India

Recent advances in speech‑enabled AI, including Google&#39;s NotebookLM and OpenAI&#39;s speech-to-speech API, are driving widespread interest in voice interfaces across sectors such as finance, health, agritech, legal services, and call‑centers in the global north and south. Despite this momentum, there exists no publicly available application-specific model evaluation that caters to Africa&#39;s linguistic diversity. We present $\textbf{AfriSpeech‑MultiBench}$, the first domain‑specific evaluation suite for over 100 African English accents across 10+ countries and seven application domains: Finance, Legal, Medical, General dialogue, Call Center, Named Entities, and Hallucination Robustness. We benchmark a diverse range of open, closed, unimodal ASR and multimodal LLM-based speech recognition systems using both spontaneous and non-spontaneous speech conversations drawn from various open African accented English speech datasets. Our empirical analysis reveals systematic variation: open‑source ASR excels in spontaneous speech contexts but degrades on noisy, non‑native dialogue; multimodal LLMs are more accent‑robust yet struggle with domain‑specific named entities; proprietary models deliver high accuracy on clean speech but vary significantly by country and domain. Smaller models fine‑tuned on African English achieve competitive accuracy with lower latency, a practical advantage for deployment. By releasing this benchmark, we empower practitioners and researchers to select voice technologies suited to African use‑cases, fostering inclusive voice applications for undeserved communities.

IJCNLP-AACL 2025

AfriSpeech-MultiBench: A Verticalized Multidomain Multicountry Benchmark Suite for African Accented English ASR

regionally tuned asr models

accent robustness

domain-specific speech evaluation

african-accented asr benchmarking

named-entity recognition

### 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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NAACL 2025

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

Recent advances in speech‑enabled AI, including Google's NotebookLM and OpenAI's speech-to-speech API, are driving widespread interest in voice interfaces across sectors such as finance, health, agritech, legal services, and call‑centers in the global north and south. Despite this momentum, there exists no publicly available application-specific model evaluation that caters to Africa's linguistic diversity. We present $\textbf{AfriSpeech‑MultiBench}$, the first domain‑specific evaluation suite for over 100 African English accents across 10+ countries and seven application domains: Finance, Legal, Medical, General dialogue, Call Center, Named Entities, and Hallucination Robustness. We benchmark a diverse range of open, closed, unimodal ASR and multimodal LLM-based speech recognition systems using both spontaneous and non-spontaneous speech conversations drawn from various open African accented English speech datasets. Our empirical analysis reveals systematic variation: open‑source ASR excels in spontaneous speech contexts but degrades on noisy, non‑native dialogue; multimodal LLMs are more accent‑robust yet struggle with domain‑specific named entities; proprietary models deliver high accuracy on clean speech but vary significantly by country and domain. Smaller models fine‑tuned on African English achieve competitive accuracy with lower latency, a practical advantage for deployment. By releasing this benchmark, we empower practitioners and researchers to select voice technologies suited to African use‑cases, fostering inclusive voice applications for undeserved communities.

technical 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.

Conventional research on speech recognition modeling relies on the canonical form for most low-resource languages while automatic speech recognition (ASR) for regional dialects is treated as a fine-tuning task. To investigate the effects of dialectal variations on ASR we develop a 78-hour annotated Bengali Speech-to-Text (STT) corpus named Ben-10. Investigation from linguistic and data-driven perspectives shows that speech foundation models struggle heavily in regional dialect ASR, both in zero-shot and fine-tuned settings. We observe that all deep learning methods struggle to model speech data under dialectal variations, but dialect specific model training alleviates the issue. Our dataset also serves as a out-of-distribution (OOD) resource for ASR modeling under constrained resources in ASR algorithms. The dataset and code developed for this project are publicly available.

Are ASR foundation models generalized enough to capture features of regional dialects for low-resource languages?

