United States

ACL 2026

ORCHID: Orchestrated Retrieval-Augmented Classification of High-Risk Property with Intelligent Decision-Making

workshop paper

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. <br>
[Full Message from General Chair Philipp Koehn](https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing) <br><br><br>
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The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

A Pipeline to Bootstrap the Evaluation of Retrieval-Augmented Generation for the Automation of Systematic Reviews in Computer Science

Crucible@Rag4Reports: Using Nuggets for Evaluation and RAG

AMU at RAG4Reports 2026 Task B: A Practical Multilingual RAG Pipeline for Citation-Grounded Reports

Exploring Capability Thresholds in Ultra-Lightweight LLM Judges for Nugget-Based Report Evaluation

EFSG: Evidence-First Structured Generation for Multilingual RAG Report Generation

JU-NLP-PG at RAG4Reports 2026: Memory-Efficient Multilingual Report Generation with 4-bit Quantized LLMs

Quantization methods are widely used to accelerate inference and streamline the deployment of large language models (LLMs). Although quantization's effects on various LLM capabilities have been extensively studied, one critical area remains underexplored: factual knowledge recall (FKR), the process by which LLMs access stored knowledge. To this end, we conduct comprehensive experiments using three common quantization techniques at distinct bit widths, in conjunction with interpretability-driven analyses on two tasks, knowledge memorization and latent multi-hop reasoning. We show that quantization typically results in information loss within LLMs, consequently diminishing their capacity for FKR. This effect is particularly amplified in smaller models within the same architectural families. However, models quantized at reduced bit precision do not consistently exhibit inferior performance and occasionally quantization may even enhance model FKR. We find that BitSandBytes demonstrates highest preservation of the original full-precision model's FKR. Despite variability across models and methods, quantization causes modest performance degradation and remains an effective compression strategy.

Through a Compressed Lens: Investigating The Impact of Quantization on Factual Knowledge Recall

Recent research has increasingly focused on training large language models (LLMs) using federated learning, known as FedLLM. However, responsible AI (RAI)—which aims to ensure safe and trustworthy responses—remains underexplored in this context. In FedLLM, client-side training data may contain harmful content, resulting in unsafe LLMs that can generate inappropriate responses. Aggregating such models into a global model and redistributing it to clients risks the widespread deployment of unsafe LLMs. To address this, we incorporate two well-established RAI techniques into FedLLM: safety filtering and constitutional AI. Our experiments show that these methods significantly improve LLM safety, achieving over 20% improvement on AdvBench.

Responsible Federated LLMs via Safety Filtering and Constitutional AI

Detecting media bias is increasingly critical in today’s fast-evolving digital landscape. While large language models (LLM) show promise in bias detection, their reasoning is often unreliable, producing opaque errors. To address this, we propose an {\bf uncertainty-aware proxy reasoning method} that combines proxy attribute prediction with uncertainty estimation to assess and stratify reasoning quality. By modeling uncertainty, our method reliably narrows down the intermediate reasoning space and highlights trustworthy bias indicators, enhancing transparency and interpretability for end users and downstream applications. We evaluate our method on the BASIL benchmark and compare it against strong LLM baselines and recent state-of-the-art approaches. Our experiments assess both decision accuracy and quality. Results show a 6.25% relative F1 improvement over competitive baselines, demonstrating that uncertainty-aware reasoning boosts performance and provides insight into the decision-making process, making media bias detection more reliable and interpretable.

Uncertainty-Aware Proxy Attribute Reasoning for Reliable Media Bias Detection

Large language models (LLMs) are increasingly deployed in wide range of applications, yet remain vulnerable to adversarial jailbreak attacks that circumvent their safety guardrails.
Existing evaluation frameworks typically report binary success/failure metrics, failing to capture the temporal dynamics of how attacks succeed under persistent adversarial pressure. This preliminary work proposes a novel evaluation framework that applies survival analysis techniques to characterize LLM jailbreak vulnerability. Our approach models the “time-to-jailbreak” as a survival outcome, enabling estimation of hazard functions, survival curves, and risk factors associated with successful attacks. We evaluate three LLMs against a sub-set of prompts from the HarmBench dataset spanning three attack categories. Our analysis reveals that models exhibit distinct vulnerability profiles: while one model demonstrates rapid degradation under iterative attacks, the wo other models show consistent moderate vulnerability. Our framework provides actionable insights for model and LLM applicaiton developers and establishes survival analysis as a rigorous methodology for LLM safety evaluation.

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Next from ACL 2026

A Pipeline to Bootstrap the Evaluation of Retrieval-Augmented Generation for the Automation of Systematic Reviews in Computer Science

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PRESENTATIONS

CONFERENCES

COMPANY

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Next from ACL 2026

A Pipeline to Bootstrap the Evaluation of Retrieval-Augmented Generation for the Automation of Systematic Reviews in Computer Science

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads