Summary of Can Language Models Take a Hint? Prompting For Controllable Contextualized Commonsense Inference, by Pedro Colon-hernandez et al.
Can Language Models Take A Hint? Prompting for Controllable Contextualized Commonsense Inference
by Pedro Colon-Hernandez, Nanxi Liu, Chelsea Joe, Peter Chin, Claire Yin, Henry Lieberman, Yida Xin, Cynthia Breazeal
First submitted to arxiv on: 3 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 proposed “hinting” technique is a data augmentation method that enhances contextualized commonsense inference in language generation models. By employing both hard and soft prompts, hinting guides the inference process to generate assertions within a given story context. The technique is applied to two datasets, ParaCOMET and GLUCOSE, evaluating its impact on general and context-specific inference. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Generating believable statements about what might happen next in a story remains a tough challenge for modern language models. Researchers have tried to solve this problem by linking commonsense inferences to stories and training language generation models to do the same. One of the tricky parts is figuring out which part of the story should be the focus of an inferred statement. In this work, we develop a new way to make language generation models better at making these kinds of statements. We call it “hinting,” and it uses special prompts to help the model figure out what kind of information is most important. |
Keywords
» Artificial intelligence » Data augmentation » Inference
