Greedy Decodingは テキスト生成技術 commonly used in 自然言語処理 (NLP) and 人工知能 (AI) to produce coherent sequences of words. The method operates by selecting the word with the highest probability at each step of the generation process, based on the model’s predictions.
In more technical terms, greedy decoding starts with an initial input (or prompt) and iteratively generates text one token (word or character) at a time. At each timestep, the model evaluates the probability distribution over the vocabulary, which indicates how likely each possible next word is given the preceding context. The word with the highest probability is then selected and added to the generated sequence.
この方法はシンプルで計算効率が良いため、リアルタイムのテキスト生成を必要とするアプリケーションで広く使われています。ただし、greedyデコーディングには顕著な制限もあります。常に最も確率の高い単語を選ぶため、繰り返しや創造性に乏しい出力になりやすいです。生成されたテキストは多様性に欠け、他の可能性の低い選択肢を探索することで得られるかもしれないより良いシーケンスを見逃すことがあります。
To address these limitations, alternative decoding strategies such as Beam Search or サンプリング技術 (e.g., Top-k sampling, Top-p sampling) are often employed. These methods allow for a broader exploration of possible outputs, improving the overall quality and creativity of the generated text.