India

With the rise of Large Language Models (LLMs) and their vision-enabled counterparts (VLMs), numerous works have investigated their capabilities in different tasks that fuse both vision and language modalities. In this work, we benchmark the extent to which VLMs are able to act as highly-trained phoneticians, interpreting spectrograms and waveforms of speech. To do this, we synthesise a novel dataset containing 4k+ English words spoken in isolation alongside stylistically consistent spectrogram and waveform figures. We test the ability of VLMs to understand these representations of speech through a multiple-choice task whereby models must predict the correct phonemic or graphemic transcription of a spoken word when presented amongst 3 distractor transcriptions that have been selected based on their phonemic edit distance to the ground truth. We observe that both zero-shot and finetuned models rarely perform above chance, demonstrating the difficulty of this task stemming from the requirement for esoteric parametric knowledge of how to interpret such figures, rather than paired samples alone.

IJCNLP-AACL 2025

Seeing isn’t Hearing: Benchmarking Vision Language Models at Interpreting Spectrograms

vlms

phonetics

spectrograms

phonology

data-to-text

## Welcome to EMNLP 2024! 
We are excited to welcome you to one of the most prominent conferences in the field of Natural Language Processing. This year, EMNLP 2024 is being held in a hybrid format,
offering both virtual and in-person participation in beautiful Miami. Due to a record-breaking number of submissions, we've expanded the total number of accepted papers to accommodate more cutting-edge research from around the globe.
### [Conference Handbook](https://drive.google.com/file/d/1WPROgxjLAC96AJL7Ugy0tEnYm7dkrbHt/view?usp=sharing)

You are required to register for this event. **Please register [here](https://2024.emnlp.org/registration/).** The EMNLP 2024 event page on Underline will be open to public one week prior to the event.

Please register!

EMNLP 2024

EMNLP 2024 will take place in Miami, Florida from Nov 12th to Nov 16th, 2024, at the Hyatt Regency Miami Hote and on Underline for remote participants.

poster

### 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.

Stuttering is a complex speech disorder that challenges both ASR systems and clinical assessment. We propose a multimodal stuttering detection and classification model that integrates acoustic and linguistic features through a two-stage fusion mechanism. Fine-tuned Wav2Vec 2.0 and HuBERT extract acoustic embeddings, which are early fused with MFCC features to capture fine-grained spectral and phonetic variations, while Llama-2 embeddings from Whisper ASR transcriptions provide linguistic context. To enhance robustness against out-of-distribution speech patterns, we incorporate Retrieval-Augmented Generation or adaptive classification. Our model achieves state-of-the-art performance on SEP-28k and FluencyBank, demonstrating significant improvements in detecting challenging stuttering events. Additionally, our analysis highlights the complementary nature of acoustic and linguistic modalities, reinforcing the need for multimodal approaches in speech disorder detection.

StuD: A Multimodal Approach for Stuttering Detection with RAG and Fusion Strategies

Automatic pronunciation assessment (APA) seeks to quantify a second language (L2) learner's pronunciation proficiency in a target language by offering timely and fine-grained diagnostic feedback. Most existing efforts on APA have predominantly concentrated on highly constrained reading-aloud tasks (where learners are prompted to read a reference text aloud); however, assessing pronunciation quality in unscripted speech (or free-speaking scenarios) remains relatively underexplored. In light of this, we first propose HiPPO, a hierarchical pronunciation assessment model tailored for spoken languages, which evaluates an L2 learner’s oral proficiency at multiple linguistic levels based solely on the speech uttered by the learner. To improve the overall accuracy of assessment, a contrastive ordinal regularizer and a curriculum learning strategy are introduced for model training. The former aims to generate score-discriminative features by exploiting the ordinal nature of regression targets, while the latter gradually ramps up the training complexity to facilitate the assessment task that takes unscripted speech as input. Experiments conducted on the Speechocean762 benchmark dataset validates the feasibility and superiority of our method in relation to several cutting-edge baselines.

