AAAI Conference on Artificial Intelligence · 1014 papers
When to Show a Suggestion? Integrating Human Feedback in AI-Assisted Programming
Hussein Mozannar (Massachusetts Institute of Technology), Eric Horvitz (Microsoft Research)
CodeRecommendation SystemAI Code AssistantReinforcement Learning from Human FeedbackLarge Language ModelText
🎯 What it does: This study investigates how to utilize programmers' feedback on accepting or rejecting code suggestions to determine when to display or hide AI-generated code suggestions in IDEs, in order to enhance programming efficiency.
Wikiformer: Pre-training with Structured Information of Wikipedia for Ad-Hoc Retrieval
Weihang Su (Tsinghua University), Shengluan Hou (Tsinghua University)
CodeRetrievalTransformerContrastive LearningText
🎯 What it does: Designed the Wikiformer pre-training framework, utilizing the structural information of Wikipedia to construct four self-supervised pre-training objectives for retrieval tasks, enhancing the effectiveness of document relevance learning.
Working Memory Capacity of ChatGPT: An Empirical Study
Dongyu Gong (Yale University), Dingmin Wang (University of Oxford)
CodeTransformerLarge Language ModelText
🎯 What it does: This paper systematically evaluates the working memory capacity of ChatGPT in Verbal and Spatial n-back tasks, finding its upper limit to be about 3 levels, similar to humans;
🎯 What it does: This paper proposes X-RefSeg3D, an end-to-end model for reference-based 3D instance segmentation that constructs cross-modal scene graphs and utilizes structured graph neural networks for entity perception fusion and relationship-driven interaction.
🎯 What it does: By introducing RGB sequences during the training phase and employing the cross-modal knowledge transfer framework X4D-SceneFormer, the understanding capability of 4D point cloud videos (which include the temporal dimension) is enhanced, allowing tasks to be completed using only point cloud data during inference.
🎯 What it does: A method for one-time reading of sentences and simultaneously predicting sentiment polarity for all aspects (One-to-Many ABSA) is proposed, called YORO;
YTCommentQA: Video Question Answerability in Instructional Videos
Saelyne Yang (Korea Advanced Institute of Science and Technology), Moontae Lee (LG AI Research)
CodeClassificationExplainability and InterpretabilityTransformerLarge Language ModelSupervised Fine-TuningVision Language ModelVideoTextMultimodality
🎯 What it does: A dataset named YTCommentQA has been constructed, containing real user questions from YouTube tutorial videos, with annotations indicating whether the questions can be answered in the video and the required modality (visual, script, or both).
Zero-1-to-3: Domain-Level Zero-Shot Cognitive Diagnosis via One Batch of Early-Bird Students towards Three Diagnostic Objectives
Weibo Gao (University of Science and Technology of China), Yuanjing He (Open University of China)
CodeDomain AdaptationTransformerLarge Language ModelTabular
🎯 What it does: Proposes the Zero-1-to-3 framework, which utilizes a single early student batch to achieve cross-domain zero-shot cognitive diagnosis, balancing diagnostic accuracy, signal propagation, and domain adaptation.
🎯 What it does: A zero-shot task adaptation method based on meta-learning is proposed, utilizing a small number of relevant features to learn a classifier for the target task.
Zhongjing: Enhancing the Chinese Medical Capabilities of Large Language Model through Expert Feedback and Real-World Multi-Turn Dialogue
Songhua Yang (Zhengzhou University), Hongying Zan (Zhengzhou University)
CodeGenerationReinforcement Learning from Human FeedbackTransformerLarge Language ModelSupervised Fine-TuningReinforcement LearningTextBiomedical Data
🎯 What it does: Developed the first end-to-end trained Chinese medical large language model Zhongjing, and constructed a dataset of 70,000 real doctor-patient multi-turn dialogues called CMtMedQA;
ZO-AdaMU Optimizer: Adapting Perturbation by the Momentum and Uncertainty in Zeroth-Order Optimization
Shuoran Jiang (Harbin Institute of Technology), Xiaobao Song (Institute of Data Security)
CodeOptimizationTransformerLarge Language ModelText
🎯 What it does: This paper proposes a zero-order optimization-based adaptive momentum and uncertainty adjustment operator (ZO-AdaMU), which improves gradient estimation by placing momentum on simulated perturbations, thereby reducing oscillation, alleviating overfitting, and enhancing convergence speed.