and market indicators via the Multi-Modal Indian Earnings Calls (MiMIC) dataset, enabling holistic prediction of post-call stock movements. Secondly, our Budget-Assisted Sectoral Impact Ranking (BASIR) dataset aids in systematically decoding government fiscal narratives by classifying budget excerpts into 81 economic sectors and evaluating their post-announcement equity performance. Thirdly, we introduce Bharat IPO Rating (BIR) datasets to redefine Initial Public Offering (IPO) evaluation through prospectus analysis, classifying potential investments into four recommendation categories (Apply, May Apply, Neutral, Avoid). By unifying textual, visual, and quantitative modalities across corporate, governmental, and public investment domains, InFiNITE addresses critical gaps in Indian financial narrative analysis. The open source data sets of the framework, including earnings calls, union budgets, and IPO prospectuses, establish benchmark resources specific to India for computational economic research under permissive licenses. For investors, InFiNITE enables data-driven identification of capital allocation opportunities and IPO risks, while policymakers gain structured insights to assess Indian fiscal communication impacts. By releasing these datasets publicly, we aim to facilitate research in computational economics and financial text analysis, particularly for the Indian market.

InFiNITE (∞): Indian Financial Narrative Inference Tasks & Evaluations

LLM (large language model) users of hosted providers commonly notice that outputs can vary for the same inputs under settings expected to be deterministic. While it is difficult to get exact statistics, recent reports on specialty news sites and discussion boards suggest that among users in all communities, the majority of LLM usage today is through cloud-based APIs. Yet the questions of how pervasive non- determinism is, and how much it affects perfor- mance results, have not to our knowledge been systematically investigated. We apply five API- based LLMs configured to be deterministic to eight diverse tasks across 10 runs. Experiments reveal accuracy variations of up to 15% across runs, with a gap of up to 70% between best pos- sible performance and worst possible perfor- mance. No LLM consistently delivers the same outputs or accuracies, regardless of task. We speculate about the sources of non-determinism such as input buffer packing across multiple jobs. To better quantify our observations, we introduce metrics focused on quantifying de- terminism, TARr@N for the total agreement rate at N runs over raw output, and TARa@N for total agreement rate of parsed-out answers. Our code and data will be publicly available at https://github.com/Anonymous.

Non-Determinism of “Deterministic” LLM System Settings in Hosted Environments

Large language models (LLMs) are increasingly used to assist scientists across diverse workflows. A key challenge is generating high-quality figures from textual descriptions, often represented as TikZ programs that can be rendered as scientific images. Prior research has proposed a variety of datasets and modeling approaches for this task. However, existing datasets for Text-to-TikZ are too small and noisy to capture the complexity of TikZ, causing mismatches between text and rendered figures. Moreover, prior approaches rely solely on supervised fine-tuning (SFT), which does not expose the model to the rendered semantics of the figure, often resulting in errors such as looping, irrelevant content, and incorrect spatial relations. To address these issues, we construct DaTikZ-V4, a dataset more than four times larger and substantially higher in quality than DaTikZ-V3, enriched with LLM-generated figure descriptions. Using this dataset, we train TikZilla, a family of small open-source Qwen models (3B and 8B) with a two-stage pipeline of SFT followed by reinforcement learning (RL). For RL, we leverage an image encoder trained via inverse graphics to provide semantically faithful reward signals. Extensive human evaluations with over 1,000 judgments show that TikZilla improves by 1.5-2 points over its base models on a 5-point scale, surpasses GPT-4o by 0.5 points, and matches GPT-5 in the image-based evaluation, while operating at much smaller model sizes. Code, data, and models will be made available.

TikZilla: Scaling Text-to-TikZ with High-Quality Data and Reinforcement Learning

Large Language Models (LLMs) show strong reasoning and text generation capabilities, prompting their use in scientific literature analysis, including novelty assessment. While evaluating novelty of scientific papers is crucial for peer review, it requires extensive knowledge of related work, something not all reviewers have.While recent work on LLM-assisted scientific literature analysis supports literature comparison, existing approaches offer limited transparency and lack mechanisms for result traceability via an information retrieval module. To address this gap, we introduce GraphMind, an easy-to-use interactive web tool designed to assist users in evaluating the novelty of scientific papers or drafted ideas. Specially, GraphMind enables users to capture the main structure of a scientific paper, explore related ideas through various perspectives, and assess novelty via providing verifiable contextual insights. GraphMind enables users to annotate key elements of a paper, explore related papers through various relationships, and assess novelty with contextual insight. This tool integrates external APIs such as arXiv and Semantic Scholar with LLMs to support annotation, extraction, retrieval and classification of papers. This combination provides users with a rich, structured view of a scientific idea's core contributions and its connections to existing work. GraphMind is available at https://oyarsa.github.io/graphmind and a demonstration video at https://youtu.be/wKbjQpSvwJg

