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Large language models (LLMs) are widely used in decision-making across diverse domains. Ensuring the generation of safe and reliable responses is critical for the effective deployment of LLM-based applications, particularly in high-stakes domains such as healthcare and finance. Most of these applications typically use carefully crafted prompts to guide response generation; however, the relationship between prompts and the reliability of LLM-generated responses is not yet fully understood. To address this gap, we propose a novel prompt-response concept model that explains the relationship between the amount of task-relevant information (informativeness) provided in the prompt and the LLM-generated response uncertainty by identifying four sources of response uncertainty: prompt underspecification, model quality, task variability, and semantic redundancy. We prove that response uncertainty decreases as prompt informativeness or model quality increases, mirroring the behavior of epistemic uncertainty in probabilistic models. Our experimental results on real-world datasets further validate our proposed model and corroborate the theoretical results.

ACL 2026

Prompting the Unknown: Understanding Response Uncertainty in Large Language Models

poster

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.

Prior work has shown that instruction-tuned large language models (LLMs) are less well calibrated than their base pre-trained counterparts. However, little is known about the frequently used chat template’s effect on the calibration of conversational LLMs. In this work, we investigate the mechanisms driving this miscalibration by decoupling the effects of the post-training algorithm and the chat format. We find that, while instruction tuning fundamentally harms calibration, the chat template aggravates the issue through an “ownership bias” – models are significantly more confident in their *own* answers than in identical answers provided by a user. Extensive experiments across six recent open-weight LLMs, three benchmarks, and three confidence elicitation methods show that models assign up to 26% higher confidence to their own responses. Leveraging this insight, we propose a simple inference-time strategy: framing the model's answer as user input during confidence elicitation. This approach significantly reduces overconfidence and improves calibration by up to 26% without the need for retraining, narrowing the gap between base and instruction-tuned models.

Large Language Models Are Overconfident in Their Own Responses

Retrieval-Augmented Generation (RAG) is widely used for question answering over well-structured document corpora. However, a large amount of real-world problem-solving knowledge is captured in goal-oriented dialogues, where common ground misalignment between users and helpers gives rise to sparse, diffuse, and dynamically refined evidence that challenges standard RAG pipelines. We propose Structured Dialogue Refinement (SDR), a unified framework that adapts dialogue corpora for RAG at both the retrieval and generation stages without altering the underlying pipeline. Specifically, SDR introduces Dual Dialogue Querying for intent-aligned retrieval via issue-centric and solution-centric pseudo-documents, and Graph-Structured Dialogues coupled with a relevance-driven subgraph selection strategy to enable effective utilization of conversational evidence. We further adopt a nugget-based evaluation setup for dialogue-grounded RAG, enabling fine-grained analysis of retrieval coverage and grounded answer generation. Experiments demonstrate that SDR substantially improves both retrieval quality and grounded QA performance under dialogue-specific structural challenges.

Structured Dialogue Refinement: Building Retrieval-Augmented Question Answering on Goal-Oriented Dialogues

Prompt tuning has achieved remarkable progress in vision–language models (VLMs) and is recently being adopted for audio–language models (ALMs). However, its generalization ability in ALMs remains largely underexplored. We observe that conventional prompt tuning for ALMs also suffers from the Base–New Tradeoff, and we identify that this issue stems from the disrupted semantic structure of the embedding space. To address this issue, we propose Semantically Expanded Prompt Tuning (SEPT)—a plug-and-play framework that explicitly regularizes the prompt embedding space by incorporating semantic neighbors generated by large language models. SEPT introduces a novel semantic expansion loss with margin constraints that promote intra-class compactness and inter-class separability, thereby enhancing the semantic structure of the prompt embedding space. For comprehensive evaluation, we establish the first benchmark setup for prompt generalization in ALMs, covering both base-to-new generalization and cross-dataset transferability. Extensive experiments demonstrate that SEPT consistently improves generalization performance across multiple prompt tuning baselines, while maintaining computational cost during inference.

Generalizable Prompt Tuning for Audio-Language Models via Semantic Expansion

Memory systems often organize user-agent interactions as retrievable external memory and are crucial for long-running agents by overcoming the limited context windows of LLMs. However, existing memory systems invoke LLMs to process every incoming interaction for memory extraction, and such an eager memory consolidation scheme leads to substantial token consumption. To tackle this problem, we propose RecMem by rethinking when memory consolidation should be conducted. RecMem stores incoming interactions in a subconscious memory layer and encode them using lightweight embedding models for retrieval. LLMs are only invoked to extract episodic and semantic memory when sustained recurrence are observed for semantically similar interactions. Such recurrence-based consolidation works because these interactions correspond to a semantic cluster with rich information and thus are worth extraction and summarization. To improve accuracy, RecMem also incorporates a semantic refinement mechanism that recovers the fine-grained facts omitted by memory extraction. Experiments show that RecMem reduces the memory construction token cost of three SOTA memory systems by up to 87% while exceeding their accuracy.

RecMem: Recurrence-based Memory Consolidation for Efficient and Effective Long-Running LLM Agents

Large Language Models (LLMs) remain vulnerable to jailbreak attacks despite extensive safety alignment. While automated red-teaming has emerged as a critical evaluation protocol, existing methods face two primary limitations: they largely explore homogeneous transformations in isolation, and they rely on brittle judgment metrics that frequently misclassify non-refusal hallucinations as successful attacks. In this paper, we reformulate jailbreak attacks as a compositional search problem guided by context-aware evaluation. We propose Chimera, a framework that generates compositional jailbreak attacks via judgment-driven search over heterogeneous strategies. Chimera systematically explores the combinatorial space of disjoint primitives, such as integrating technical obfuscation with semantic persuasion, under strict ordering constraints. Crucially, to drive the search process effectively, we introduce StrongREJECT++, a relevance-aware metric that eliminates false positive rewards by penalizing irrelevant responses. Experiments on multiple open-source and commercial LLMs show that Chimera uncovers qualitatively different vulnerability regions and consistently improves attack success rates and transferability compared to state-of-the-art baselines.

