This study introduces an automated evaluation method for large language model (LLM) based chatbots in educational settings, utilizing LLM-as-a-Judge to assess their performance. Our results demonstrate the efficacy of this approach in evaluating the accuracy of three LLM-based chatbots (Llama 3 70B, ChatGPT 4, Gemini Advanced) across two subjects: history and biology. The analysis reveals promising performance across different subjects. On a scale from 1 to 5 describing the correctness of the judge itself, the LLM judge’s average scores for correctness when evaluating each chatbot on history related questions are 3.92 (Llama 3 70B), 4.20 (ChatGPT 4), 4.51 (Gemini Advanced); for biology related questions, the average scores are 4.04 (Llama 3 70B), 4.28 (ChatGPT 4), 4.09 (Gemini Advanced). This underscores the potential of leveraging the LLM-as-a-judge strategy to evaluate the correctness of responses from other LLMs.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:lnu-131267 |
| Date | January 2024 |
| Creators | Duan, Yuyao, Lundborg, Vilgot |
| Publisher | Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM) |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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