Summary of Comparing Zero-shot Self-explanations with Human Rationales in Text Classification, by Stephanie Brandl and Oliver Eberle
Comparing zero-shot self-explanations with human rationales in text classification
by Stephanie Brandl, Oliver Eberle
First submitted to arxiv on: 4 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 The paper explores the ability of instruction-tuned language models (LLMs) to generate self-explanations about their output, which do not require complex methods or gradient computations. The authors evaluate these self-explanations in two text classification tasks: sentiment classification and forced labour detection, using input rationales and comparing them to human annotations for plausibility and faithfulness. They also analyze the self-explanations of four LLMs, including Danish and Italian translations of the sentiment classification task. The results show that self-explanations align more closely with human annotations than layer-wise relevance propagation (LRP), while maintaining a comparable level of faithfulness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how language models can explain their answers to users. It’s like when you’re asked why a certain sentence was classified as positive or negative, and the model says something like “I looked at these words in the sentence that made me think it was happy.” The authors wanted to know if this way of explaining things works well. They tested it on two different tasks: guessing how someone feels about something (sentiment classification) and detecting signs of forced labor. They also translated some parts of the test into Danish and Italian to see if the explanations work just as well in other languages. The results show that when people check the explanations, they seem more believable than another way of explaining things called layer-wise relevance propagation. |
Keywords
» Artificial intelligence » Classification » Text classification
