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Found 9 papers, 3 papers with code
YODA: Teacher-Student Progressive Learning for Language Models
With the teacher's guidance, the student learns to iteratively refine its answer with feedback, and forms a robust and comprehensive understanding of the posed questions.
Gaining Wisdom from Setbacks: Aligning Large Language Models via Mistake Analysis
The rapid development of large language models (LLMs) has not only provided numerous opportunities but also presented significant challenges.
Improving End-to-End Speech Processing by Efficient Text Data Utilization with Latent Synthesis
For SLU, LaSyn improves our E2E baseline by absolute 4. 1% for intent classification accuracy and 3. 8% for slot filling SLU-F1 on SLURP, and absolute 4. 49% and 2. 25% for exact match (EM) and EM-Tree accuracies on STOP respectively.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
SELF: Self-Evolution with Language Feedback
SELF initiates with a meta-skill learning process that equips the LLMs with capabilities for self-feedback and self-refinement.
Aligning Large Language Models with Human: A Survey
(2) Training methodologies: a detailed review of the prevailing training methods employed for LLM alignment.
SPIRAL: Self-supervised Perturbation-Invariant Representation Learning for Speech Pre-Training
The student network is trained to output representation resembling that of the teacher.
CoCA-MDD: A Coupled Cross-Attention based Framework for Streaming Mispronunciation Detection and Diagnosis
We utilize conv-transformer structure to encode input speech in a streaming manner.
Conv-Transformer Transducer: Low Latency, Low Frame Rate, Streamable End-to-End Speech Recognition
To reduce computation cost, we gradually downsample acoustic input, also with the interleaved convolution layers.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
NEZHA: Neural Contextualized Representation for Chinese Language Understanding
The pre-trained language models have achieved great successes in various natural language understanding (NLU) tasks due to its capacity to capture the deep contextualized information in text by pre-training on large-scale corpora.
