Summary of Revealing the Inherent Instructability Of Pre-trained Language Models, by Seokhyun An et al.
Revealing the Inherent Instructability of Pre-Trained Language Models
by Seokhyun An, Minji Kim, Hyounghun Kim
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 This paper investigates a novel approach to fine-tuning large language models (LLMs) for instructability, building upon their existing multitask learning capabilities during pre-training. The proposed Response Tuning (RT) method removes instruction-response pairs and instead focuses on establishing response distributions, demonstrating that RT models can effectively respond to diverse instructions and exhibit helpfulness comparable to instruction-tuned counterparts. Additionally, the models learn to recognize and reject unsafe queries by leveraging refusal conditions learned from training responses. These findings support the hypothesis that pre-trained LLMs have inherent capabilities for comprehending and addressing instructions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how to make large language models better at understanding and following instructions. It’s like teaching a super smart AI how to do tasks correctly, using its existing knowledge as a starting point. The new method, called Response Tuning, helps the AI focus on what it should say in response to an instruction, rather than also considering the instruction itself. This makes the AI more helpful and able to recognize when it shouldn’t provide information. |
Keywords
» Artificial intelligence » Fine tuning
