Summary of Llm-as-an-interviewer: Beyond Static Testing Through Dynamic Llm Evaluation, by Eunsu Kim et al.
LLM-as-an-Interviewer: Beyond Static Testing Through Dynamic LLM Evaluation
by Eunsu Kim, Juyoung Suk, Seungone Kim, Niklas Muennighoff, Dongkwan Kim, Alice Oh
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed LLM-as-an-Interviewer paradigm evaluates large language models (LLMs) through multi-turn interactions, providing feedback and follow-up questions to assess their performance. The approach mitigates data contamination by dynamically modifying datasets at the start of each interview. Six models are evaluated on MATH and DepthQA tasks, revealing insights into initial response quality, adaptability to feedback, and ability to address clarification requests. This framework addresses limitations of conventional methods like verbosity bias and inconsistency across runs. The Interview Report aggregates insights from the process, offering a comprehensive analysis of the LLM’s strengths and weaknesses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are being used more and more to help us with tasks like solving math problems or answering questions about depth in pictures. But how well do they really work? One way to figure this out is to have them talk to each other, kind of like an interview. This idea is called LLM-as-an-Interviewer, and it helps us understand what the models are good at and what they need to get better. We tested six different models using this approach on some math problems and questions about depth. The results showed that it’s a helpful way to see how well the models do things like give good initial answers or adapt to new information. |