Predictive modeling of hospital patient data is challenging due to its structured format, irregular timing of measurements, and variation in data representation across institutions. While traditional models often struggle with such inconsistencies, Large Language Models (LLMs) offer a flexible alternative. In this work, we propose a method for verbalizing structured Electronic Health Records (EHRs) into a format suitable for LLMs and systematically examine how to include time-stamped clinical observations—such as lab tests and vital signs—from previous time points in the prompt. We study how different ways of structuring this temporal information affect predictive performance, and whether fine-tuning alone enables LLMs to effectively reason over such data. Evaluated on two real-world hospital datasets and MIMIC-IV, our approach achieves strong in-hospital and cross-hospital performance, laying the groundwork for more generalizable clinical modeling.

Decode Like a Clinician: Enhancing LLM Fine-Tuning with Temporal Structured Data Representation

Large language models (LLMs) are increasingly being used for complex research tasks such as literature review, idea generation, and scientific paper analysis, yet their ability to truly understand and process the intricate relationships within complex research papers, such as the logical links between claims and supporting evidence remains largely unexplored. In this study, we present CLAIM-BENCH, a comprehensive benchmark for evaluating LLMs' capabilities in scientific claim-evidence extraction and validation, a task that reflects deeper comprehension of scientific argumentation. We systematically compare three approaches which are inspired by divide and conquer approaches, across six diverse LLMs, highlighting model-specific strengths and weaknesses in scientific comprehension. Through evaluation involving over 300 claim-evidence pairs across multiple research domains, we reveal significant limitations in LLMs' ability to process complex scientific content. Our results demonstrate that closed-source models like GPT-4 and Claude consistently outperform open-source counterparts in precision and recall across claim-evidence identification tasks. Furthermore, strategically designed three-pass and one-by-one prompting approaches significantly improve LLMs' abilities to accurately link dispersed evidence with claims, although this comes at increased computational cost. CLAIM-BENCH sets a new standard for evaluating scientific comprehension in LLMs, offering both a diagnostic tool and a path forward for building systems capable of deeper, more reliable reasoning across full-length papers.

Can AI Validate Science? Benchmarking LLMs on Claim →Evidence Reasoning in AI Papers

We present a modular, interactive system, SPORTSQL, for natural language querying and visualization of dynamic sports data, with a focus on the English Premier League (EPL). The system translates user questions into executable SQL over a live, temporally indexed database constructed from real-time Fantasy Premier League (FPL) data. It supports both tabular and visual outputs, leveraging symbolic reasoning capabilities of Large Language Models (LLMs) for query parsing, schema linking, and visualization selection. To evaluate system performance, we introduce the Dynamic Sport Question Answering Benchmark (DSQABENCH), comprising 1,700+ queries annotated with SQL programs, gold answers, and database snapshots. Our demo highlights how non-expert users can seamlessly explore evolving sports statistics through a natural, conversational interface.

SPORTSQL: An Interactive System for Real-Time Sports Reasoning and Visualization

Large Language Models (LLMs) have been positioned as having the potential to expand access to health information in the Global South, yet their evaluation remains heavily dependent on benchmarks designed around Western norms. We present insights from a preliminary benchmarking exercise with a chatbot for sexual and reproductive health (SRH) for an underserved community in India. We evaluated using HealthBench, a benchmark for conversational health models by OpenAI. We extracted 637 SRH queries from the dataset and evaluated on the 330 single-turn conversations. Responses were evaluated using HealthBench's rubric-based automated grader, which rated responses consistently low. However, qualitative analysis by trained annotators and public health experts revealed that many responses were actually culturally appropriate and medically accurate. We highlight recurring issues, particularly a Western bias, such as for legal framing and norms (e.g., breastfeeding in public), diet assumptions (e.g., fish safe to eat during pregnancy), and costs (e.g., insurance models). Our findings demonstrate the limitations of current benchmarks in capturing the effectiveness of systems built for different cultural and healthcare contexts. We argue for the development of culturally adaptive evaluation frameworks that meet quality standards while recognizing needs of diverse populations.

