Enabling small language models to solve complex reasoning tasks
NeutralArtificial Intelligence
- Recent advancements in language models (LMs) have shown improvements in tasks like image generation and trivia, yet they still struggle with complex reasoning tasks, exemplified by their inefficiency in solving Sudoku puzzles. While they can verify correct solutions, they fail to fill in the grid effectively.
- This limitation highlights the gap between current AI capabilities and human-like reasoning, emphasizing the need for further research and development in enhancing the reasoning abilities of smaller language models.
- The ongoing exploration of large language models (LLMs) reveals a broader challenge in AI, where despite significant progress, issues such as the symbol grounding problem and biases in reasoning persist. These challenges underline the necessity for innovative frameworks and methodologies to improve AI's reasoning and decision-making processes.
— via World Pulse Now AI Editorial System
