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Quantization methods are widely used to accelerate inference and streamline the deployment of large language models (LLMs). Although quantization&#39;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&#39;s FKR. Despite variability across models and methods, quantization causes modest performance degradation and remains an effective compression strategy.

ACL 2026

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

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.

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.

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.

Quantifying LLM Safety Degradation Under Repeated Attacks Using Survival Analysis

Verifying complex real-world claims against diverse and potentially unreliable open-web sources requires balancing evidence comprehensiveness with rigorous source reliability. Current automated fact-checking approaches often fail to address this holistically, losing contextual dependencies and applying trust signals monolithically at the document level.

We introduce ClaimCLAIRE, a multi-component fact-checking agent that integrates four key innovations: (1) iterative component-aware decomposition with exhaustiveness validation, (2) holistic evidence gathering using a ReAct agent that maintains cross-component semantic awareness, (3) trust-modulated retrieval that weights evidence by source credibility to mitigate the influence of misinformation, and (4) adaptive gap-filling to address recall bottlenecks in under-supported sub-claims.

Evaluated on the AVeriTeC benchmark, ClaimCLAIRE achieves 84.27% accuracy and a macro-F1 of 0.806. Our systematic ablations demonstrate that while decomposition alone can degrade performance, its integration with trust-aware retrieval and adaptive gap-filling yields a pipeline where component-level verdicts, source trust ratings, and deterministic AND-logic synthesis together support transparent, accountable fact verification.

ClaimCLAIRE: A Trust-Aware Multi-Component Fact-Checking Agent for Open-World Claims

This paper introduces ChatbotManip, a novel dataset for studying manipulation in Chatbots. It contains simulated generated conversations between a chatbot and a (simulated) user, where the chatbot is explicitly asked to showcase manipulation tactics, persuade the user towards some goal, or simply be helpful. We consider a diverse set of chatbot manipulation contexts, from consumer and personal advice to citizen advice and controversial proposition argumentation. Each conversation is annotated by human annotators for both general manipulation and specific manipulation tactics. Our research reveals three key findings. First, Large Language Models (LLMs) can be manipulative when explicitly instructed, with annotators identifying manipulation in approximately 84% of such conversations. Second, even when only instructed to be ``persuasive'' without explicit manipulation prompts, LLMs frequently default to controversial manipulative strategies, particularly Gaslighting and Fear Enhancement. Third, zero-shot larger models such as Gemini 2.5 pro have the best performance in detecting manipulation (of the models tested), with more work required to fine-tune smaller open source models for real-world on-device oversight. Our work provides important insights for AI safety research and highlights the need of addressing manipulation risks as LLMs are increasingly deployed in consumer-facing applications.

ChatbotManip: a Dataset to Facilitate Evaluation and Oversight of Manipulative Chatbot Behaviour

The fairness-accuracy trade-off is a key challenge in NLP tasks. Current work focuses on finding a single optimal solution to balance the two objectives, which is limited considering the diverse solutions on the Pareto front.
This work intends to provide controllable trade-offs according to the user's preference of the two objectives, which is defined as a reference vector. 
To achieve this goal, we apply multi-objective optimization (MOO), which can find solutions from various regions of the Pareto front. 
However, it is challenging to precisely control the trade-off due to the stochasticity of the training process and the high dimensional gradient vectors.
Thus, we propose Controllable Pareto Trade-off (CPT) that can effectively train models to perform different trade-offs according to users' preferences.
CPT 1) stabilizes the fairness update with a moving average of stochastic gradients to determine the update direction, and 2) prunes the gradients by only keeping the gradients of the critical parameters. 
We evaluate CPT on hate speech detection and occupation classification tasks. Experiments show that CPT can achieve a higher-quality set of solutions on the Pareto front than the baseline methods. It also exhibits better controllability and can precisely follow the human-defined reference vectors.

Controllable Pareto Trade-off between Fairness and Accuracy

As the influence of LLMs expands, it is imperative to gain insight into their decisions. One way to do that is to develop probes that detect the presence or absence of a broad set of concepts within the embeddings computed in an LLM - which is what we might say a model is ``thinking" about. Such probes should be low-cost and easily applicable to any LLM, so that monitoring for many concepts is possible during normal operation.

In this paper, we take the first steps towards developing the capability of creating many such probes by defining and executing examples of the key tasks needed: first, the careful delineation of a concept through the creation of a dataset with the concept both present and then absent. Then, the training and testing of a set of linear probes to detect the concept on any layer of an LLM, including an exploration of the complexity of the probe needed. Finally, we show that such probes can track concepts across larger contexts. This is done with four separate concepts and three different LLMs. When this process is scaled to many more concepts, it will create the ability to easily monitor new models.

