This work investigates the effectiveness of masked language models (MLMs) and autoregressive language models (LLMs) with fewer than one billion parameters in the detection of implicit hate speech through fine-grained span identification. The evaluation spans zero-shot, few-shot, and full supervision settings across two core benchmarks—SBIC and IHC—and an auxiliary testbed, OffensiveLang.RoBERTa-Large-355M emerges as the strongest zero-shot model, achieving the highest F1 scores of 75.8 (SBIC) and 72.5 (IHC), outperforming larger models like LLaMA 3.2-1B. ModernBERT-125M closely matches this performance with scores of 75.1 and 72.2, demonstrating the advantage of architectural efficiency. Among instruction-tuned models, SmolLM2-135M Instruct and LLaMA 3.2 1B Instruct consistently outperform their non-instructed counterparts, with up to +2.3 F1 gain on SBIC and +1.7 on IHC. Interestingly, the larger SmolLM2-360M Instruct does not outperform the 135M variant, highlighting that model scale does not always correlate with performance in implicit hate detection tasks.Few-shot fine-tuning with SmolLM2-135M Instruct achieves F1 scores of 68.2 (SBIC) and 64.0 (IHC), trailing full-data fine-tuning by only 1.6 and 2.0 points, respectively, with accuracy drops under 0.5 points. This illustrates the promise of compact, instruction-aligned models in data-scarce settings, particularly when optimized with Low-Rank Adaptation (LoRA).Topic-guided error analysis using Latent Dirichlet Allocation (LDA) reveals recurring model failures in ideologically charged or euphemistic discourse. Misclassifications often involve neutral references to identity, politics, or advocacy language, underscoring current limitations in discourse-level inference and sociopragmatic understanding.

Les théories de l’interaction suggèrent que l’émergence d’une compréhension mutuelle entre les locuteurs en conversation naturelle dépend de la construction d’une base de connaissances partagée (common ground), mais n’explicitent ni le choix ni les circonstances de la mémorisation de ces informations.Des travaux antérieurs utilisant les métriques dérivées de la théorie de l’information pour analyser la dynamique d’échange d’information ne fournissent pas de moyen efficace de localiser les informations qui entreront dans le common ground. Nous proposons une nouvelle méthode basée sur la segmentation automatique d’une conversation en thèmes qui sont ensuite résumés. L’emplacement des transferts d’informations est finalement obtenu en calculant la distance entre le résumé du thème et les différents énoncés produits par un locuteur. Nous évaluons deux grands modèles de langue (LLMs) sur cette méthode, sur le corpus conversationnel français Paco-Cheese. Plus généralement, nous étudions la façon dont les derniers développement dans le champ des LLMs permettent l’étude de questions s’appuyant normalement fortement sur le jugement d’annotateurs humains.

Interaction theories suggest that the emergence of mutual understanding between speakers in natural conversations depends on the construction of a shared knowledge base (common ground), but the details of which information and the circumstances under which it is memorized are not explained by any model. Previous works have looked at metrics derived from Information Theory to quantify the dynamics of information exchanged between participants, but do not provide an efficient way to locate information that will enter the common ground. We propose a new method based on the segmentation of a conversation into themes followed by their summarization. We then obtain the location of information transfers by computing the distance between the theme summary and the different utterances produced by a speaker. We evaluate two Large Language Models (LLMs) on this pipeline, on the French conversational corpus Paco-Cheese. More generally, we explore how the recent developments in the field of LLMs provide us with the means to implement these new methods and more generally support research into questions that usually heavily relies on human annotators.