TART: A plug-and-play Transformer module for task-agnostic reasoning (2306.07536v1)

Abstract: LLMs exhibit in-context learning abilities which enable the same model to perform several tasks without any task-specific training. In contrast, traditional adaptation approaches, such as fine-tuning, modify the underlying models for each specific task. In-context learning, however, consistently underperforms task-specific tuning approaches even when presented with the same examples. While most existing approaches (e.g., prompt engineering) focus on the LLM's learned representations to patch this performance gap, our analysis actually reveal that LLM representations contain sufficient information to make good predictions. As such, we focus on the LLM's reasoning abilities and demonstrate that this performance gap exists due to their inability to perform simple probabilistic reasoning tasks. This raises an intriguing question: Are LLMs actually capable of learning how to reason in a task-agnostic manner? We answer this in the affirmative and propose TART which generically improves an LLM's reasoning abilities using a synthetically trained Transformer-based reasoning module. TART trains this reasoning module in a task-agnostic manner using only synthetic logistic regression tasks and composes it with an arbitrary real-world pre-trained model without any additional training. With a single inference module, TART improves performance across different model families (GPT-Neo, Pythia, BLOOM), model sizes (100M - 6B), tasks (14 NLP binary classification tasks), and even across different modalities (audio and vision). Additionally, on the RAFT Benchmark, TART improves GPT-Neo (125M)'s performance such that it outperforms BLOOM (176B), and is within 4% of GPT-3 (175B). Our code and models are available at https://github.com/HazyResearch/TART .

• The paper introduces Tart, a synthetically trained reasoning module designed to perform probabilistic inference across diverse tasks.
• It demonstrates broad compatibility and scalability by enhancing models from 100M to 6B parameters and extending its use to vision and audio modalities.
• Tart narrows the in-context learning gap by effectively reducing reasoning deficiencies, achieving near task-specific tuning performance on multiple benchmarks.

Overview of Tart: A Plug-and-Play Transformer Module for Task-Agnostic Reasoning

The paper introduces Tart, an innovative modular approach aimed at enhancing the reasoning capabilities of LLMs in a task-agnostic manner. Traditional methods such as fine-tuning and prompt engineering have focused on task-specific adaptations, often lacking the flexibility and scalability required for broader applications. Tart addresses the persistent performance gap between in-context learning and task-specific tuning by emphasizing reasoning improvements rather than merely optimizing representations.

Core Contributions

1. Reasoning Module Training: The authors present a synthetically trained Transformer-based reasoning module designed to perform probabilistic inference across a diverse set of tasks. This module is trained exclusively using synthetic logistic regression tasks, allowing it to generalize without being explicitly fine-tuned for specific applications.
2. Model Compatibility and Scalability: Tart is demonstrated to enhance models from varied families (GPT-Neo, Pythia, BLOOM), across a wide size range (100M to 6B parameters), and even extends to different modalities, including vision and audio. It surpasses the performance of 176 billion parameter models like BLOOM when coupled with smaller models such as GPT-Neo (125M).
3. In-Context Learning Limitations: The research highlights that LLMs, while proficient in generating rich representations, frequently falter in executing reasoning tasks. By employing task-specific adapters, up to 90% of this gap can be closed without altering core model parameters—suggesting that the reasoning deficiency is a key bottleneck.
4. Performance and Benchmarking: The paper includes extensive evaluations over 14 diverse NLP tasks and showcases significant improvements where Tart, despite its task-agnostic framework, achieves near-parity with task-specific fine-tuning approaches. On benchmarks like RAFT, Tart displays exemplary performance, closely approaching that of models with orders of magnitude more parameters.

Implications and Future Work

The insights and methodologies introduced in this paper open various avenues for the future of AI development:

• Theoretical Generalization Analysis: The authors provide theoretical insights suggesting that the effectiveness of Tart hinges on minimizing distributional shifts between synthetic and real data representations. This theoretical groundwork offers a robust foundation for the development and assessment of future reasoning modules.
• Modality Expansion and Multi-Task Applications: Tart’s demonstrated efficacy across NLP, vision, and audio indicates the potential for adaptive models in multi-modal AI applications. Expanding this framework to handle complex multi-task environments will be pivotal.
• Broader Integration and Deployment: Practically, Tart could revolutionize the deployment scenarios of LLMs in edge computing and environments where resources are constrained, leveraging a single reasoning module across different applications without bespoke fine-tuning.

In conclusion, Tart represents a meaningful advancement in the quest for task-agnostic reasoning improvements within AI. By shifting the focus towards addressing reasoning deficiencies, the authors provide a scalable and efficient approach capable of redefining the adaptation strategies in LLMs. This paper holds promise for transforming how AI models are adapted to rapidly changing and diverse environments without sacrificing performance or incurring prohibitive computational costs. Future research could further explore extensions to additional reasoning paradigms and more sophisticated cognitive tasks.