United States

Humor is one of the most culturally embedded and socially significant dimensions of human communication, yet it remains largely unexplored as a dimension of Large Language Model (LLM) alignment. In this study, five frontier language models play the same Cards Against Humanity games (CAH) as human players. The models select the funniest response from a slate of ten candidate cards across 9,894 rounds. While all models exceed the random baseline, alignment with human preference remains modest. More striking is that models agree with each other substantially more often than they agree with humans. We show that this preference is partly explained by systematic position biases and content preferences, raising the question whether LLM humor judgment reflects genuine preference or structural artifacts of inference and alignment.

ACL 2026

Cards Against LLMs: Benchmarking Humor Alignment in Large Language Models

llm alignment

large language models (llms)

computational humor

## Welcome to EMNLP 2022!
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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.

We present exploratory experiments in the comedic roasting capabilities of GPT4o. Specifically, @ComedyCentral roasts were scraped to design a survey in which participants blindly evaluated snippets of human and AI roasts, and had to predict the author (AI/human) in a second round of reviewing. The results show that there is no significant difference in how the barbs in human- and AI-generated roasts are rated. Further, a qualitative analysis showed that although the model utilizes specific recurrent phrases to imitate the style of human comedians, both generative LLM detectors and humans performed suboptimally in predicting the true author of the roasts.

The Roast of GPT4o: Experiments in Generating, Detecting and Evaluating Celebrity Roast Comedy

We investigate whether scaling model parameters improves humor generation through a controlled ablation study. Using five Qwen3 variants (8B–235B, dense and MoE), we generate jokes across 50 themes. Beyond evaluating humor scaling, this work serves as an empirical study into the nature of LLM versus human evaluations on highly subjective creative tasks. While an automated judge yields a perfect monotonic ranking between parameter count and win rate, human annotators find no significant aggregate difference in humor quality. Restricting to themes where annotators agree reveals a significant preference for the largest model (p = 0.039), suggesting scaling effects exist but are masked by a "quality floor." Crucially, our analysis of bias characteristics shows that the automated judge exhibits severe positional and length biases compared to human evaluators, further suggesting that LLMs may systematically distort quality differences on subjective tasks.

Does Bigger Mean Funnier? Evaluating Humor Generation Across the Qwen3 Model Family

Large language models (LLMs) can generate jokes that rival human comedy in quality, yet their originality remains uncertain. This study develops and validates automated, corpus-based methods for quantifying joke originality by focusing on core "topic handles" – the key nouns or short noun phrases that capture a joke's script opposition and logical mechanism (per the General Theory of Verbal Humor). Using a reference corpus of one million English jokes from Reddit, we compute Pointwise Mutual Information (PMI) on handle pairs in three variants (raw co-occurrence, semantic-cluster smoothing, and word- decomposition) and two embedding-based measures (handle-level conceptual distance and full-text corpus novelty via Sentence-BERT). We tested the measures on 400 LLM- generated jokes (200 from GPT-4o and 200 from GPT-5.4 prompted for original two-sentence jokes) and 80 benchmark jokes from the Witscript-powered JEST benchmark (including non- humorous controls). Three professional comedians rated all items on 0–3 scales of originality and funniness. Automated measures, especially raw PMI average (ρ = .386) and corpus novelty (ρ = .372), correlated significantly with human originality ratings but showed no relation to funniness. Benchmark jokes were rated most original, and the originality– funniness relationship differed sharply across sources (positive for benchmark, negative for GPT-4o). The results demonstrate that handle-based PMI and semantic novelty metrics offer practical, quantitative tools for assessing originality in both human and AI-generated humor, advancing objective evaluation of computational creativity while highlighting distinct patterns in how general-purpose LLMs versus specialized humor systems produce novel jokes.

Navigating the Joke Space: Towards Automated Originality Assessment of AI-Generated Humor

Computational mental health (CMH) classifiers often degrade under distribution shift because human annotators and distant-supervision pipelines reward different linguistic signals. 
 
