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This paper describes our system submitted to SemEval-2026 Task 11: Disentangling Content and Formal Reasoning in Large Language Models. We present an efficient modular neuro-symbolic approach, combining a symbolic prover with small reasoning LLMs (4B parameters). The system consists of an LLM-based parser that translates natural language syllogisms to a first-order logic (FOL) representation, an automated theorem prover, and two optional modules: machine translation for multilingual inputs and a symbolic retrieval component for the identification of relevant premises. The system achieves competitive accuracy and relatively low content effect on most subtasks. Our ablations show that this approach outperforms LLM-based zero-shot baselines in this parameter size range, but also reveal limited multilingual capabilities of small LLMs. Finally, we include a discussion of the task&#39;s main ranking metric and analyze its limitations.

ACL 2026

UFAL-CUNI at SemEval-2026 Task 11: An Efficient Modular Neuro-symbolic Method for Syllogistic Reasoning

content effect

prover9

syllogistic reasoning

neuro-symbolic

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.

This paper describes our system submitted to SemEval-2026 Task 11: Disentangling Content and Formal Reasoning in Large Language Models. We present an efficient modular neuro-symbolic approach, combining a symbolic prover with small reasoning LLMs (4B parameters). The system consists of an LLM-based parser that translates natural language syllogisms to a first-order logic (FOL) representation, an automated theorem prover, and two optional modules: machine translation for multilingual inputs and a symbolic retrieval component for the identification of relevant premises. The system achieves competitive accuracy and relatively low content effect on most subtasks. Our ablations show that this approach outperforms LLM-based zero-shot baselines in this parameter size range, but also reveal limited multilingual capabilities of small LLMs. Finally, we include a discussion of the task's main ranking metric and analyze its limitations.

workshop paper

We present the shared task on narrative similarity and narrative representation learning — NSNRL (pronounced "nass-na-rel").
The task operationalizes narrative similarity as a binary classification problem: determining which of two stories is more similar to an anchor story.
We introduce a novel definition of narrative similarity, compatible with both narrative theory and intuitive judgment.
Based on the similarity judgments collected under this concept, we also evaluate narrative embedding representations.
We collected at least two annotations each for more than 1,000 story summary triples, with each annotation being backed by at least two annotators in agreement.
This paper describes the sampling and annotation process for the dataset; further, we give an overview of the submitted systems and the techniques they employ.
We received a total of 71 final submissions from 46 teams across our two tracks.
In our triple-based classification setup, LLM ensembles make up many of the top-scoring systems, while in the embedding setup, systems with pre- and post-processing on pretrained embedding models perform about on par with custom fine-tuned solutions.
Our analysis identifies potential headroom for improvement of automated systems in both tracks.
The task website includes visualizations of embeddings alongside instance-level classification results for all teams.

SemEval-2026 Task 4: Narrative Story Similarity and Narrative Representation Learning

We present the GUIR system for SemEval-2026 Task 7, Everyday Knowledge Across Diverse Languages and Cultures, which probes the extent to which general-purpose LLMs encode cultural knowledge without any culture-specific supervision or fine-tuning. Our system addresses two tracks built on the BLEnD benchmark. For the short-answer question (SAQ) track, we employ zero-shot prompting with gpt-4.1, achieving 55.5% accuracy across 61 language locales. For the multiple-choice question (MCQ) track, we propose a three-stage pipeline: (1) zero-shot chain-of-thought inference with gpt-5-mini, (2) cross-locale majority voting to correct inconsistent predictions, and (3) a multi-agent debate protocol in which three LLM instances argue and adjudicate over residual errors. This pipeline achieves 97.47% overall accuracy across 30 locales, ranking first among all submitted systems on the MCQ track. We further conduct a targeted human evaluation on the Persian locale, revealing that BLEnD's lemma-matching scorer systematically underestimates model performance, with human annotators scoring the system 18 percentage points higher than the lemma-matching evaluation. This reveals the need for better evaluation of morphologically rich languages like Persian.