Chimera: Compositional Jailbreak Attacks on LLMs via Judgment-Driven Search over Heterogeneous Strategies

We propose DPLoRA (Dual-Pruning Low-Rank Adaptation), a framework that optimizes rank allocation via two stages: (1) an initial pruning stage (OPLoRA; Optimal Pruning LoRA) that uses Integer Linear Programming (ILP) to determine optimal layer-wise ranks without manual tuning; and (2) a progressive pruning stage that further reduces ranks adaptively during training using importance scores smoothed by Exponential Moving Average (EMA). Experiments demonstrate that OPLoRA consistently outperforms existing PEFT baselines on GLUE and instruction-following tasks, while the full DPLoRA framework establishes a new state-of-the-art among compared PEFT baselines on GLUE at high-performance settings ($p=0.4$). At efficiency-focused settings ($p=0.8$), our method reduces trainable parameters by over 80\% and training time by 46\% compared to standard LoRA, offering a highly efficient solution for deploying large-scale models in resource-constrained environments.

 DPLoRA: A Dual-Pruning Framework based on ILP Optimization and Progressive Pruning for Parameter-Efficient LoRA Fine-Tuning

We annotate PubMed Central paragraphs for document type, domain, and educational quality using a two-stage pipeline: Llama-3.1-70B labels 400K paragraphs, then a fine-tuned XLM-RoBERTa propagates annotations to the full corpus. This paragraph-level approach captures content diversity within scientific articles that document-level labels miss. The resulting Biomed-Enriched corpus contains 2M clinical case paragraphs, providing a publicly available alternative to restricted clinical datasets. For decoders, continual pretraining experiments enable targeted improvements, with clinical upsampling boosting performance by 4 points on MMLU ProfMed and educational filtering improving MedQA and MedMCQA by ~1 point. Combinations of these techniques led to faster convergence, reaching the same performance with a third of training tokens. For encoders, our best recipe matches BioClinical-ModernBERT on 11 tasks (77.3% vs 77.1% F1) while using 2.5x fewer tokens and only public data.

Biomed-Enriched: Data-Efficient Biomedical Pretraining via Paragraph-Level Annotation

While multimodal generative models have advanced radiology report generation (RRG), challenges remain in making reports accessible to patients and ensuring reliable evaluation. The technical language and templated nature of professional reports hinder patient comprehension and enable models to artificially boost lexical metrics such as BLEU by reproducing common report patterns. To address these limitations, we propose the Layman’s RRG framework, which leverages layperson-friendly language to enhance patient accessibility and promote more robust evaluation and report generation by encouraging models to focus on semantic accuracy over rigid templates. Our approach also introduces and releases two refined layman-style datasets (at the sentence and report levels), along with a semantics-based evaluation metric that mitigates inflated lexical scores and a layman-guided training strategy. Experiments show that training on layman-style data helps models better capture the meaning of clinical findings. Notably, we observe a positive scaling law: model performance improves with more layman-style data, in contrast to the inverse trend observed with templated professional language.

X-ray Made Simple: Lay Radiology Report Generation and Robust Evaluation

Large Language Models (LLMs) are evolving rapidly on code generation tasks. While it is important to evaluate their code generation accuracy, ensuring they follow responsible practices is equally critical. Some of the previous works use tools such as CodeQL to match patterns against Common Weakness Enumeration (CWE), suffering from high error rate, while others rely on human annotation to only focus on top CWE categories, limiting security coverage. We propose AutoSUIT Bench, which addresses these limitations through a paradigm to automate the vulnerable code benchmark creation with iterative auto validation. As a result, our benchmark covers 232 CWE categories across C/C++, Java, and Python languages and is designed to evaluate on four coding tasks: (i) code generation, (ii) generation with CWE context, (iii) security patching, and (iv) code completion. Upon benchmarking against LLMs, we found that functionality pass rate is consistently higher than vulnerability pass rate for all programming languages. One notable observation from our benchmark is that LLMs perform well on top CWEs while lacks on others down the list. This highlights the necessity of vulnerable code benchmarks with larger CWE coverage.

AutoSUIT Bench - Automated Security UnIt Test Benchmark for LLM Coding

Formal postconditions precisely characterize program behavior and support debugging, testing, and verification, but writing them requires substantial expertise and effort. This has motivated recent work on automatically generating postconditions from code and natural-language artifacts using large language models (LLMs). However, evaluation remains a key bottleneck. Existing benchmarks primarily emphasize correctness under limited evaluation settings, often relying on surface-form matching or manual assessment on small or synthetic datasets.

We introduce POSTCONDBENCH, a multilingual benchmark for evaluating method-level postcondition generation from real-world software. POSTCONDBENCH comprises 420 Python and Java tasks drawn from 121 open-source projects, each paired with a high-quality ground-truth postcondition set constructed with expert involvement. To enable automatic evaluation, POSTCONDBENCH provides a runnable execution environment and operationalizes completeness via defect discrimination: a postcondition set is more complete if it is violated by more defective implementations while remaining satisfied on correct executions. Using POSTCONDBENCH, we formulate three generation settings and evaluate five SOTA LLMs. Our results reveal a substantial gap between correctness and completeness, and show that repository-level dependencies and method complexity exacerbate this gap.

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