Conversational ability assessment
Evaluating the conversational abilities of LLMs is crucial for chatbot and dialogue system applications. Let’s look at a key benchmark in this area: MT-Bench.
MT-Bench is a benchmark for evaluating multi-turn conversations. It assesses the model’s ability to maintain context and provide coherent responses over multiple turns.
MT-Bench evaluations often combine automated scoring with human assessments to ensure a more comprehensive evaluation of AI models, particularly for tasks requiring nuanced reasoning, coherence, and contextual understanding. While automated metrics provide consistency and scalability, human evaluations help capture qualitative aspects such as reasoning depth, relevance, and fluency, which may not be fully captured by automated methods alone.
Here’s a simplified approach to evaluate on MT-Bench:
import json def evaluate_mt_bench(model, tokenizer, data_path): with open(data_path...
