Summary of Inference to the Best Explanation in Large Language Models, by Dhairya Dalal et al.
Inference to the Best Explanation in Large Language Models
by Dhairya Dalal, Marco Valentino, André Freitas, Paul Buitelaar
First submitted to arxiv on: 16 Feb 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 proposes a novel framework called IBE-Eval to improve the interpretation and evaluation of Large Language Models’ (LLMs) explanations. The framework draws inspiration from philosophical accounts on Inference to the Best Explanation (IBE), combining explicit logical and linguistic features such as consistency, parsimony, coherence, and uncertainty to estimate the plausibility of natural language explanations. The authors conduct extensive experiments on Causal Question Answering (CQA), using GPT 3.5 and Llama 2 to generate competing explanations, which are then evaluated by IBE-Eval. The results show that IBE-Eval can identify the best explanation with up to 77% accuracy, outperforming a baseline approach while being more efficient and interpretable. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how large language models work better. It creates a new way to check if an explanation is good or not by looking at things like consistency, simplicity, and clarity. The authors tested this idea on some questions about causes and effects, and it did really well! It was able to pick the best explanation most of the time, even when compared to another smart model. This could be important for making sure we understand what these models are telling us. |
Keywords
» Artificial intelligence » Gpt » Inference » Llama » Question answering
