Summary System 2 Attention for Large Language Models arxiv.org
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System 2 Attention (S2A) enhances Large Language Models (LLMs) by improving input context, factuality, and reducing bias.
Slides
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Key Points
- Large Language Models (LLMs) have impressive capabilities but are prone to making mistakes due to weak reasoning abilities.
- LLMs can be influenced by irrelevant context or opinions in the input prompt, leading to erroneous judgments or sycophancy.
- System 2 Attention (S2A) is proposed as a method to address these issues by regenerating the input context to include only the relevant portions before attending to it.
- S2A outperforms standard attention-based LLMs in terms of factuality, objectivity, and reducing sycophancy.
- S2A significantly improves accuracy in tasks involving opinion or irrelevant information compared to baseline LLMs.
- Variations of S2A have been explored, but S2A remains the most effective method.
- S2A shows promise in improving the performance of LLMs by addressing issues related to attention and irrelevant context.
- Further research can explore optimizing S2A with fine-tuning, reinforcement learning, or other prompting techniques, as well as distillation of S2A into standard LLM generations.
Summaries
19 word summary
System 2 Attention (S2A) improves Large Language Models (LLMs) by regenerating relevant input context, enhancing factuality and reducing bias.
50 word summary
To address the issues of irrelevant context and biased responses in Large Language Models (LLMs), the authors propose System 2 Attention (S2A). S2A regenerates the input context to include only relevant portions, improving factuality, objectivity, and reducing sycophancy. S2A outperforms standard attention-based LLMs and shows promise in improving LLM performance.
128 word summary
Large Language Models (LLMs) can be influenced by irrelevant context or opinions, leading to errors and sycophantic responses. This is due to the attention mechanism used in LLMs, which assigns probability to irrelevant parts of the context. To address these issues, the authors propose System 2 Attention (S2A), which regenerates the input context to include only relevant portions before generating a response. S2A outperforms standard attention-based LLMs in terms of factuality, objectivity, and reducing sycophancy. The authors evaluate S2A on various tasks and find that it significantly improves accuracy compared to baseline LLMs. They explore variations of S2A but find that S2A remains the most effective method. Overall, S2A shows promise in improving LLM performance and further research can explore optimization techniques and distillation into standard LLM generations.
325 word summary
Large Language Models (LLMs) have impressive capabilities but are still prone to making mistakes due to weak reasoning abilities. One issue is that LLMs can be influenced by irrelevant context or opinions in the input prompt, leading to erroneous judgments or sycophancy. These problems stem from the attention mechanism used in LLMs, which tends to assign probability to a large portion of the context, including irrelevant parts. Additionally, LLMs often overly focus on repeated tokens and treat the context as a bag-of-words.
To address these issues, the authors propose System 2 Attention (S2A), a method that leverages LLMs' ability to reason in natural language and follow instructions. S2A works by regenerating the input context to include only the relevant portions before attending to it to generate the final response. The authors compare S2A with standard attention-based LLMs on tasks involving opinion or irrelevant information and find that S2A outperforms in terms of factuality, objectivity, and reducing sycophancy.
The authors describe different implementations of S2A and conduct experiments to evaluate its performance. They use modified versions of TriviaQA and longform argument generation tasks to assess factuality and objectivity. They also test S2A on math word problems with distracting sentences. The results show that S2A significantly improves accuracy in all tasks compared to baseline LLMs.
The authors also explore variations of S2A, such as not separating the context and question in the regenerated context, keeping the original context in addition to the regenerated context, and using instructed prompting. They find that while these variations have slight differences in performance, S2A remains the most effective method.
Overall, S2A shows promise in improving the performance of LLMs by addressing issues related to attention and irrelevant context. It provides a more deliberate attention mechanism that enhances factuality, objectivity, and reduces sycophancy. Further research can explore optimizing S2A with fine-tuning, reinforcement learning, or other prompting techniques. Distillation of S2A into standard LLM generations is also a potential area for future investigation.