What are They Thinking? Delineation, Probing and Tracking of Concepts in LLMs

Large Language Models (LLMs) are rarely static and are frequently updated in practice. A growing body of alignment research has shown that models initially deemed ``aligned'' can exhibit misaligned behavior after fine-tuning, such as forgetting jailbreak safety features or re-surfacing knowledge that was intended to be forgotten. These works typically assume that the initial model is aligned based on static black-box evaluation, i.e., the absence of undesired responses to a fixed set of queries. In contrast, we formalize model alignment in both the static and post-update settings and uncover a fundamental limitation of black-box evaluation. We theoretically show that, due to overparameterization, static alignment provides no guarantee of post-update alignment for \emph{any} update dataset. Moreover, we prove that static black-box probing cannot distinguish a model that is genuinely post-update robust from one that conceals an arbitrary amount of adversarial behavior, which can be activated by even a single benign gradient update. We further validate these findings empirically in LLMs across three core alignment domains: privacy, jailbreak safety, and behavioral honesty. We demonstrate the existence of LLMs that pass all standard black-box alignment tests, yet become severely misaligned after a single benign update. Finally, we show that the capacity to hide such latent adversarial behavior increases with model scale, confirming our theoretical prediction that post-update misalignment grows with the number of parameters. Together, our results highlight the inadequacy of static evaluation protocols and emphasize the urgent need for post-update--robust alignment evaluation

Hair-Trigger Alignment: Black-Box Evaluation Cannot Guarantee Post-Update Alignment

Trustworthy legal AI requires training data that is expert-verified and grounded in statutory provisions, as fluent generation alone cannot guarantee legally sound and verifiable outputs. However, Korean legal datasets offer no such foundation. To fill this gap, we introduce KoLegalQA, a large-scale Korean legal question–answer corpus designed to promote trustworthy and explanation-based and verifiable reasoning in real-world legal consultation scenarios. The dataset comprises 19,266 consultations collected from government-operated services, with all response authored or verified by licensed legal professionals. Unlike prior resources, KoLegalQA provides explicit statutory references and clause-level summaries, enabling systematic evaluation of transparency, citation-faithfulness, and evidential support in legal language models. We benchmark six Korean-capable LLMs using both automated evaluation (G-Eval) and human assessment across multiple criteria, including legal correctness, reasoning quality, and citation relevance. Experimental results show that fine-tuning on KoLegalQA significantly enhances statute-aligned response generation and mitigates hallucinated claims. We present this resource as a practical benchmark for advancing safe and verifiable legal NLP research. Dataset, code, and evaluation scripts will be publicly released to support reproducible and trustworthy Korean legal NLP research.

KoLegalQA: A Korean Legal QA Dataset for Trustworthy and Explanation-Grounded Legal AI

Retrieval-augmented generation systems serving multiple users under role-based access control face a trustworthiness gap: semantic retrieval operates on embedding similarity rather than authorization predicates and can introduce unauthorized content into a model’s context window before any filter intervenes. We formalize this as a pipeline ordering problem and introduce Authorization-First Retrieval (AFR), an architectural invariant requiring that authorization constrain the retrieval candidate set before any learned component consumes retrieved content. We reduce authorization correctness to the classical noninterference property and prove AFR is necessary whenever the processing model violates noninterference—a condition our experiments confirm empirically. Evaluation on a controlled corpus of 247 chunks across 232 documents with 431 base queries spanning 12 enterprise roles and 9 domains (584 total queries including negation exploitation and parametric probes) shows that retrieve-then-filter pipelines expose unauthorized context in 86.1% of queries, while AFR eliminates structural leaks by construction. 

Cross-model experiments with Gemini 2.0 Flash and GPT-4o-mini reveal that structural exposure is an architectural property independent of the underlying model, whereas behavioral defenses fail at model-dependent rates, producing answer leakage of 41.3% and 29.5% respectively under retrieve-then-filter. A negation exploitation study demonstrates consistent disclosure vulnerabilities across framing types, while a metadata-tag freshness ablation shows that conditional authorization mechanisms degrade under realistic policy staleness. Stress tests across retrieval depths and chunking granularities confirm AFR’s robustness. Our results demonstrate that behavioral guardrails and metadata tagging cannot reliably enforce least privilege in RAG pipelines, while authorization-first architectures provide a verifiable and model-independent security guarantee.

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Responsible Federated LLMs via Safety Filtering and Constitutional AI

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Responsible Federated LLMs via Safety Filtering and Constitutional AI

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