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Large language models (LLMs) perform well on well-posed questions, yet standard question-answering (QA) benchmarks remain far from solved. We argue that this gap is partly due to underspecified questions, that are queries whose interpretation cannot be uniquely determined without additional context. We introduce an LLM-based classifier to identify underspecified questions and apply it to several widely used QA datasets, finding that 16% to over 60% of benchmark questions are underspecified and that LLMs perform significantly worse on them. To isolate the effect of underspecification, we conduct a controlled rewriting experiment that serves as an upper-bound analysis, rewriting underspecified questions into fully specified variants while holding gold answers fixed. QA performance consistently improves under this setting, indicating that many apparent QA failures stem from question underspecification rather than model limitations. Our findings highlight underspecification as an important confound in QA evaluation and motivate greater attention to question clarity in benchmark design.

ACL 2026

Who is the richest club in the championship? Detecting and Rewriting Underspecified Questions Improve QA Performance

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ACL 2024

The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

technical 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.

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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.

Multimodal clinical records contain structured measurements and clinical notes recorded over time, offering rich temporal information about the evolution of patient health. Yet these observations are sparse, and whether they are recorded depends on the patient’s latent condition. Observation patterns also differ across modalities, as structured measurements and clinical notes arise under distinct recording processes. While prior work has developed methods that accommodate missingness in clinical time series, how to extract and use the information carried by the observation process itself remains underexplored. We therefore propose a patient representation learning framework for multimodal clinical time series that explicitly leverages informative missingness. The framework combines (1) a multimodal encoder that captures signals from structured and textual data together with their observation patterns, (2) a Bayesian filtering module that updates a latent patient state over time from observed multimodal signals, and (3) downstream modules for offline treatment policy learning and patient outcome prediction based on the learned patient state. We evaluate the framework on ICU sepsis cohorts from MIMIC-III, MIMIC-IV, and eICU. It improves both offline treatment policy learning and adverse outcome prediction, achieving FQE 0.679 versus 0.528 for clinician behavior and AUROC 0.886 for post-72-hour mortality prediction on MIMIC-III.

Learning Dynamic Representations and Policies from Multimodal Clinical Time-Series with Informative Missingness

In the real world, the execution of a task often depends on the executor's recognition of its value. Motivated by this observation, we propose the value-driven jailbreak attack (VDJA), a simple and effective black-box jailbreak method against large language models (LLMs). VDJA first exploits the phenomenon that LLMs tend to agree with humans to induce LLMs to affirm the moral value of harmful tasks. During autoregressive generation, these value-endorsement tokens function as an implicit value prior, making LLMs more likely to accept and generate harmful content. Extensive experiments on five state-of-the-art (SOTA) LLMs demonstrate the superiority of VDJA. Using only a single query and without concealing harmful instructions, VDJA achieves an average attack success rate (ASR) of 91.8% on JailbreakBench and 95.2% on the AdvBench subset, showcasing SOTA jailbreak success rates and attack efficiency. Most importantly, our work suggests a previously underexplored vulnerability in the safety guardrails of LLMs, which highlights the urgent need to enhance their robustness.

Safety Guardrails of Large Language Models Are Vulnerable to Value-Driven Adversarial Prompting

Automated negotiation in complex, multi-party and multi-issue settings critically depends on accurate opponent modeling. However, conventional numerical-only approaches fail to capture the qualitative information embedded in natural language interactions, resulting in unstable and incomplete preference estimation. Although Large Language Models (LLMs) enable rich semantic understanding of utterances, it remains challenging to quantitatively incorporate such information into a consistent opponent modeling. 
To tackle this issue, we propose a novel preference estimation method integrating natural language information into a structured Bayesian opponent modeling framework. Our approach leverages LLMs to extract qualitative cues from utterances and converts them into probabilistic formats for dynamic belief tracking. Experimental results on a multi-party benchmark demonstrate that our framework improves the full agreement rate and preference estimation accuracy by integrating probabilistic reasoning with natural language understanding.

Preference Estimation via Opponent Modeling in Multi-Agent Negotiation

Current approaches for Natural Language to SPARQL (NL2SPARQL) generation primarily rely on one-turn, training-intensive models. While effective in specific settings, these models often lack generalizability and fail to provide transparency or mechanisms for error recovery in realistic scenarios. Additionally, prior interactive works are largely outdated and incompatible with modern large language model (LLM) workflows. In this paper, we introduce InteracSPARQL, a training-free interactive refinement pipeline that acts as a plug-and-play enhancement for existing SPARQL generation systems. Our approach integrates a set of efficient entity and property lookup tools within a self-correction loop, guided by a novel hybrid Natural Language Explanation (NLE) module. This module combines rule-based Abstract Syntax Tree (AST) parsing with LLM semantic enrichment to produce explanations that are both structurally accurate and linguistically fluent. We evaluate InteracSPARQL on standard benchmarks (QALD-9 and QALD-10), showing that our tool-augmented self-refinement significantly boosts the accuracy of base models without fine-tuning. Furthermore, human evaluation confirms that our structured explanations substantially improve user understanding and ability to correct queries compared to unstructured baselines.

InteracSPARQL : An Interactive System for SPARQL Query Refinement Using Natural Language Explanations

Recent advances in large reasoning models have been driven by reinforcement learning and test-time scaling, accompanied by growing interest in latent rather than purely textual reasoning. However, existing latent reasoning methods lack mechanisms to ensure stable reasoning dynamics in latent space and a systematic way to interleave implicit and explicit reasoning. We introduce SpiralThinker, a unified framework that performs iterative updates over latent representations while enabling interleaved reasoning across latent and textual steps. At its core, SpiralThinker employs a progressive alignment objective and structured annotations to stabilize latent reasoning and maintain coherence with textual reasoning. Across mathematical, logical, and commonsense reasoning tasks, SpiralThinker achieves state-of-the-art performance among latent reasoning baselines. Detailed analyses reveal that both iteration and alignment are indispensable, the numbers of latent tokens and iterations exhibit dataset-specific optima, and appropriate alignment proves critical for an effective iterative process. Overall, SpiralThinker bridges iterative computation and latent reasoning, demonstrating that aligned iterative updates can reliably steer reasoning in the latent space.

