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Found 16 papers, 9 papers with code
Enhancing Speech Large Language Models with Prompt-Aware Mixture of Audio Encoders
Connecting audio encoders with large language models (LLMs) allows the LLM to perform various audio understanding tasks, such as automatic speech recognition (ASR) and audio captioning (AC).
Optimizing Speech Multi-View Feature Fusion through Conditional Computation
Recent advancements have highlighted the efficacy of self-supervised learning (SSL) features in various speech-related tasks, providing lightweight and versatile multi-view speech representations.
Adapting Large Language Models to Log Analysis with Interpretable Domain Knowledge
The increasing complexity of computer systems necessitates innovative approaches to fault and error management, going beyond traditional manual log analysis.
Hard-Synth: Synthesizing Diverse Hard Samples for ASR using Zero-Shot TTS and LLM
Existing research primarily utilizes additional text data and predefined speech styles supported by TTS models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Large Language Model Should Understand Pinyin for Chinese ASR Error Correction
Large language models can enhance automatic speech recognition systems through generative error correction.
Why Not Transform Chat Large Language Models to Non-English?
For the second issue, we propose a method comprising two synergistic components: low-rank adaptation for training to maintain the original LLM parameters, and recovery KD, which utilizes data generated by the chat LLM itself to recover the original knowledge from the frozen parameters.
Clustering and Ranking: Diversity-preserved Instruction Selection through Expert-aligned Quality Estimation
Given the significant resource allocation required for training and evaluating models, it is advantageous to have an efficient method for selecting high-quality IT data.
Using Large Language Model for End-to-End Chinese ASR and NER
In this work, we connect the Whisper encoder with ChatGLM3 and provide in-depth comparisons of these two approaches using Chinese automatic speech recognition (ASR) and name entity recognition (NER) tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
CoachLM: Automatic Instruction Revisions Improve the Data Quality in LLM Instruction Tuning
Instruction tuning is crucial for enabling Language Learning Models (LLMs) in responding to human instructions.
A Simple but Effective Bidirectional Framework for Relational Triple Extraction
Moreover, experiments show that both the proposed bidirectional extraction framework and the share-aware learning mechanism have good adaptability and can be used to improve the performance of other tagging based methods.
Community detection in censored hypergraph
In practice, a network may has censored (or missing) values and it is shown that censored values have non-negligible effect on the structural properties of a network.
A Novel Global Feature-Oriented Relational Triple Extraction Model based on Table Filling
Next, the mined global associations are integrated into the table feature of each relation.
A Three-Stage Learning Framework for Low-Resource Knowledge-Grounded Dialogue Generation
Neural conversation models have shown great potentials towards generating fluent and informative responses by introducing external background knowledge.
Knowledge-Grounded Dialogue with Reward-Driven Knowledge Selection
Knowledge-grounded dialogue is a task of generating a fluent and informative response based on both conversation context and a collection of external knowledge, in which knowledge selection plays an important role and attracts more and more research interest.
A Conditional Cascade Model for Relational Triple Extraction
Tagging based methods are one of the mainstream methods in relational triple extraction.
An Effective System for Multi-format Information Extraction
Finally, our system ranks No. 4 on the test set leader-board of this multi-format information extraction task, and its F1 scores for the subtasks of relation extraction, event extractions of sentence-level and document-level are 79. 887%, 85. 179%, and 70. 828% respectively.
