Summary of Concise Thoughts: Impact Of Output Length on Llm Reasoning and Cost, by Sania Nayab et al.
Concise Thoughts: Impact of Output Length on LLM Reasoning and Cost
by Sania Nayab, Giulio Rossolini, Marco Simoni, Andrea Saracino, Giorgio Buttazzo, Nicolamaria Manes, Fabrizio Giacomelli
First submitted to arxiv on: 29 Jul 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 Medium Difficulty summary: This paper focuses on improving the conciseness of large language model (LLM) outputs by analyzing the impact of output lengths on inference pipelines. The authors introduce novel metrics to evaluate the correct conciseness of a model and related prompting techniques, such as chain-of-thought (CoT). They propose Constrained-CoT (CCoT), a refined prompt engineering strategy that encourages models to produce more concise outputs. To understand the effects of CCoT, two additional scores are introduced: redundancy and information flow in generated answers. Experiments on pre-trained LLMs and multiple datasets demonstrate the benefits of these metrics and the effectiveness of CCoT across different models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This research helps make computer-generated text shorter and more understandable. Currently, large language models can answer complex questions, but they often write too much. This makes it hard to understand what they’re saying. The authors are trying to solve this problem by creating new ways to measure how well the models do when they’re forced to be concise. They also developed a special technique called Constrained-CoT that helps the models write shorter answers. By testing these methods on different language models and datasets, the researchers showed that their approach works well. |
Keywords
» Artificial intelligence » Inference » Large language model » Prompt » Prompting