Beyond the Rubric: Cultural Misalignment in LLM Benchmarks for Sexual and Reproductive Health

Large language models (LLMs) are now used in scientific peer review, but their judgments can still be influenced by how information is presented.
We study how the style of a paper’s title affects the way LLMs score scientific work.
To control for content variation, we build the TitleTrap benchmark using abstracts generated by a language model for common research topics in computer vision and NLP.
Each abstract is paired with three titles: a branded colon style, a plain descriptive style, and an interrogative style, while the abstract text remains fixed.
We ask GPT-4o and Claude to review these title–abstract pairs under the same instructions.
Our results show that title style alone can change the scores: branded titles often receive higher ratings, while interrogative titles sometimes lead to lower assessments of rigor.
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.

TitleTrap: Probing Presentation Bias in LLM-Based Scientific Reviewing

We propose a range of automated metrics for evaluation of generated poetry. The metrics measure various aspects of poetry: rhyming, metre, syntax, semantics, and amount of unknown words. In a case study, we implement the metrics for Czech language, apply them to poetry generated by several automated systems as well as human-written, and correlate them with human judgment. We find that most of the proposed metrics correlate well with corresponding human evaluation, but semantically oriented metrics are much better predictors of the overall impression than metrics evaluating formal properties.

Evaluation of Generated Poetry

Large Language Models (LLMs) with extended context windows promise direct reasoning over long documents, reducing the need for chunking or retrieval. Constructing annotated resources for training and evaluation, however, remains costly. Synthetic data offers a scalable alternative, and we introduce SynClaimEval, a framework for evaluating synthetic data utility in long-context claim verification—a task central to hallucination detection and fact-checking. Our framework examines three dimensions: (i) input characteristics, by varying context length and testing generalization to out-of-domain benchmarks; (ii) synthesis logic, by controlling claim complexity and error type variation; and (iii) explanation quality, measuring the degree to which model explanations provide evidence consistent with predictions. Experiments across benchmarks show that long-context synthesis can improve verification in base instruction-tuned models, particularly when augmenting existing human-written datasets. Moreover, synthesis enhances explanation quality, even when verification scores don't improve, underscoring its potential to strengthen both performance and explainability.

SynClaimEval: A Framework for Evaluating the Utility of Synthetic Data in Long-Context Claim Verification

Existing benchmarks evaluating biases in large language models (LLMs) primarily rely on explicit cues, declaring protected attributes like religion, race, gender by name. However, real-world interactions often contain implicit biases, inferred subtly through names, cultural cues, or traits. This critical oversight creates a significant blind spot in fairness evaluation. We introduce ImplicitBBQ, a benchmark extending the Bias Benchmark for QA (BBQ) with implicitly cued protected attributes across 6 categories. Our evaluation of GPT-4o on ImplicitBBQ illustrates troubling performance disparity from explicit BBQ prompts, with accuracy declining up to 7% in the "sexual orientation" subcategory and consistent decline located across most other categories. This indicates that current LLMs contain implicit biases undetected by explicit benchmarks. ImplicitBBQ offers a crucial tool for nuanced fairness evaluation in NLP.

“The dentist is an involved parent, the bartender is not”: Revealing Implicit Biases in QA with Implicit BBQ

Similar to human intelligence, which is highly complex in nature, evaluating large language models becomes especially challenging when they move beyond well-defined, STEM-style tasks into socially and culturally rich domains. The first part of this talk focuses on assessing social intelligence in LLMs, exploring their ability to handle phenomena where ambiguity, cultural difference, and subjectivity make “correctness” difficult to define, and where capabilities beyond text are required, such as omni-modal sensory understanding. The talk then examines the role of LLMs as evaluators, considering their reliability, biases, and prompt sensitivity, and concludes with reflections on building more robust and socially grounded evaluation frameworks.

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Are ASR foundation models generalized enough to capture features of regional dialects for low-resource languages?

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