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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 Additionally, prior interactive works are largely outdated and incompatible with modern large language model (LLM) workflows.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.

ACL 2026

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

The AACL, Asia-Pacific Chapter of the Association for Computational Linguistics, and the AFNLP, Asian Federation of Natural Language Processing, invite you to participate in the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL) and the 10th International Joint Conference on Natural Language Processing (IJCNLP).

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IJCNLP-AACL 2022

The AACL, Asia-Pacific Chapter of the Association for Computational Linguistics, and the AFNLP, Asian Federation of Natural Language Processing, invite you to participate in the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL) and the 10th International Joint Conference on Natural Language Processing (IJCNLP). 

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.

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. 
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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 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.

Evaluating Perspectival Biases in Cross-Modal Retrieval

As medical LLMs transition to clinical deployment, assessing their ethical reasoning capability becomes critical. While achieving high accuracy on knowledge benchmarks, LLMs lack validated assessment for navigating ethical trade-offs in clinical decision-making where multiple valid solutions exist. Existing benchmarks lack systematic approaches to incorporate recognized philosophical frameworks and expert validation for ethical reasoning assessment. We introduce PrinciplismQA, a philosophy-grounded approach to assessing LLM clinical medical ethics alignment. Grounded in Principlism, our approach provides a systematic methodology for incorporating clinical ethics philosophy into LLM assessment design. PrinciplismQA comprises 3,648 expert-validated questions spanning knowledge assessment and clinical reasoning. Our expert-calibrated pipeline enables reproducible evaluation and models ethical biases. Evaluating recent models reveals significant ethical reasoning gaps despite high knowledge accuracy, demonstrating that knowledge-oriented training does not ensure clinical ethical alignment. PrinciplismQA provides a validated tool for assessing clinical AI deployment readiness.

PrinciplismQA: A Philosophy-Grounded Approach to Assessing LLM-Human Clinical Medical Ethics Alignment

The integration of extensive, dynamic knowledge into Large Language Models (LLMs) remains a significant challenge due to the inherent entanglement of factual data and reasoning patterns. Existing solutions, ranging from non-parametric Retrieval-Augmented Generation (RAG) to parametric knowledge editing, are often constrained in practice by finite context windows, retriever noise, or the risk of catastrophic forgetting. In this paper, we propose \textbf{DRIFT}, a novel dual-model architecture designed to explicitly decouple knowledge extraction from the reasoning process. Unlike static prompt compression, DRIFT employs a lightweight knowledge model to dynamically compress document chunks into implicit fact tokens conditioned on the query. These dense representations are projected into the reasoning model's embedding space, replacing raw, redundant text while maintaining inference accuracy. Extensive experiments show that DRIFT significantly improves performance on \textbf{long-context tasks}, outperforming strong baselines among comparably sized models. Our approach provides a scalable and efficient paradigm for extending the effective context window and reasoning capabilities of LLMs. Our code and data will be made public upon publication.

Decoupled Reasoning with Implicit Fact Tokens (DRIFT): A Dual-Model Framework for Efficient Long-Context Inference

Temporal Entity Alignment (TEA), which aims to identify equivalent entities across Temporal Knowledge Graphs (TKGs), is crucial for integrating knowledge facts from multiple sources. However, existing TEA models often fail to capture the orthogonal yet complementary effect between structural and temporal features, and typically overlook the importance of information richness—a key factor for effective message passing in the neural feature encoders. To address these limitations, we propose a RCTEA framework that jointly models both structural and temporal aspects of the TKGs for entity alignment. Specifically, we design a richness-guided attention mechanism along with an adaptive weighting strategy to facilitate effective feature fusion. To ensure robust alignment despite noisy entity contexts, we introduce a dual-view neighborhood consensus algorithm that jointly refines the feature encoders to enforce local structural consistency of the predicted alignments. Extensive experiments demonstrate the superiority of RCTEA, achieving state-of-the-art performance on public TEA benchmarks.

RCTEA: Richness-guided Co-training for Temporal Entity Alignment

Preference alignment methods such as RLHF and Direct Preference Optimization (DPO) improve instruction following, but they can also reinforce hallucinations when preference judgments reward fluency and confidence over factual correctness. We introduce F-DPO (Factuality-aware Direct Preference Optimization), a simple extension of DPO that uses only binary factuality labels. F-DPO (i) applies a label-flipping transformation that corrects misordered preference pairs so the chosen response is never less factual than the rejected one, and (ii) adds a factuality-aware margin that emphasizes pairs with clear correctness differences, while reducing to standard DPO when both responses share the same factuality. We construct factuality-aware preference data by augmenting DPO pairs with binary factuality indicators and synthetic hallucinated variants. Across seven open-weight LLMs (1B–14B), F-DPO consistently improves factuality and reduces hallucination rates relative to both base models and standard DPO. On Qwen3-8B, F-DPO reduces hallucination rates by 5×(from 0.424 to 0.084) while improving factuality scores by 50% (from 5.26 to 7.90). F-DPO also generalizes to out-of-distribution benchmarks: on TruthfulQA, Qwen2.5-14B achieves +17% MC1 accuracy (0.500 to 0.585) and +49% MC2 accuracy (0.357 to 0.531). F-DPO requires no auxiliary reward model, token-level annotations, or multi-stage training.

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

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