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Found 10 papers, 2 papers with code
On Speculative Decoding for Multimodal Large Language Models
We show that a language-only model can serve as a good draft model for speculative decoding with LLaVA 7B, bypassing the need for image tokens and their associated processing components from the draft model.
HyperCLOVA X Technical Report
We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding.
Direct Alignment of Draft Model for Speculative Decoding with Chat-Fine-Tuned LLMs
In this paper, we propose a simple draft model training framework for direct alignment to chat-capable target models.
Recursive Speculative Decoding: Accelerating LLM Inference via Sampling Without Replacement
We empirically evaluate RSD with Llama 2 and OPT models, showing that RSD outperforms the baseline methods, consistently for fixed draft sequence length and in most cases for fixed computational budgets at LLM.
Unleashing the Creative Mind: Language Model As Hierarchical Policy For Improved Exploration on Challenging Problem Solving
Large Language Models (LLMs) have achieved tremendous progress, yet they still often struggle with challenging reasoning problems.
Painter: Teaching Auto-regressive Language Models to Draw Sketches
In this work, we apply LLMs to image generation tasks by directly generating the virtual brush strokes to paint an image.
Look, Remember and Reason: Grounded reasoning in videos with language models
Multi-modal language models (LM) have recently shown promising performance in high-level reasoning tasks on videos.
Deductive Verification of Chain-of-Thought Reasoning
In light of this, we propose to decompose a reasoning verification process into a series of step-by-step subprocesses, each only receiving their necessary context and premises.
Query-by-example on-device keyword spotting
A keyword spotting (KWS) system determines the existence of, usually predefined, keyword in a continuous speech stream.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Orthogonality Constrained Multi-Head Attention For Keyword Spotting
Augmenting regularization terms which penalize positional and contextual non-orthogonality between the attention heads encourages to output different representations from separate subsequences, which in turn enables leveraging structured information without explicit sequence models such as hidden Markov models.