GUIR at SemEval-2026 Task 7: Probing Cultural Knowledge in LLMs via Multi-Agent Debate

We present the results and findings from SemEval Task 8: MTRAGEval. MTRAGEval measures three Retrieval Augmented Generation (RAG) subtasks: A. Retrieval, B. Generate, and C. Retrieve+Generate (full RAG) on multi-turn conversations. The task is evaluated using MTRAG-UN, a new benchmark for Multi-Turn RAG focusing on Unanswerable, Underspecified, Non-Standalone, and Unclear Questions. The MTRAGEval task attracted strong participation with 107 registered teams and 92 submissions across all subtasks, and yielded several interesting findings on effective retrieval and query rewriting techniques, the use of ensemble models, and the compounding costs of retrieval errors on downstream generation quality.

SemEval-2026 Task 8: MTRAGEval: Evaluating Multi-Turn RAG Conversations

We introduce SemEval-2026 Task 5 on "Rating Plausibility of Word Senses in Ambiguous Stories through Narrative Understanding". 
The dataset for this task consists of 4-5 sentence English short stories. In each story, one sentence includes a lexical ambiguity and different senses are to be judged in terms of plausibility on a Likert scale. The task is intentionally constructed to be challenging by stories only providing sparse contextual cues. We give an overview of well-performing, frequent and interesting approaches used by participating systems. From a total of 175 registered participants and 27 submitted system description papers, the best system achieved an "accuracy within standard deviation" score of 93.3%.

SemEval-2026 Task 5: Rating Plausibility of Word Senses in Ambiguous Stories through Narrative Understanding

SemEval-2026 Task 11 evaluates the ability of Large Language Models (LLMs) to perform content-independent reasoning through a novel multilingual syllogistic dataset designed to measure the "content effect" — the tendency to conflate semantic plausibility with logical validity. The competition featured four subtasks, covering English and multilingual settings with both standard and noisy premise sets. Evaluations of zero-shot baselines reveal that the content effect is pervasive in open models, whereas newer versions demonstrate a significant shift in performance. Across the subtasks, findings indicate that introducing distracting premises can challenge the models' ability to filter misleading information, while multilingual settings amplify their susceptibility to content biases compared to English. Participants proposed diverse approaches, including neuro-symbolic decomposition, fine-tuning and distillation methods, data augmentation, and activation steering. While explicit symbolic verification remains the most reliable strategy, activation-level interventions and fine-tuning methods offer promising pathways for internalising formal logic within neural architectures. Ultimately, the task reinforces the efficacy of neuro-symbolic approaches and emerging architectural trends for logical reliability, while also highlighting that multilingual setups and longer contexts still pose significant challenges to be investigated in future research.

SemEval-2026 Task 11: Disentangling Content and Formal Reasoning in Large Language Models

We present our shared task on predicting variation in emotional valence and arousal over time from ecological essays. The shared task uses a longitudinal dataset collected over 7 data collection phases of 14-day each spanning from 2021 to 2024, consisting of real-time essays and feeling words (e.g., happy, calm, sad, etc.) written in English by U.S. service-industry workers about ``how they are feeling''. Each text is associated with self‑reported valence (V) (0 - 4, highly negative to highly positive affect) and arousal (A) (0 - 2, low to high energy) scores. The shared task consists of three parts, Subtask (1): Longitudinal Affect Assessment, Subtask (2): Forecasting Variation in Affect as a (2a): \textit{state change}, and (2b): \textit{disposition change}.

The task attracted over 200 member registrations on Codabench, receiving official system submissions from 31 teams (total 104 team members), of which 28 teams (with 90 team members) submitted system description papers making it to our leaderboard. We discuss baseline results along with findings from 28 systems, highlighting the best-performing systems, a deeper analysis of performance on essays versus feeling words, and assessments for authors seen versus unseen during training. The datasets for this task are publicly available.