SpiralThinker: Latent Reasoning through an Iterative Process with Text–Latent Interleaving

Large Language Models (LLMs) have been shown to exhibit sycophancy, a tendency to align with user assertions even when they conflict with facts. We frame sycophancy as a sociolinguistic phenomenon, disentangling two distinct drivers of credibility: explicit authority (credentials) and implicit authority (linguistic register). We introduce the Sycophancy Matrix, an adversarial evaluation framework that isolates these variables. Using a controlled subset of TruthfulQA, we evaluate open-weight models across English, Spanish, and Portuguese variants. Our findings reveal that models often conflate high register with truthfulness: for some architectures, sophisticated tone triggers deference more effectively than explicit expertise. Furthermore, we observe statistically significant variability across cultural variants of Spanish and Portuguese, supporting the hypothesis that LLMs internalise language-specific sociolinguistic norms and that sycophancy is not a purely technical deficit but an emergent property of multilingual training and alignment. Finally, we identify stable sycophancy fingerprints--domain-specific vulnerability profiles that persist across languages--suggesting that alignment artefacts are intrinsic to model families rather than linguistic context.

Sounding vs. Being an Expert: Disentangling Authority, Register and Cultural Impact in Sycophantic LLMs

Large language model (LLM)-based agents have demonstrated remarkable capabilities in tool use, but their ability to follow user preferences when calling tools remains underexplored. To address this gap, we introduce APOLLO, a benchmark designed to evaluate agents' ability to identify personalized user preferences from interaction histories and to adhere to these preferences when calling tools to solve user queries. In APOLLO, user preferences expressed in the interaction history take two forms: explicit preferences stated directly, and implicit preferences conveyed through behaviors such as option selection and comparison. In addition, the benchmark includes two types of queries, reactive and proactive, which pose challenges for LLMs to ground user queries in the corresponding preferences. Using APOLLO, we evaluate and analyze both language models and reasoning models, and investigate the impact of different agent frameworks, such as Reflexion, on model performance. Experimental results show that current models still struggle to follow user preferences when calling tools. For instance, GPT-4o achieves only 51.16% accuracy on the benchmark. Furthermore, we develop a reinforcement learning-based approach to improve LLMs, achieving substantial performance gains on APOLLO. Our dataset and code are publicly available at https://github.com/zhiyuanc2001/APOLLO.

Towards Preference Following in Tool Calling Language Agents

Large language models (LLMs) enable simulating individual responses from persona information, supporting applications such as opinion elicitation and virtual character creation. However, existing approaches typically assume rich persona profiles, which are often unavailable in practice. In this work, motivated by recent findings that LLMs can identify query-relevant persona dimensions (e.g., whether a user is price-sensitive), we study query-focused individual simulation under cold-start settings, where relevant persona information is identified and requested on demand for each query. To solve this task while minimizing the number of persona requests, we explore a progressive method that iteratively predicts the most critical relevant persona dimension and uses self-reported confidence as a stopping signal to determine when sufficient information has been collected. Experiments on two dialogue datasets show that this query-driven paradigm achieves simulation performance comparable to approaches that rely on rich persona information extracted from dialogue history, using only a few persona dimensions (up to five per query), and this number is further reduced by our progressive method while maintaining or improving simulation quality.

Query-Focused Individual Simulation with Progressive Persona Completion

We introduce SuperIgor, a framework for instruction-following tasks. Unlike prior methods that rely on predefined subtasks, SuperIgor enables a language model to generate and refine high-level plans through a self-learning mechanism, reducing the need for manual dataset annotation. Our approach involves iterative co-training: an RL agent is trained to follow the generated plans, while the language model adapts and modifies these plans based on RL feedback and preferences. This creates a feedback loop where both the agent and the planner improve jointly. We validate our framework in environments with rich dynamics and stochasticity. Results show that SuperIgor agents adhere to instructions more strictly than baseline methods, while also demonstrating strong generalization to previously unseen instructions.

Self-Guided Plan Extraction for Instruction-Following Tasks with Goal-Conditional Reinforcement Learning

Multimodal retrieval systems are expected to operate in a semantic space, agnostic to the language or cultural origin of the query. In practice, however, retrieval outcomes systematically reflect perspectival biases: deviations shaped by linguistic **prevalence** and **cultural** associations. We introduce the **Cross-Cultural, Cross-Modal, Cross-lingual Multimodal (3XCM)** benchmark to isolate these effects. Results from our studies indicate that, for image-to-text retrieval, models tend to favor entries from prevalent languages over those that are semantically faithful. For text-to-image retrieval, we observe a consistent "tugging effect'' in the joint embedding space between semantic alignment and language-conditioned cultural association. When semantic representations are insufficiently resolved, particularly in low-resource languages, similarity is increasingly governed by culturally familiar visual patterns, leading to systematic association bias in retrieval. Our findings suggest that achieving equitable multimodal retrieval necessitates targeted strategies that explicitly decouple language from culture, rather than relying solely on broader data exposure. This work highlights the need to treat linguistic and cultural biases as distinct, measurable challenges in multimodal representation learning.

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Learning Dynamic Representations and Policies from Multimodal Clinical Time-Series with Informative Missingness

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