Temporal Guidance for Large Language Models
Introduces Temporal Guidance (TeGu), leveraging temporal contrast to enhance LLM generation quality, achieving a 3.03% improvement on GSM8K.
Key Findings
Methodology
Proposed Temporal Guidance (TeGu), a novel temporal contrast strategy leveraging Multi-Token Prediction (MTP) to generate weaker amateur signals. Introduced Conditional MTP Projector (cMTPP) to support models without native MTP.
Key Results
- On GSM8K, TeGu improves by 3.03%, reaching 75.51%.
- On IFEval, TeGu achieves an 11.83% improvement, increasing from 15.16% to 26.99%.
- On MiMo-7B, TeGu boosts HEval+ performance by 4.88%, reaching 52.44%.
Significance
TeGu significantly enhances LLM generation quality, particularly in reasoning, coding, and instruction-following tasks, addressing high memory overhead issues in existing contrastive decoding methods.
Technical Contribution
Introduced temporal contrast strategy, overcoming limitations of layer-wise contrast; designed cMTPP module for models lacking MTP; employed Log-SumExp for stable probability computation.
Novelty
First to leverage temporal contrast signals for LLM guidance, eliminating dual-model overhead in standard CD and addressing DoLa's instability in smaller models.
Limitations
- Limited improvement on factual tasks like TruthfulQA, primarily benefits reasoning tasks.
- Dependency on MTP modules may restrict applicability to certain models.
Future Work
Future work could explore more complex temporal offset combinations or integrate TeGu with retrieval modules for factual tasks.
AI Executive Summary
Current LLM generation often suffers from repetition, generic responses, and hallucinations. Existing contrastive decoding methods improve quality by contrasting expert and amateur outputs but require dual models, leading to high computational overhead.
This paper introduces Temporal Guidance (TeGu), leveraging temporal contrast signals for generation. TeGu uses Multi-Token Prediction (MTP) to create weaker amateur signals and designs a lightweight Conditional MTP Projector (cMTPP) to avoid multi-network overhead. Experiments show TeGu significantly outperforms greedy decoding and DoLa across reasoning, coding, and instruction-following tasks.
While TeGu has limited impact on factual tasks, its robustness and efficiency in long-horizon reasoning make it a promising direction for future research, potentially integrating retrieval modules for broader applicability.
Deep Analysis
Background
LLMs have excelled in reasoning and creative tasks but face issues like repetition and hallucinations. Contrastive Decoding improves quality by contrasting expert and amateur outputs but requires dual models, increasing memory overhead. Internal methods like DoLa attempt layer-wise contrast but fail on smaller models.
Core Problem
Existing contrastive decoding methods require dual models, leading to high memory overhead and inference latency. Layer-wise contrast methods like DoLa are unstable on smaller models, limiting universality.
Innovation
Introduced Temporal Guidance (TeGu), leveraging temporal contrast signals; designed lightweight Conditional MTP Projector (cMTPP) for models without native MTP; employed Log-SumExp for stable probability computation.
Methodology
- �� Use Multi-Token Prediction (MTP) to generate weaker amateur signals.
- �� Design cMTPP module to condition historical hidden states on temporal offsets.
- �� Apply Log-SumExp for stable probability distribution computation.
- �� Contrast expert and amateur signals during inference to update probabilities.
Experiments
Experiments conducted on GSM8K, IFEval, and other datasets. Baselines include greedy decoding, standard CD, and DoLa. cMTPP trained using DeepSpeed framework, optimizing cross-entropy and knowledge distillation losses while freezing the LLM backbone.
Results
TeGu improves GSM8K performance by 3.03% and IFEval by 11.83%. On MiMo-7B, TeGu boosts HEval+ performance by 4.88%.
Applications
TeGu is suitable for reasoning, coding, and instruction-following tasks, especially in scenarios requiring long-horizon reasoning.
Limitations & Outlook
TeGu has limited impact on factual tasks and depends on MTP modules, potentially restricting applicability. Future work could integrate retrieval modules for improvement.
Plain Language Accessible to non-experts
Imagine a kitchen where an expert chef uses freshly chopped ingredients to decide the next step, while an amateur chef guesses based on earlier preparations. TeGu acts like a smart assistant, comparing both suggestions to choose the best step, avoiding repetition or mistakes.
ELI14 Explained like you're 14
Imagine playing a strategy game! The expert player makes precise decisions based on the latest moves, while the newbie guesses based on earlier ones. TeGu is like a super teammate that compares both strategies and picks the best one to help you win!
Glossary
Contrastive Decoding
Enhances generation quality by contrasting expert and amateur outputs.
Used to address repetition and hallucination in LLMs.
Multi-Token Prediction
Predicts multiple future tokens simultaneously.
Generates weaker amateur signals for contrast.
Conditional MTP Projector
Lightweight module supporting models without native MTP.
Generates temporal offset amateur signals.
Log-SumExp
Stable probability computation method.
Used for weighted amateur signal aggregation.
Adaptive Plausibility Constraint
Prevents zero-probability issues in contrastive operations.
Ensures stability during generation.
Open Questions Unanswered questions from this research
- 1 How to improve TeGu's performance on factual tasks?
- 2 Can retrieval modules enhance TeGu's generation quality?
Applications
Immediate Applications
Mathematical Reasoning
Improves complex problem-solving quality for education and research.
Code Generation
Optimizes coding tasks for software development automation.
Long-term Vision
General AI Generation
Enhances LLM long-horizon reasoning, advancing general AI capabilities.
Abstract
Contrastive Decoding (CD) enhances the generation quality of large language models (LLMs) but incurs significant additional computational overhead due to the need for an auxiliary model. Existing internal self-contrastive decoding methods, such as Decoding by Contrasting Layers (DoLa), focus on discrepancies across different layers, which are notably unstable on small-scale models. In this work, based on the observation that LLMs exhibit local preferences, we propose a novel contrastive guidance strategy along the temporal dimension, namely Temporal Guidance (TeGu). Our method ingeniously leverages Multi-Token Prediction (MTP) to construct weaker amateur predictions for model self-contrast. To standardize the implementation of this mechanism, we further introduce a lightweight Conditional MTP Projector (cMTPP), which avoids maintaining multiple independent networks as required by other MTP modules. Across various model series and benchmarks, TeGu achieves significant performance improvements while maintaining low additional memory consumption and computational overhead.