85 citations · 361 across the 25 of their papers we have counts for
26 papers
Supervised Fine-Tuning or Contrastive Learning? Towards Better Multimodal LLM Reranking
Ziqi Dai, Xin Zhang, Mingxin Li +6
In information retrieval, training reranking models mainly focuses on two types of objectives: metric learning (e.g. contrastive loss to increase the predicted scores on relevant q…
LLMs Can Also Do Well! Breaking Barriers in Semantic Role Labeling via Large Language Models
Xinxin Li, Huiyao Chen, Chengjun Liu +4
Semantic role labeling (SRL) is a crucial task of natural language processing (NLP). Although generative decoder-based large language models (LLMs) have achieved remarkable success…
Adaptive Detoxification: Safeguarding General Capabilities of LLMs through Toxicity-Aware Knowledge Editing
Yifan Lu, Jing Li, Yigeng Zhou +7
Large language models (LLMs) exhibit impressive language capabilities but remain vulnerable to malicious prompts and jailbreaking attacks. Existing knowledge editing methods for LL…
Contrastive Learning on LLM Back Generation Treebank for Cross-domain Constituency Parsing
Peiming Guo, Meishan Zhang, Jianling Li +2
Cross-domain constituency parsing is still an unsolved challenge in computational linguistics since the available multi-domain constituency treebank is limited. We investigate auto…
BeMERC: Behavior-Aware MLLM-based Framework for Multimodal Emotion Recognition in Conversation
Yumeng Fu, Junjie Wu, Zhongjie Wang +3
Multimodal emotion recognition in conversation (MERC), the task of identifying the emotion label for each utterance in a conversation, is vital for developing empathetic machines.…
SpeechEE: A Novel Benchmark for Speech Event Extraction
Bin Wang, Meishan Zhang, Hao Fei +5
Event extraction (EE) is a critical direction in the field of information extraction, laying an important foundation for the construction of structured knowledge bases. EE from tex…