We present SDialog, an MIT-licensed open-source Python toolkit for end-to-end development, simulation, evaluation, and analysis of LLM-based conversational agents. Built around a standardized Dialog representation, SDialog unifies persona-driven multi-agent simulation with composable orchestration for controlled synthetic dialog generation; multi-layer evaluation combining linguistic metrics, LLM-as-a-judge assessments, and functional correctness validators; mechanistic interpretability tools for activation inspection and causal behavior steering via feature ablation and induction; and audio rendering with full acoustic simulation, including 3D room modeling and microphone effects. The toolkit integrates with major LLM backends under a consistent API, enabling mixed-backend and reproducible experiments. By bridging agent construction, user simulation, dialog generation, evaluation, and interpretability within a single coherent workflow, SDialog enables more controlled, transparent, and systematic research on conversational systems.

The widespread deployment of large language models (LLMs) across diverse domains has underscored the critical need to ensure the credibility and accuracy of their generated content, particularly in the presence of hallucinations. These hallucinations can severely compromise both the practical performance of models and the security of their applications. In response to this issue, SemEval-2025 Task 3 Mu-SHROOM: Multilingual Shared-task on Hallucinations and Related Observable Overgeneration Mistakes introduces a more granular task for hallucination detection. This task seeks to identify hallucinations in text, accurately locate hallucinated segments, and assess their credibility. In this paper, we present a three-stage method for fine-grained hallucination detection and localization. First, we transform the text into a triplet representation, facilitating more precise hallucination analysis. Next, we leverage a large language model to generate fact-reference texts that correspond to the triplets. Finally, we employ a fact alignment strategy to identify and localize hallucinated segments by evaluating the semantic consistency between the extracted triplets and the generated reference texts. We evaluate our method on the unlabelled test set across all languages in Task 3, demonstrating strong detection performance and validating its effectiveness in multilingual contexts.

The goal of Emotion Cause Pair Extraction (ECPE) is to explore the causes of emotion changes and what causes a certain emotion. This paper proposes a three-step learning approach for the task of Textual Emotion-Cause Pair Extraction in Conversations in SemEval-2024 Task 3, named ECSP. We firstly perform data preprocessing operations on the original dataset to construct negative samples. Secondly, we use a pre-trained model to construct token sequence representations with contextual information to obtain emotion prediction. Thirdly, we regard the textual emotion-cause pair extraction task as a machine reading comprehension task, and fine-tune two pre-trained models, RoBERTa and SpanBERT. Our results have achieved good results in the official rankings, ranking 3rd under the strict match with the Strict F1-score of 15.18%, which further shows that our system has a robust performance.