HiPPO: Exploring A Novel Hierarchical Pronunciation Assessment Approach for Spoken Languages

Modern speech applications require compact embeddings that generalize across both linguistic and paralinguistic tasks. However, most existing embeddings are task-specific and fail to transfer effectively across domains. We propose wavCSE, a feature-based multi-task learning model that produces a fixed-size unified speech embedding suitable for both linguistic and paralinguistic tasks. wavCSE is jointly trained on keyword spotting, speaker identification, and emotion recognition, achieving state-of-the-art performance on all three tasks. The resulting unified embedding is then evaluated on twelve downstream tasks spanning both linguistic and paralinguistic domains. Experimental results show that it outperforms strong baselines on nine of the twelve tasks, indicating effective generalization across domains. To streamline embedding generation, we introduce a recursive layer selection strategy that identifies the most informative hidden layer outputs from the upstream model and refine how these selected outputs are aggregated in the downstream model. These enhancements reduce memory usage and computational cost while improving task performance, making them broadly applicable to self-supervised learning-based speech processing models.

wavCSE: Learning Fixed-size Unified Speech Embeddings via Feature-based Multi-Task Learning

The rapid progress and widespread availability of text-to-image (T2I) generation models have heightened concerns about the misuse of AI-generated visuals, particularly in the context of misinformation campaigns. Existing AI-generated image detection (AGID) methods often overfit to known generators and falter on outputs from newer or unseen models. To systematically address this generalization gap, we introduce the Visual Counter Turing Test (VCT^2), a comprehensive benchmark of 166,000 images, comprising both real and synthetic prompt-image pairs produced by six state-of-the-art (SoTA) T2I systems: Stable Diffusion 2.1, SDXL, SD3 Medium, SD3.5 Large, DALL·E 3, and Midjourney 6. We curate two distinct subsets: COCO_AI, featuring structured captions from MS COCO, and Twitter_AI, containing narrative-style tweets from The New York Times. Under a unified zero-shot evaluation, we benchmark 17 leading AGID models and observe alarmingly low detection accuracy, 58% on COCO_AI and 58.34% on Twitter_AI. To transcend binary classification, we propose the Visual AI Index (V_AI), an interpretable, prompt-agnostic realism metric based on twelve low-level visual features, enabling us to quantify and rank the perceptual quality of generated outputs with greater nuance. Correlation analysis reveals a moderate inverse relationship between V_AI and detection accuracy: Pearson rho of -0.532 on COCO_AI and rho of -0.503 on Twitter_AI; suggesting that more visually realistic images tend to be harder to detect, a trend observed consistently across generators. We release COCO_AI and Twitter_AI to catalyze future advances in robust AGID and perceptual realism assessment.

The Visual Counter Turing Test (VCT²): A Benchmark for Evaluating AI-Generated Image Detection and the Visual AI Index (V_AI)

We introduce InterChart, a diagnostic benchmark that evaluates how well vision-language models (VLMs) reason across multiple related charts, a task central to real-world applications such as scientific reporting, financial analysis, and public policy dashboards. Unlike prior benchmarks focusing on isolated, visually uniform charts, InterChart challenges models with diverse question types ranging from entity inference and trend correlation to numerical estimation and abstract multi-step reasoning grounded in 2–3 thematically or structurally related charts. We organize the benchmark into three tiers of increasing difficulty: (1) factual reasoning over individual charts, (2) integrative analysis across synthetically aligned chart sets, and (3) semantic inference over visually complex, real-world chart pairs. Our evaluation of state-of-the-art open- and closed-source VLMs reveals consistent and steep accuracy declines as chart complexity increases. We find that models perform better when we decompose multi-entity charts into simpler visual units, underscoring their struggles with cross-chart integration. By exposing these systematic limitations, InterChart provides a rigorous framework for advancing multimodal reasoning in complex, multi-visual environments.

INTERCHART: Benchmarking Visual Reasoning Across Decomposed and Distributed Chart Information

Instruction-tuned large language models (LLMs) have shown strong performance on a variety of tasks; however, generalizing from synthetic to human-authored instructions in grounded environments remains a challenge for them. In this work, we study generalization challenges in spatial grounding tasks where models interpret and translate instructions for building object arrangements on a 2.5D grid. We fine-tune LLMs using only synthetic instructions and evaluate their performance on a benchmark dataset containing both synthetic and human-authored instructions. Our results reveal that while models generalize well on simple tasks, their performance degrades significantly on more complex tasks. We present a detailed error analysis of the gaps in instruction generalization.

