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Found 15 papers, 10 papers with code
Learning From Correctness Without Prompting Makes LLM Efficient Reasoner
Large language models (LLMs) have demonstrated outstanding performance across various tasks, yet they still exhibit limitations such as hallucination, unfaithful reasoning, and toxic content.
Learning to Edit: Aligning LLMs with Knowledge Editing
Knowledge editing techniques, aiming to efficiently modify a minor proportion of knowledge in large language models (LLMs) without negatively impacting performance across other inputs, have garnered widespread attention.
Diffusion of Thoughts: Chain-of-Thought Reasoning in Diffusion Language Models
This work explores the integration of diffusion models and Chain-of-Thought (CoT), a well-established technique to improve the reasoning ability in autoregressive language models.
Planning, Creation, Usage: Benchmarking LLMs for Comprehensive Tool Utilization in Real-World Complex Scenarios
The recent trend of using Large Language Models (LLMs) as tool agents in real-world applications underscores the necessity for comprehensive evaluations of their capabilities, particularly in complex scenarios involving planning, creating, and using tools.
G-LLaVA: Solving Geometric Problem with Multi-Modal Large Language Model
We first analyze the limitations of current Multimodal Large Language Models (MLLMs) in this area: they struggle to accurately comprehending basic geometric elements and their relationships.
PerceptionGPT: Effectively Fusing Visual Perception into LLM
In this paper, we present a novel end-to-end framework named PerceptionGPT, which efficiently and effectively equips the VLLMs with visual perception abilities by leveraging the representation power of LLMs' token embedding.
DetGPT: Detect What You Need via Reasoning
Overall, our proposed paradigm and DetGPT demonstrate the potential for more sophisticated and intuitive interactions between humans and machines.
DYNAFED: Tackling Client Data Heterogeneity with Global Dynamics
Specifically, we first reserve a short trajectory of global model snapshots on the server.
ProGen: Progressive Zero-shot Dataset Generation via In-context Feedback
To improve the quality of dataset synthesis, we propose a progressive zero-shot dataset generation framework, ProGen, which leverages the feedback from the task-specific model to guide the generation of new training data via in-context examples.
Self-Guided Noise-Free Data Generation for Efficient Zero-Shot Learning
In this paradigm, the synthesized data from the PLM acts as the carrier of knowledge, which is used to train a task-specific model with orders of magnitude fewer parameters than the PLM, achieving both higher performance and efficiency than prompt-based zero-shot learning methods on PLMs.
Revisiting Over-smoothing in BERT from the Perspective of Graph
Recently over-smoothing phenomenon of Transformer-based models is observed in both vision and language fields.
ZeroGen: Efficient Zero-shot Learning via Dataset Generation
There is a growing interest in dataset generation recently due to the superior generative capacity of large pre-trained language models (PLMs).
AutoBERT-Zero: Evolving BERT Backbone from Scratch
Transformer-based pre-trained language models like BERT and its variants have recently achieved promising performance in various natural language processing (NLP) tasks.
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SparseBERT: Rethinking the Importance Analysis in Self-attention
A surprising result is that diagonal elements in the attention map are the least important compared with other attention positions.
UNISON: Unpaired Cross-lingual Image Captioning
In this work, we present a novel unpaired cross-lingual method to generate image captions without relying on any caption corpus in the source or the target language.
