Unused information in token probability distribution of generative LLM: improving LLM reading comprehension through calculation of expected values (2406.10267v2)

Abstract: LLM text decoding is key component for perceived LLM quality. We demonstrate two experiments showing that decoding methods could be improved by manipulation of token probabilities. First, we test few LLM on SummEval summary scoring dataset, to measure reading comprehension. We compare scores from greedy decoding to expected values over the next token distribution. We scale logits by large temperature to increase the entropy of scores. This allows strong improvement of performance on SummEval (in terms of correlations to human judgement). We see improvement from 6-8% to 13-28% for 7B Mistral and from 20%-46% to 37%-56% for Mixtral, beating GPT 4 0314 result on two metrics. Part of the gain seems related to positional bias. Secondly, we use probability-based tree sampling algorithm, to examine all most probable generations for given prompt.

• The paper introduces an expected value decoding strategy that utilizes the full token probability distribution to improve reading comprehension.
• It employs a probability-based tree sampling analysis to explore model behavior and enhance output coherence, outperforming models like GPT-4 in key metrics.
• Quantitative results on the SummEval dataset show Mixtral’s coherence metric rising from 0.428 to 0.485, confirming the method’s effectiveness.

Analysis and Implications of Enhanced Decoding Techniques for Generative LLMs

The paper by Krystian Zawistowski examines the potential improvements in decoding strategies for reading comprehension within generative LLMs by optimizing the token probability distributions during inference. The emphasis is on leveraging unused information in token probability distributions, specifically through expected value calculations, to enhance comprehension and generation quality.

Key Contributions and Methodology

The paper makes significant strides in revealing unused information within token probability distributions. Two primary contributions are noted:

1. Expected Value Decoding: The research challenges traditional greedy decoding, which selects the highest-probability token, by employing an expected value methodology over the token probability distribution. This approach accounts for the entire distribution rather than focusing on a single token, thus enabling LLMs to produce more contextually relevant and coherent responses without over-committing to potentially spurious signals. The effectiveness of this method is quantified using the SummEval summary scoring dataset, demonstrating enhanced alignment with human judgments in reading comprehension tasks. Notably, for Mixtral, this method outperformed GPT-4 on relevance and coherence, showing Pearson correlation improvements from 20%-46% to 37%-56%.
2. Tree-Based Sampling Analysis: Complementing the expected value approach, a probability-based tree sampling methodology is introduced. This process explores possible completions by assessing the most probable generations, providing insights into LLM behavior across diverse prompts and configurations. It suggests the potential of these methodologies in evaluating attention models and overall text coherence, furthering the understanding of the impacts of temperature settings and entropy on model outputs.

Numerical Findings

The empirical evaluation exhibits substantial numerical gains, with Mixtral showing a remarkable improvement in human-comparable metrics over baseline models such as GPT-3.5 and GPT-4. For instance, the coherence metric for Mixtral using the expected value method reached 0.485, compared to 0.428 with GPT-4. These results underscore the efficiency of integrating token probability information to fine-tune output accuracy and consistency.

Theoretical and Practical Implications

The theoretical underpinning challenges the conventionally static application of temperatures in sampling techniques, advocating for dynamic, context-driven adjustments. This introduces a more nuanced interpretation of human-like text generation patterns, which do not always correlate with the highest modeled probabilities. The paper's findings denote a pivotal role for dynamically adjusted decoding parameters, particularly in applications where specific qualitative attributes are prioritized, such as automated summarization, artificial intelligence-driven content creation, and reading comprehension systems.

Practically, these methodologies offer a scalable solution for enhancing LLM performance in constrained environments, such as edge devices and cost-sensitive deployment scenarios, by leveraging quantized models. This could pave the way for more efficient applications in areas like RAG (retrieval-augmented generation) and other AI-driven services.

Future Directions

The discussion opens avenues for exploring adaptive decoding methods that respect the versatility and unpredictability of human language. Future research may focus on:

• Optimization of Temperature Scaling: Dynamically manipulating temperature based on contextual decoding objectives.
• Enhanced Output Control: Implementing safeguards against unwanted continuum in responses and exploring taboo sampling strategies.
• Neural Network Architecture Analysis: Investigating the softmax bottlenecks within attention mechanisms to improve output originality and variability.

In sum, the paper posits a robust framework that not only challenges current decoding norms but also extends new methodologies for enhancing generative text inference, with wide-reaching implications for AI language generation and beyond.