Researchers explore how large language models (LLMs) can reason without explicit prompting by altering the decoding process, revealing natural Chain of Thought (CoT) reasoning paths.
- LLMs can reason without explicit prompting by changing how they generate answers.
- Traditional methods include few-shot and zero-shot prompting for reasoning tasks.
- CoT reasoning can be revealed by considering alternative options usually ignored by the model.
- CoT decoding improves model performance on reasoning tasks without additional training.
- Allowing models to consider different options before deciding enhances reasoning.
- CoT decoding shows models are more confident in their final answers.
- CoT decoding is unsupervised and doesn't require additional training.
- CoT paths naturally occur but are tricky to identify among top choices.
- CoT paths often don't get the highest rankings in model probability assessments.
- Confidence levels indicated by logits help identify CoT paths.
- Longer decoding paths might contain CoT components, especially in math reasoning.
- Identifying answer spans in model responses is crucial for accurate CoT decoding.
- Aggregating top K decoding paths improves model performance.
- Sampling introduces randomness but isn't effective for finding CoT reasoning paths.
- CoT decoding significantly improves reasoning tasks, especially in math.
- Instruction-tuned models benefit from CoT decoding, enhancing their performance.
- Higher K values generally lead to better performance in CoT decoding.
- Models struggle with synthetic tasks not well-represented in training data.
- Accurate state tracking is essential for complex tasks like coin flip and Web of Lies.
- Few-shot CoT prompting brings natural CoT paths to the forefront.
- Aggregated path approach boosts accuracy compared to selecting the highest score path.
- CoT decoding reveals the model's intrinsic strategy without external prompts.
- Diverse beam search prioritizes diversity over accuracy in model generation.
- Contrastive decoding enhances reasoning performance by penalizing logits from smaller models.
- Context-aware decoding increases faithfulness by incorporating more context into the process.
- Efficiency-oriented techniques like speculative decoding could improve CoT decoding efficiency.
- LLMs can reason effectively by altering the decoding process, revealing natural CoT paths.
- CoT decoding enhances model confidence and accuracy without additional training or prompts.
- Aggregating multiple paths stabilizes results and improves reasoning task performance.
- Instruction-tuned models benefit from CoT decoding, showing inherent reasoning capabilities.
- Higher K values in decoding lead to better performance, revealing hidden CoT paths.
- Models struggle with synthetic tasks, highlighting the importance of training data representation.
- Accurate state tracking is crucial for complex reasoning tasks like coin flip and Web of Lies.
- Few-shot CoT prompting highlights the model's natural reasoning strategies.
- Diverse beam search and contrastive decoding enhance model generation quality and reasoning performance.
- "LLMs can reason without explicit prompting by changing how they generate answers."
- "CoT decoding improves model performance on reasoning tasks without additional training."
- "Allowing models to consider different options before deciding enhances reasoning."
- "CoT paths naturally occur but are tricky to identify among top choices."
- "Confidence levels indicated by logits help identify CoT paths."
- "Aggregating top K decoding paths improves model performance."
- "Sampling introduces randomness but isn't effective for finding CoT reasoning paths."
- "Instruction-tuned models benefit from CoT decoding, enhancing their performance."
- "Higher K values generally lead to better performance in CoT decoding."
- "Models struggle with synthetic tasks not well-represented in training data."
- "Accurate state tracking is essential for complex tasks like coin flip and Web of Lies."
- "Few-shot CoT prompting brings natural CoT paths to the forefront."
- "Aggregated path approach boosts accuracy compared to selecting the highest score path."
- "CoT decoding reveals the model's intrinsic strategy without external prompts."
- "Diverse beam search prioritizes diversity over accuracy in model generation."
- "Contrastive decoding enhances reasoning performance by penalizing logits from smaller models."
- "Context-aware decoding increases faithfulness by incorporating more context into the process."
- "Efficiency-oriented techniques like speculative decoding could improve CoT decoding efficiency."
- Allowing models to consider different options before deciding enhances reasoning capabilities.
- Aggregating multiple paths stabilizes results and improves reasoning task performance.
- Using higher K values in decoding leads to better performance, revealing hidden CoT paths.
- Employing diverse beam search prioritizes diversity over accuracy in model generation.
- Incorporating context-aware decoding increases faithfulness by adding more context into the process.
- LLMs can reason without explicit prompting by changing how they generate answers.
- Traditional methods include few-shot and zero-shot prompting for reasoning tasks.
- CoT reasoning can be revealed by considering alternative options usually ignored by the model.
- Confidence levels indicated by logits help identify CoT paths.
- Aggregating top K decoding paths improves model performance.
- Sampling introduces randomness but isn't effective for finding CoT reasoning paths.
- Instruction-tuned models benefit from CoT decoding, enhancing their performance.
- Higher K values generally lead to better performance in CoT decoding.
- Models struggle with synthetic tasks not well-represented in training data.
- Accurate state tracking is essential for complex tasks like coin flip and Web of Lies.
None mentioned.
Altering the decoding process reveals LLMs' natural Chain of Thought (CoT) reasoning, enhancing accuracy and confidence without additional training.
- Consider different options before deciding to enhance reasoning capabilities in LLMs.
- Aggregate multiple paths to stabilize results and improve reasoning task performance.
- Use higher K values in decoding to reveal hidden Chain of Thought (CoT) paths.
- Employ diverse beam search to prioritize diversity over accuracy in model generation.
- Incorporate context-aware decoding to increase faithfulness by adding more context into the process.