Summary of Self-[in]correct: Llms Struggle with Discriminating Self-generated Responses, by Dongwei Jiang et al.
SELF-[IN]CORRECT: LLMs Struggle with Discriminating Self-Generated Responses
by Dongwei Jiang, Jingyu Zhang, Orion Weller, Nathaniel Weir, Benjamin Van Durme, Daniel Khashabi
First submitted to arxiv on: 4 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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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 A unified framework is proposed to compare the generative and discriminative capabilities of large language models (LLMs) on various tasks. The study investigates whether LLMs can consistently improve their previous outputs by discriminating among previously-generated alternatives, rather than simply generating initial responses. The findings suggest that while LLMs are capable of generating high-quality text, they do not reliably demonstrate better discriminative capabilities than generative ones. This challenges the notion that LLMs may be able to enhance their performance through self-judgment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are powerful tools for generating text. But can they get even better? One idea is that LLMs can look at what they’ve written before and pick the best option. This would mean they’re not just good at coming up with ideas, but also at choosing the best one. Researchers tested this idea by looking at how well different models did on a range of tasks. They found that while models are great at generating text, they don’t actually get better at picking the best option. This means that LLMs might not be able to improve their performance just by judging themselves. |
