Natural Language Generation: Decoding Techniques and Training Challenges

This lecture focuses on the intricacies of natural language generation, particularly decoding methods and training challenges. The instructor begins by discussing greedy decoding techniques, including Argmax and Beam Search, which allow for the selection of tokens based on probability distributions generated by the model. The limitations of greedy methods are highlighted, particularly the inability to revise previous decisions, leading to potentially awkward sequences. The lecture then transitions to sampling methods, such as Top-k and Top-p sampling, which introduce randomness into the generation process, enhancing diversity in outputs. The instructor addresses training challenges, including exposure bias and the need for reinforcement learning to improve model performance. The session concludes with a discussion on the importance of balancing maximum likelihood estimation with reinforcement learning to ensure coherent and diverse text generation. Overall, the lecture provides a comprehensive overview of the techniques and challenges in the field of natural language generation.