 We introduce TSS (Triple-Stream Stress probe), a multi-channel diagnostic framework that decomposes text into (A) lexical character n-grams, (B) a small, mostly content-free morpho-syntactic channel, and (C) a 154-feature psycholinguistic style channel. Across four English datasets (N = 12,906), TSS reveals a lexical interference effect: adding lexical features to the style channel reduces Macro-F1 on human-labeled data (mean drop 0.072, p < 10⁻⁴) but not on auto-labeled data. 
 
 We propose Degree of Divergence (DoD), a difference-in-differences statistic adapted from econometrics for label-source auditing, with instance-level bootstrap inference; the headline estimate is DoD(BC–A) = 0.0374, 95% CI [0.0097, 0.0651], p = 0.0032. 
 
 A platform-stratified Twitter-only DoD (which removes the Reddit vs. Twitter contrast) reproduces the pattern with bootstrap inference: DoDᵀᵥ,BC–A = +0.096 (p < 0.001) and DoDᵀᵥ,AC–A = −0.089 (p < 0.001). Interventional masking (pos_only) retains ∼95–99% of Channel C's performance after destroying content words on human datasets, indicating that the style channel does not rely primarily on lexical surface form. 
 
 TSS is positioned as a diagnostic audit framework, not a clinical screening tool: it flags label-source-specific shortcut learning before generalization claims are made.

The Divergence Hypothesis: Unmasking Lexical Interference and Label Bias in Mental Health NLP

Large language models achieve strong performance on biomedical question answering and summarization benchmarks, yet traditional evaluation metrics often fail to detect clinically significant factual errors. We introduce a unified evaluation framework that combines reference-based measures with evidence-grounded factuality verification to assess biomedical text generation. Evaluating four open-source models across three benchmarks (BioASQ, PubMedQA, MedLFQA), we find that 13.4–24.7% of generated claims are contradicted and 23–41% are unsupported, despite high lexical overlap scores. Our proposed Fact-Aligned Score (FAS) correlates strongly with claim-level verifiability (rho=0.68), substantially outperforming ROUGE-L (rho=0.41). We release an open-source toolkit with model outputs and analysis scripts to support reproducible factuality evaluation and safer deployment of biomedical LLMs.

Towards Unified Factuality Evaluation for Biomedical QA and Summarization: Aligning Metrics with Clinical Use-Cases

Seizure freedom is a key clinical outcome for people with epilepsy (PWE) yet it is primarily recorded in free-text notes and letters in the United Kingdom, making it difficult to aggregate and track at scale. This paper introduces a generative LLM-based pipeline boosted by synthetic data to identify a PWE's seizure freedom status in clinicians' records. We fine-tuned seven different LLMs with between 4-14 billion parameters using LoRA to compare models trained on synthetic records against those trained on expert annotated records. The best performing configuration, based on Qwen-2.5-14B, was trained entirely on synthetic records and used chain-of-thought (CoT) reasoning (both generated by GPT-5). This achieved an F1 score of 0.90±0.02 on double-annotated test data and outperformed the equivalent model trained on authentic clinician records, which achieved 0.87±0.04. The synthetically trained models also have the benefit of outputting their CoT reasoning process for greater decision-making transparency and can also make use of the unused supervised training data for significantly increased test examples. This work has implications for monitoring a key treatment outcome for PWE automatically and at scale.

Using Synthetic Records to Improve Automated Identification of Seizure Freedom in Clinical Text about People with Epilepsy

This paper studies how to improve biomedical named entity recognition (NER) using large language models (LLMs), especially for low-resource languages like Bangla and Basque. The main goal is to understand how different prompt styles and output formats affect model performance.
 
 The study finds that the way we design prompts is very important. Among all methods, question-style prompting works best across all languages. It helps the model understand the biomedical task more clearly and improves accuracy. In fact, improvements are much greater in Bangla and Basque compared to high-resource languages like English and Spanish.
 
