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Found 12 papers, 6 papers with code
KNN-Contrastive Learning for Out-of-Domain Intent Classification
The Out-of-Domain (OOD) intent classification is a basic and challenging task for dialogue systems.
Data Mixing Laws: Optimizing Data Mixtures by Predicting Language Modeling Performance
Pretraining data of large language models composes multiple domains (e. g., web texts, academic papers, codes), whose mixture proportions crucially impact the competence of outcome models.
Data-freeWeight Compress and Denoise for Large Language Models
Significantly, our method is characterized by without necessitating additional involvement of any corpus, while simultaneously preserving orthogonality in conjunction with pruning and quantization methods.
Code Needs Comments: Enhancing Code LLMs with Comment Augmentation
The programming skill is one crucial ability for Large Language Models (LLMs), necessitating a deep understanding of programming languages (PLs) and their correlation with natural languages (NLs).
AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling
We introduce AnyGPT, an any-to-any multimodal language model that utilizes discrete representations for the unified processing of various modalities, including speech, text, images, and music.
Turn Waste into Worth: Rectifying Top-$k$ Router of MoE
To address the dropped tokens and padding, we propose the Rectify-Router, comprising the Intra-GPU Rectification and the Fill-in Rectification.
DenoSent: A Denoising Objective for Self-Supervised Sentence Representation Learning
These methods regularize the representation space by pulling similar sentence representations closer and pushing away the dissimilar ones and have been proven effective in various NLP tasks, e. g., semantic textual similarity (STS) tasks.
Discovering New Intents Using Latent Variables
In this paper, starting from the intuition that discovering intents could be beneficial to the identification of the known intents, we propose a probabilistic framework for discovering intents where intent assignments are treated as latent variables.
The Open-World Lottery Ticket Hypothesis for OOD Intent Classification
Most existing methods of Out-of-Domain (OOD) intent classification rely on extensive auxiliary OOD corpora or specific training paradigms.
What Dense Graph Do You Need for Self-Attention?
Transformers have made progress in miscellaneous tasks, but suffer from quadratic computational and memory complexities.
BBTv2: Towards a Gradient-Free Future with Large Language Models
By contrast, gradient-free methods only require the forward computation of the PTM to tune the prompt, retaining the benefits of efficient tuning and deployment.
Early Exiting with Ensemble Internal Classifiers
In this paper, we show that a novel objective function for the training of the ensemble internal classifiers can be naturally induced from the perspective of ensemble learning and information theory.
