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Found 10 papers, 4 papers with code
SELF-[IN]CORRECT: LLMs Struggle with Refining Self-Generated Responses
Can LLMs continually improve their previous outputs for better results?
LeanReasoner: Boosting Complex Logical Reasoning with Lean
Large language models (LLMs) often struggle with complex logical reasoning due to logical inconsistencies and the inherent difficulty of such reasoning.
Enhancing Systematic Decompositional Natural Language Inference Using Informal Logic
Contemporary language models enable new opportunities for structured reasoning with text, such as the construction and evaluation of intuitive, proof-like textual entailment trees without relying on brittle formal logic.
Speech SIMCLR: Combining Contrastive and Reconstruction Objective for Self-supervised Speech Representation Learning
Self-supervised visual pretraining has shown significant progress recently.
TMT: A Transformer-based Modal Translator for Improving Multimodal Sequence Representations in Audio Visual Scene-aware Dialog
Audio Visual Scene-aware Dialog (AVSD) is a task to generate responses when discussing about a given video.
DiDiSpeech: A Large Scale Mandarin Speech Corpus
This paper introduces a new open-sourced Mandarin speech corpus, called DiDiSpeech.
Audio and Speech Processing
A Further Study of Unsupervised Pre-training for Transformer Based Speech Recognition
In this paper, we conduct a further study on MPC and focus on three important aspects: the effect of pre-training data speaking style, its extension on streaming model, and how to better transfer learned knowledge from pre-training stage to downstream tasks.
Improving Transformer-based Speech Recognition Using Unsupervised Pre-training
Speech recognition technologies are gaining enormous popularity in various industrial applications.
Towards End-to-End Code-Switching Speech Recognition
Code-switching speech recognition has attracted an increasing interest recently, but the need for expert linguistic knowledge has always been a big issue.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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A comparable study of modeling units for end-to-end Mandarin speech recognition
We find that all types of modeling units can achieve approximate character error rate (CER) in CTC model and the performance of Chinese character attention model is better than syllable attention model.
