Large Language Models (LLMs) demonstrate remarkable ability to comprehend instructions and generate human-like text, enabling sophisticated agent simulation beyond basic behavior replication. However, the potential for creating freely customisable characters remains underexplored. We introduce the Customisable Conversation Agent Framework, which employs LLMs to simulate real-world characters through personalised characteristic feature injection, enabling diverse character creation according to user preferences.We propose the SimsConv dataset, comprising 68 customised characters and 13,971 multi-turn role-playing dialogues across 1,360 real-world scenes. Characters are initially customised using pre-defined elements (career, aspiration, traits, skills), then expanded through personal and social profiles. Building on this, we present SimsChat, a freely customisable role-playing agent incorporating various realistic settings and topic-specified character interactions.Experimental results on both SimsConv and WikiRoleEval datasets demonstrate SimsChat’s superior performance in maintaining character consistency, knowledge accuracy, and appropriate question rejection compared to existing models. Comprehensive ablation studies validate each component’s contribution to overall performance, with the pre-defined aspects framework and scene construction showing particularly significant impact. Our framework provides valuable insights for developing more accurate and customisable human simulacra.Our data and code are publicly available at https://github.com/Bernard-Yang/SimsChat.

Automatic open-domain dialogue evaluation has attracted increasing attention, yet remains challenging due to the complexity of assessing response appropriateness. Traditional evaluation metrics, typically trained with true positive and randomly selected negative responses, tend to assign higher scores to responses that share greater content similarity with contexts. However, adversarial negative responses, despite possessing high lexical overlap with contexts, can be semantically incongruous. Consequently, existing metrics struggle to evaluate such responses effectively, resulting in low correlations with human judgments. While recent studies have demonstrated the effectiveness of Large Language Models (LLMs) for open-domain dialogue evaluation, they still face challenges in handling adversarial negative examples. We propose a novel evaluation framework that integrates Abstract Meaning Representation (AMR) enhanced domain-specific language models (SLMs) with LLMs. Our SLMs explicitly incorporate AMR graph information through a gating mechanism for enhanced semantic representation learning, while both SLM predictions and AMR knowledge are integrated into LLM prompts for robust evaluation. Extensive experiments on open-domain dialogue evaluation tasks demonstrate the superiority of our method compared to state-of-the-art baselines, particularly in discriminating adversarial negative responses. Our framework achieves strong correlations with human judgments across multiple datasets, establishing a new benchmark for dialogue evaluation. Our code and data are publicly available at https://github.com/Bernard-Yang/SIMAMR.

Complex named entities (NE), like the titles of creative works, are not simple nouns and pose challenges for NER systems. In the SemEval 2023, Task 2: MultiCoNER II was proposed, whose goal is to recognize complex entities against out of knowledge-base entities and noisy scenarios. To address the challenges posed by MultiCoNER II, our team NetEase.AI proposed an entity recognition system that integrates text error correction system and external knowledge, which can recognize entities in scenes that contain entities out of knowledge base and text with noise. Upon receiving an input sentence, our systems will correct the sentence, extract the entities in the sentence as candidate set using the entity recognition model that incorporates the gazetteer information, and then use the external knowledge to classify the candidate entities to obtain entity type features. Finally, our system fused the multi-dimensional features of the candidate entities into a stacking model, which was used to select the correct entities from the candidate set as the final output. Our system exhibited good noise resistance and excellent entity recognition performance, resulting in our team’s first place victory in the Chinese track of MultiCoNER II.