Summary The Poison of Alignment in Language Models arxiv.org
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The paper examines the impact of alignment on large language models in instruction tuning datasets, comparing curated and web-crawled datasets and highlighting the importance of data cleaning and deduplication for improved model performance.
Slides
Slide Presentation (11 slides)
Key Points
- Alignment in supervised fine-tuning datasets can limit the harmful content generation of large language models (LLMs).
- Aligned answers in instruction-tuned models significantly worsen the performance on reasoning benchmarks by 4-33%.
- Dataset cleaning and preparation are crucial for improving the performance of supervised instruction fine-tuning.
- Dataset cleaning methods, such as alignment removal, can enhance the performance of LLMs.
- The quality of data has a greater impact on model performance than data quantity.
Summaries
37 word summary
This paper explores how alignment affects large language models (LLMs) in instruction tuning datasets. It questions the superiority of curated datasets over web-crawled datasets and emphasizes the need for data cleaning and deduplication to enhance model performance.
40 word summary
This paper examines the effect of alignment on large language models (LLMs), specifically in instruction tuning datasets. It challenges the notion that curated datasets outperform web-crawled datasets and emphasizes the importance of data cleaning and deduplication for optimal model performance
208 word summary
This paper discusses the impact of alignment on the performance of large language models (LLMs). Alignment refers to the intentional method of reinforcing models to not respond to certain user inputs and is present in instruction tuning datasets such as OpenAssistant or Guanaco.
A study challenges the belief that curated datasets perform better than web-crawled datasets for language models. It also highlights the importance of cleaning and deduplicating data to achieve optimal model performance. The quality of data has a greater impact on model performance than
The excerpt discusses the data cleaning process for a language model dataset and provides details about the merging of datasets and the removal of alignment. The authors eliminated low-quality chats with non-informative content, short input texts, low average tokens per message, and
This study highlights the negative impact of alignment on the performance of language models. The presence of alignment in supervised fine-tuning (SFT) behaves similarly to dataset poisoning, leading to a significant decrease in reasoning abilities. The previous fine-tuning methods did
This text excerpt includes a list of references to various papers and preprints related to language models and their training. The references cover topics such as benchmark datasets, evaluation methods, data extraction, transfer learning, language modeling datasets, instruction tuning, finetuning