SemEval-2026 Task 2: Predicting Variation in Emotional Valence and Arousal over Time from Ecological Essays

We present the SemEval-2026 shared task on Dimensional Aspect-Based Sentiment Analysis (DimABSA), which improves traditional ABSA by modeling sentiment along valence–arousal (VA) dimensions rather than using categorical polarity labels. To extend ABSA beyond consumer reviews to public-issue discourse (e.g., political, energy, and climate issues), we introduce an additional task, Dimensional Stance Analysis (DimStance), which treats stance targets as aspects and reformulates stance detection as regression in the VA space. The task consists of two tracks: Track A (DimABSA) and Track B (DimStance). Track A includes three subtasks: (1) dimensional aspect sentiment regression, (2) dimensional aspect sentiment triplet extraction, and (3) dimensional aspect sentiment quadruplet extraction, while Track B includes only the regression subtask for stance targets. We also introduce a continuous F1 (cF1) metric to jointly evaluate structured extraction and VA regression.

The task attracted more than 400 participants, resulting in 112 final submissions and 42 system description papers. We report baseline results, discuss top-performing systems, and analyze key design choices to provide insights into dimensional sentiment analysis at the aspect and stance-target levels. All resources are available on our GitHub repository.

SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis (DimABSA)

We present SemEval-2026 Task 1: MWAHAHA (Models Write Automatic Humor And Humans Annotate), the first shared task on general-purpose humor generation. Systems must produce short jokes in English, Spanish, and Chinese under lexical or topical constraints (Subtask A) and generate humorous captions for GIFs (Subtask B). To discourage memorization and ensure fairness, all jokes must meet specific criteria, such as using infrequent word pairs or relating to recent news headlines. Evaluation is conducted through pairwise human preference judgments in a Chatbot Arena-style setting, yielding Elo-based rankings. The task attracted 309 registered users, with 37 teams submitting systems to the evaluation phase. Participating systems employ a wide range of NLP techniques, including generate-then-rank pipelines, reinforcement learning, parameter-efficient fine-tuning, retrieval-augmented generation, humor-theory-grounded prompting, and persona-based strategies. Our Gemini 2.5 Flash baseline, using simple prompts, tied for first place in all subtasks, and the majority of elaborate multi-stage pipelines only marginally surpassed it with overlapping confidence intervals. More work is necessary to outperform the simple usage of state-of-the-art large language models. We release all evaluation data, prompts, and leaderboard results to support future research in computational humor generation.

SemEval-2026 Task 1: MWAHAHA, Models Write Automatic Humor And Humans Annotate

We tackle classifying evasive political answers
within the context of SemEval-2026 Task 6 and
compare three modeling strategies: a flat base-
line, a hierarchical cascade, and a multitask
learning approach. Our experiments demon-
strate that a hierarchical RoBERTa-base model
achieves the best performance, particularly by
leveraging the distinctiveness of the class Clear
Non-Reply. Conversely, we find that stan-
dard multitask learning frequently produces
structurally invalid label combinations in a sig-
nificant fraction of predictions. Our demon-
strations show that applying a constrained in-
ference mask eliminates these errors entirely
while improving F1 performance, whereas a
fully joint training approach underperforms due
to data sparsity. Finally, we employ dataset
cartography to compare training dynamics be-
tween the hierarchical and multitask approach.

FER at SemEval-2026 Task 6: Analysis of Different Approaches to Unmasking Political Question Evasions

This paper addresses SemEval-2026 Task 11, which focused on mitigating content effects in syllogistic reasoning. Logical validity is often conflated with semantic plausibility in large language models.
Prior methods rely on standard fine-tuning or prompting, without explicit bias control.
A rule- and template-based symbolic data augmentation framework is proposed for fine-tuning the \texttt{Qwen2-1.5B-Instruct} model and instruction-tuning the \texttt{XLM-RoBERTa-large} model. 
Logic-preserving synthetic data are generated through lexical rules. 
The system is ranked 1st in Task 1 with a perfect overall score of 100, and 6th in Task 3 with a score of 56.97. Code is publicly available at: \url{https://github.com/YNU-HPCC/semeval-2026-task11}.

YNU-HPCC at SemEval-2026 Task 11: Mitigating Content Effects in Syllogistic Reasoning with Qwen2-1.5B-Instruct and XLM-RoBERTa-Large for English and Multilingual TasksMultilingual Tasks

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