Summary of Fine-tuning Llms with Noisy Data For Political Argument Generation and Post Guidance, by Svetlana Churina et al.
Fine-Tuning LLMs with Noisy Data for Political Argument Generation and Post Guidance
by Svetlana Churina, Kokil Jaidka
First submitted to arxiv on: 25 Nov 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 investigates the effects of fine-tuning and prompting strategies on GPT-3.5 Turbo, a large language model, for generating politically sensitive content with reduced toxicity. Specifically, it uses subsets of the CLAPTON dataset, which contains labeled Twitter and Reddit data, to analyze how different approaches can mitigate incivility in automated text generation. The results show that fine-tuning models on Reddit data improves discussion quality, while combining noisy data leads to persistent toxicity. Additionally, prompting strategies can reduce specific toxic traits, such as personal attacks, but have limited broader impact. Overall, the study emphasizes the importance of high-quality training data and well-crafted prompts for reducing incivility and improving rhetorical quality in automated political discourse generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make computer-generated text more respectful and thoughtful when discussing politics online. The researchers used a special dataset that includes tweets and Reddit posts about politics, labeled as being justified, reciprocal, or uncivil. They found that if they fine-tuned the language model using this data, it got better at creating high-quality discussions. However, even with these improvements, there was still some negative talk. The team also tried different ways to prompt the model to behave more nicely, but it didn’t make a huge difference. Overall, the study shows that we need good quality training data and clever prompts to get computers to generate respectful online conversations. |
Keywords
» Artificial intelligence » Discourse » Fine tuning » Gpt » Language model » Large language model » Prompt » Prompting » Text generation
