Główne pojęcia
Large language models benefit from multi-turn interactions with tools and natural language feedback, as shown by the MINT evaluation benchmark.
Streszczenie
The content introduces MINT, an evaluation benchmark for Large Language Models (LLMs) focusing on multi-turn interactions with tools and natural language feedback. It discusses the importance of evaluating LLMs in real-world scenarios where multiple rounds of interaction are required. The paper outlines the framework of MINT, including the use of external tools and simulated natural language feedback from GPT-4. It also presents findings from evaluating 20 LLMs, highlighting performance gains with tool use and feedback. The study reveals that better single-turn performance does not guarantee better multi-turn performance and identifies discrepancies between open-source and closed-source LLMs in multi-turn capabilities.
INTRODUCTION
- Introduction to the importance of multi-turn interactions for LLMs.
- Overview of MINT as an evaluation benchmark for LLMs.
EVALUATION FRAMEWORK
- Description of how MINT evaluates LLMs' task-solving abilities.
- Use of external tools and simulated natural language feedback.
- Construction of a subset of challenging instances requiring multi-turn interaction.
EXPERIMENT RESULTS
- Findings on the performance gains with tool use and natural language feedback.
- Comparison between open-source and closed-source LLMs in multi-turn interactions.
- Impact of supervised instruction fine-tuning (SIFT) and reinforcement learning from human feedback (RLHF).
DATA EXTRACTION
- "LLMs generally benefit from tools and language feedback, with performance gains..."
- "Better single-turn performance does not guarantee better multi-turn performance."
- "Among the evaluated LLMs, supervised instruction-finetuning (SIFT)..."
Statystyki
LLMs generally benefit from tools and language feedback, with performance gains...
Better single-turn performance does not guarantee better multi-turn performance.
Among the evaluated LLMs, supervised instruction-finetuning (SIFT)...