GraphMind: Interactive Novelty Assessment System for Accelerating Scientific Discovery

Retrieval-Augmented Generation (RAG) has emerged as an effective strategy to ground Large Language Models (LLMs) with reliable, real-time information. This paper investigates the trade-off between cost and performance by evaluating 13 LLMs within a RAG pipeline for the Question Answering (Q&A) task under noisy retrieval conditions. We assess four extractive and nine generative models—spanning both open- and closed-source ones of varying sizes—on a journalistic benchmark specifically designed for RAG. By systematically varying the level of noise injected into the retrieved context, we analyze not only which models perform best, but also their robustness to noisy input. Results show that large open-source generative models (approx. 70B parameters) achieve performance and noise tolerance on par with top-tier closed-source models. However, their computational demands limit their practicality in resource-constrained settings. In contrast, medium-sized open-source models (approx. 7B parameters) emerge as a compelling compromise, balancing efficiency, robustness, and accessibility.

A Comprehensive Evaluation of Large Language Models for Retrieval-Augmented Generation under Noisy Conditions

This paper presents a comprehensive study on the multilingual adaptability of large language models (LLMs), with a focus on the interplay between training strategies and prompt design. Using Thai as a case study, we examine: (RQ1) the extent to which pre-trained models (Base) can adapt to another language through additional fine-tuning; (RQ2) how continual pre-training (CPT) compares to multilingual pre-training (MLLM) in terms of performance on downstream tasks; and (RQ3) how language variation within different components of a structured prompt--task instruction, context input, and output instruction--influences task performance in cross-lingual settings. Our findings reveal that CPT proves to be a promising strategy for enhancing model performance in languages other than English like Thai in monolingual settings, particularly for models that initially lack strong linguistic capabilities. Its effectiveness, however, is highly task-dependent and varies based on the base model's initial proficiency. In cross-lingual scenarios, MLLMs exhibit superior robustness compared to Base and CPT models, which are more susceptible to context-output language mismatches. Considering the high cost of training multilingual models from scratch, MLLMs remain a critical component for downstream tasks in multilingual settings due to their strong cross-lingual performance.

Language Confusion and Multilingual Performance: A Case Study of Thai-Adapted Large Language Models

Hallucinations of vision-language models (VLMs), which are misalignments between visual content and generated text, undermine the reliability of VLMs. One common approach for detecting them employs the same VLM, or a different one, to assess generated outputs. This process is computationally intensive and increases model latency. In this paper, we explore an efficient on-the-fly method for hallucination detection by training traditional ML models over signals based on the VLM's next-token probabilities (NTPs). NTPs provide a direct quantification of model uncertainty. We hypothesize that high uncertainty (i.e., a low NTP value) is strongly associated with hallucinations. To test this, we introduce a dataset of 1,400 human-annotated statements derived from VLM-generated content, each labeled as hallucinated or not, and use it to test our NTP-based lightweight method. Our results demonstrate that NTP-based features are valuable predictors of hallucinations, enabling fast and simple ML models to achieve performance comparable to that of strong VLMs. Furthermore, augmenting these NTPs with linguistic NTPs, computed by feeding only the generated text back into the VLM, enhances hallucination detection performance. Finally, integrating hallucination prediction scores from VLMs into the NTP-based models led to better performance than using either VLMs or NTPs alone. We hope this study paves the way for simple, lightweight solutions that enhance the reliability of VLMs. All data is publicly available at https://huggingface.co/datasets/wrom/Language-Vision-Hallucinations.

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Simulating Training Data Leakage in Multiple-Choice Benchmarks for LLM Evaluation

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Next from IJCNLP-AACL 2025

Simulating Training Data Leakage in Multiple-Choice Benchmarks for LLM Evaluation

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Testing the Effect of Code Documentation on Large Language Model Code Understanding

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