Locally Typical Sampling (2202.00666v5)

Abstract: Today's probabilistic language generators fall short when it comes to producing coherent and fluent text despite the fact that the underlying models perform well under standard metrics, e.g., perplexity. This discrepancy has puzzled the language generation community for the last few years. In this work, we posit that the abstraction of natural language generation as a discrete stochastic process--which allows for an information-theoretic analysis--can provide new insights into the behavior of probabilistic language generators, e.g., why high-probability texts can be dull or repetitive. Humans use language as a means of communicating information, aiming to do so in a simultaneously efficient and error-minimizing manner; in fact, psycholinguistics research suggests humans choose each word in a string with this subconscious goal in mind. We formally define the set of strings that meet this criterion: those for which each word has an information content close to the expected information content, i.e., the conditional entropy of our model. We then propose a simple and efficient procedure for enforcing this criterion when generating from probabilistic models, which we call locally typical sampling. Automatic and human evaluations show that, in comparison to nucleus and top-k sampling, locally typical sampling offers competitive performance (in both abstractive summarization and story generation) in terms of quality while consistently reducing degenerate repetitions.

Locally Typical Sampling: An Information-Theoretic Approach to Language Generation

The paper "Locally Typical Sampling" addresses the perplexing issue of why modern probabilistic LLMs, despite their success in achieving low perplexity on various datasets, often produce text that is incoherent or repetitive when used as generators. The authors propose an innovative decoding strategy based on the concept of local typicality, inspired by human language use, to mitigate these shortcomings.

The central thesis of the paper is that, although these models are effective at estimating the probability of natural language strings, the traditional decoding strategies may not align well with the characteristics of typical human language. By viewing natural language generation as a discrete stochastic process, the paper argues that a more nuanced approach is needed to understand and improve the quality of text generation.

Key Contributions

• Information-Theoretic Perspective: The paper introduces an information-theoretical framework to analyze language generation, proposing the concept of local typicality. This notion is grounded in the idea that humans generate text aiming for balanced information efficiency and error minimization.
• Locally Typical Sampling Algorithm: The authors develop a new sampling algorithm that enforces local typicality in the generated text. This approach limits the sampling space to words whose information content closely matches the expected information content given prior context.
• Empirical Validation: Through experiments in abstractive summarization and story generation, the paper demonstrates that locally typical sampling consistently reduces repetitive sequences and improves the perceived quality of generated text. The method compares favorably against popular techniques like nucleus and top-$k$ sampling, showing competitive performance in human evaluations.

Methodology and Results

The paper formulates a language process view of probabilistic LLMs, allowing the use of concepts like entropy rate and typicality sets from information theory. The authors argue that traditional strategies, such as ancestral sampling, fail to produce human-like text due to their focus on high-probability sequences, which often result in dull or generic outputs.

The proposed locally typical sampling entails selecting words whose probability is near the conditional entropy, ensuring a balance between novelty and coherence. Experiments reveal that this method not only aligns generated text closer to human-like information rates but also enhances text quality and diversity, as evidenced by metrics such as rep values and human ratings.

Implications and Future Directions

The research outlined in this paper offers several implications for both theoretical and practical developments in AI and NLP:

• Theoretical Insights: By aligning language generation more closely with human cognitive processes, the locally typical sampling approach provides a framework that could inspire future models to incorporate psycholinguistic insights more deeply.
• Practical Benefits: The findings suggest that adopting this sampling strategy can significantly enhance the performance of existing LLMs, especially in creative and open-ended tasks, by producing more coherent and engaging outputs.
• Future Research: Future work could explore possible extensions of this approach, such as deterministic versions, adaptive mechanisms for entropy approximation, or integration with reinforcement learning techniques for continuous improvement.

In conclusion, the paper presents a compelling case for the integration of information-theoretic principles into the design of language generation systems, demonstrating that locally typical sampling offers a robust and efficient alternative to traditional decoding strategies. This advancement not only bridges a crucial gap between model perplexity and text quality but also sets a new trajectory for research into more human-like AI language systems.