Summary of Evaluating Consistencies in Llm Responses Through a Semantic Clustering Of Question Answering, by Yanggyu Lee et al.
Evaluating Consistencies in LLM responses through a Semantic Clustering of Question Answering
by Yanggyu Lee, Jihie Kim
First submitted to arxiv on: 20 Oct 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 This paper tackles the issue of inconsistent outputs from Large Language Models (LLMs), which can erode user trust. The researchers propose a new approach to evaluate semantic consistency by comparing alternative techniques. They demonstrate the effectiveness of leveraging external knowledge or improving LLM performance using Zero-shot-CoT on enhancing consistency across different domains and question-answering tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is all about making sure that when you ask a large language model a question, it gives you the same answer every time! Right now, models can be pretty inconsistent, which means they might give you different answers even if you ask the same question. The researchers are trying to fix this by coming up with a new way to measure how consistent the model’s responses are. They’re testing two different methods to see which one works best. |
Keywords
» Artificial intelligence » Large language model » Question answering » Zero shot