From Templates to Natural Language: Generalization Challenges in Instruction-Tuned LLMs for Spatial Reasoning

As large language models (LLMs) are increasingly deployed in enterprise settings, controlling model behavior based on user roles becomes an essential requirement. Existing safety methods typically assume uniform access and focus on preventing harmful or toxic outputs, without addressing role-specific access constraints. In this work, we investigate whether LLMs can be fine-tuned to generate responses that reflect the access privileges associated with different organizational roles. We explore three modeling strategies: a BERT-based classifier, an LLM-based classifier, and role-conditioned generation. To evaluate these approaches, we construct two complementary datasets. The first is adapted from existing instruction-tuning corpora through clustering and role labeling, while the second is synthetically generated to reflect realistic, role-sensitive enterprise scenarios. We assess model performance across varying organizational structures and analyze robustness to prompt injection, role mismatch, and jailbreak attempts.

Role-Aware Language Models for Secure and Contextualized Access Control in Organizations

Identifying relevant text spans is important for several downstream tasks in NLP, as it contributes to model explainability. While most span identification approaches rely on relatively smaller pre-trained language models like BERT, a few recent approaches have leveraged the latest generation of Large Language Models (LLMs) for the task. Current work has focused on explicit span identification like Named Entity Recognition (NER), while more subjective span identification with LLMs in tasks like Aspect-based Sentiment Analysis (ABSA) has been underexplored. In this paper, we fill this important gap by presenting an evaluation of the performance of various LLMs on text span identification in three popular tasks, namely sentiment analysis, offensive language identification, and claim verification. We explore several LLM strategies like instruction tuning, in-context learning, and chain of thought. Our results indicate underlying relationships within text aid LLMs in identifying precise text spans.

Exploring the Performance of Large Language Models on Subjective Span Identification Tasks

Reasoning Large Language Models (LLMs) with enhanced accuracy and explainability are increasingly being adopted in the medical domain, as the life-critical nature of clinical decision-making demands reliable support. Despite these advancements, existing reasoning LLMs often generate unnecessarily lengthy reasoning processes, leading to significant computational overhead and response latency. These limitations hinder their practical deployment in real-world clinical environments. To address these challenges, we introduce \textbf{ControlMed}, a medical language model that enables users to actively control the length of the reasoning process at inference time through fine-grained control markers. ControlMed is trained through a three-stage pipeline: 1) pre-training on a large-scale synthetic medical instruction dataset covering both \textit{direct} and \textit{reasoning responses}; 2) supervised fine-tuning with multi-length reasoning data and explicit length-control markers; and 3) reinforcement learning with model-based reward signals to enhance factual accuracy and response quality. Experimental results on a variety of English and Korean medical benchmarks demonstrate that our model achieves similar or better performance compared to state-of-the-art models. Furthermore, users can flexibly balance reasoning accuracy and computational efficiency by controlling the reasoning length as needed. These findings demonstrate that ControlMed is a practical and adaptable solution for clinical question answering and medical information analysis.

ControlMed: Adding Reasoning Control to Medical Language Model

Grammar competency estimation is essential for assessing linguistic proficiency in both written and spoken language; however, the spoken modality presents additional challenges due to its spontaneous, unstructured, and disfluent nature. Developing accurate grammar scoring models further requires extensive expert annotation, making large-scale data creation impractical. To address these limitations, we propose a zero-shot grammar competency estimation framework that leverages unlabeled data and Large Language Models (LLMs) without relying on manual labels. During training, we employ LLM-generated predictions on unlabeled data by using grammar competency rubric-based prompts. These predictions, treated as pseudo labels, are utilized to train a transformer-based model through a novel training framework designed to handle label noise effectively. We show that the choice of LLM for pseudo-label generation critically affects model performance and that the ratio of clean-to-noisy samples during training strongly influences stability and accuracy. Finally, a qualitative analysis of error intensity and score prediction confirms the robustness and interpretability of our approach. Experimental results demonstrate the efficacy of our approach in estimating grammar competency scores with high accuracy, paving the way for scalable, low-resource grammar assessment systems.

Downloads

Next from IJCNLP-AACL 2025

StuD: A Multimodal Approach for Stuttering Detection with RAG and Fusion Strategies

Similar lecture

A Unified Framework and Dataset for Assessing Societal Bias in Vision-Language Models