India

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&#39; 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&#39; 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&#39; 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.

IJCNLP-AACL 2025

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

statistical testing for evaluation

nlp datasets

automatic evaluation of datasets

evaluation methodologies

reproducibility

benchmarking

metrics

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

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AAAI 2026

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

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.

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.

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

This study explores the potential of a lightweight, open-source Large Language Model (LLM), demonstrating how its integration with Retrieval-Augmented Generation (RAG) can support cost-effective evaluation of revision quality and writing style differentiation. By retrieving reference documents from a carefully chosen and constructed corpus of peer-reviewed conference proceedings, our framework leverages few-shot in-context learning to track manuscript revisions and venue-specific writing styles. We demonstrate that the LLM-based evaluation aligns closely with human revision histories—consistently recognizing quality improvements across revision stages and distinguishing writing styles associated with different conference venues. These findings highlight how a carefully designed evaluation framework, integrated with adequate, representative data, can advance automated assessment of scientific writing.

Between the Drafts: An Evaluation Framework for Identifying Quality Improvement and Stylistic Differences in Scientific Texts

Language corpora are the foundation of most natural language processing research, yet they often reproduce structural inequalities. One such inequality is gender discrimination in how actors are represented, which can distort analyses and perpetuate discriminatory outcomes. This paper introduces a user-centric, actor-level pipeline for detecting and mitigating gender discrimination in large-scale text corpora. By combining discourse-aware analysis with metrics for sentiment, syntactic agency, and quotation styles, our method enables both fine-grained auditing and exclusion-based balancing. Applied to the taz2024full corpus of German newspaper articles (1980–2024), the pipeline yields a more gender-balanced dataset while preserving core dynamics of the source material. Our findings show that structural asymmetries can be reduced through systematic filtering, though subtler biases in sentiment and framing remain. We release the tools and reports to support further research in discourse-based fairness auditing and equitable corpus construction.

Fair Play in the Newsroom: Actor-Based Filtering Gender Discrimination in Text Corpora

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.

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

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

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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Next from IJCNLP-AACL 2025

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

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