 Another key finding is about the output format. Traditional BIO tagging (labeling each word) performs poorly with LLMs because it is strict and sensitive to small errors. Instead, span-based extraction (directly extracting text phrases) works much better and gives higher F1 scores. This is because LLMs naturally generate text spans rather than token-level labels.
 
 The paper also analyzes errors. Common problems include hallucination, missing entities, and boundary mistakes. Translation-based prompts can reduce hallucination, while question-style prompts reduce empty outputs in biomedical NER.
 
 Overall, the study shows that choosing the right prompt and output format is very important, especially for low-resource high-vocabulary languages. It provides useful guidance for building better multilingual medical information extraction systems.

Analyzing Prompt Design Choices in Biomedical Information Extraction for Low-Resource Languages

Extracting structured knowledge from unstructured text is a fundamental challenge in machine learning, particularly for concepts organized within complex hierarchical ontologies. In genomics, identifying phenotypes from clinical narratives is crucial for diagnostic precision, yet current methods struggle with contextual interpretation and subtle clinical descriptions.
 We present a hierarchy-aware workflow for ontology-based phenotype linking that combines semantic and hierarchical signals. Our approach integrates Large Language Models for span detection with retrieval and a hybrid reranking strategy using both Euclidean (semantic) and hyperbolic (hierarchical) embeddings trained on the Human Phenotype Ontology.
 We show that while hyperbolic embeddings alone do not outperform standard semantic retrieval, they provide complementary structural signals that improve performance over strong baselines when combined with Euclidean representations. In particular, the hybrid approach outperforms existing state-of-the-art methods and yields more hierarchically coherent predictions, especially in settings involving implicit phenotype mentions.
 Experiments on a public benchmark (ID-68) and a newly released clinical dataset (CHU-50), publicly released with code and data, highlight both performance gains and improved alignment with ontology structure. We further introduce a hierarchy-aware evaluation framework that reflects clinical relevance beyond exact-match metrics.

Hierarchy-Aware Hyperbolic and Semantic Reranking for Ontology-Based Phenotype Linking

The MedCPT model has demonstrated that strong biomedical retrievers can be trained using proprietary PubMed search logs. In this work, we study whether freely available citation sentences are sufficient to train similarly effective models. We construct a large-scale training dataset of ~ 62 million citation sentence-abstract pairs extracted from PubMed Central. We train a lightweight BERT-based retriever-reranker model called CiteRec on this dataset and evaluate it across three benchmark settings: (a) the biomedical subset of BEIR for information retrieval, (b) SciRepEval for generalizable scientific document embeddings, and (c) CitancePlus, a new set of ~ 90 thousand citation sentence-abstract pairs for PubMed-scale citation recommendation.
 
 We show that CiteRec performs competitively with MedCPT on the biomedical BEIR subset and outperforms it on SciRepEval. On CitancePlus, CiteRec achieves strong performance for citation recommendation over the full PubMed corpus, outperforming both MedCPT and a substantially larger Qwen3-Embedding-8B retriever.

Training Biomedical Retrievers From Large-Scale Citation Contexts

Standard clinical Natural Language Processing (NLP) benchmarks often yield inflated metrics by forcing deterministic classification on ambiguous instances, thereby obscuring the clinical risks of overconfident predictions. To bridge this gap, we propose a risk-aware hybrid selective classification framework, evaluated on early Human Immunodeficiency Virus suspicion identification in Spanish clinical notes. Our dual-verification approach explicitly decouples aleatoric uncertainty through Mondrian conformal prediction and epistemic uncertainty using a Multi-Centroid Mahalanobis Distance veto. Empirical evaluations reveal that standard uncertainty metrics and baseline classifiers are structurally insufficient for safe medical triage, suffering severe coverage collapse when forced to operate under strict reliability constraints. In contrast, by demanding that clinical narratives pass both probabilistic and geometric safeguards, the proposed framework successfully isolates a highly trustworthy operational domain.The obtained results show that explicit, decoupled uncertainty quantification is essential for translating biomedical NLP into responsible clinical practice.

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The Roast of GPT4o: Experiments in Generating, Detecting and Evaluating Celebrity Roast Comedy

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