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Found 10 papers, 5 papers with code
Intent Segmentation of User Queries Via Discourse Parsing
In this paper, we explore a new approach based on discourse analysis for the task of intent segmentation.
Advancing Parameter Efficiency in Fine-tuning via Representation Editing
Parameter Efficient Fine-Tuning (PEFT) has gained significant attention for its ability to achieve competitive results while updating only a small subset of trainable parameters.
Aligning Large Language Models with Human Preferences through Representation Engineering
Aligning large language models (LLMs) with human preferences is crucial for enhancing their utility in terms of helpfulness, truthfulness, safety, harmlessness, and interestingness.
VL-GPT: A Generative Pre-trained Transformer for Vision and Language Understanding and Generation
In combination with the existing text tokenizer and detokenizer, this framework allows for the encoding of interleaved image-text data into a multimodal sequence, which can subsequently be fed into the transformer model.
FollowEval: A Multi-Dimensional Benchmark for Assessing the Instruction-Following Capability of Large Language Models
In pursuit of this goal, various benchmarks have been constructed to evaluate the instruction-following capacity of these models.
Uni-Perceiver v2: A Generalist Model for Large-Scale Vision and Vision-Language Tasks
In this paper, we propose Uni-Perceiver v2, which is the first generalist model capable of handling major large-scale vision and vision-language tasks with competitive performance.
Uni-Perceiver-MoE: Learning Sparse Generalist Models with Conditional MoEs
To mitigate such interference, we introduce the Conditional Mixture-of-Experts (Conditional MoEs) to generalist models.
Uni-Perceiver: Pre-training Unified Architecture for Generic Perception for Zero-shot and Few-shot Tasks
The model is pre-trained on several uni-modal and multi-modal tasks, and evaluated on a variety of downstream tasks, including novel tasks that did not appear in the pre-training stage.
Complementary Relation Contrastive Distillation
Specifically, we estimate the mutual relation in an anchor-based way and distill the anchor-student relation under the supervision of its corresponding anchor-teacher relation.
Ranked #26 on
Knowledge Distillation
on ImageNet
Two-level Attention with Two-stage Multi-task Learning for Facial Emotion Recognition
In this paper, a two-level attention with two-stage multi-task learning (2Att-2Mt) framework is proposed for facial emotion estimation on only static images.
