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Found 4 papers, 1 papers with code
Planning, Creation, Usage: Benchmarking LLMs for Comprehensive Tool Utilization in Real-World Complex Scenarios
The recent trend of using Large Language Models (LLMs) as tool agents in real-world applications underscores the necessity for comprehensive evaluations of their capabilities, particularly in complex scenarios involving planning, creating, and using tools.
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.
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.
