MeTHanol: Modularized Thinking Language Models with Intermediate Layer Thinking, Decoding and Bootstrapping Reasoning (2409.12059v4)

Abstract: LLM can reasonably understand and generate human expressions but may lack of thorough thinking and reasoning mechanisms. Recently there have been several studies which enhance the thinking ability of LLMs but most of them are not data-driven or training-based. In this paper, we are motivated by the cognitive mechanism in the natural world, and design a novel model architecture called TaS which allows it to first consider the thoughts and then express the response based upon the query. We design several pipelines to annotate or generate the thought contents from prompt-response samples, then add language heads in a middle layer which behaves as the thinking layer. We train the LLM by the thoughts-augmented data and successfully let the thinking layer automatically generate reasonable thoughts and finally output more reasonable responses. Both qualitative examples and quantitative results validate the effectiveness and performance of TaS. Our code is available at https://anonymous.4open.science/r/TadE.

• The paper introduces a dual-layer training framework that embeds an intermediate thinking layer to guide final output generation.
• It demonstrates enhanced performance on Theory-of-Mind benchmarks, outperforming baseline models like GPT-4 on tasks like TOMI and BIGTOM.
• The approach has practical implications for improving AI applications in customer support, education, and creative content through nuanced reasoning.

Dual-Layer Training and Decoding of LLM with Simultaneously Thinking and Speaking

The paper introduces a novel framework known as Dual-Layer Training and Decoding for LLMs, which embodies the "Think and Speak" (TaS) protocol. Unlike existing methodologies that enhance the reasoning capabilities of LLMs, this approach is inherently data-driven and training-based, simulating human-like cognitive processes by integrating a thinking layer within the LLM architecture. This mechanism allows LLMs to systematically engage in deliberation before text generation, mimicking a process where thought precedes articulation.

Framework Overview

The proposed framework spans across three innovative phases: annotation, training, and inference. Initially, the model enriches prompt-response samples by generating intermediate "thought" annotations, utilizing both rule-based and human-annotated methods alongside auto-generation via advanced LLMs like GPT-4. During training, the model introduces a thought-generating layer, fine-tuning it to autonomously synthesize thought contents that guide the final response generation. The inference phase employs a two-pass methodology: thoughts are generated first, which then inform the synthesis of the final response.

Methodology and Technical Details

The training paradigm diverges from conventional models by implementing a dual-layer fine-tuning strategy. Specifically, a middle 'thinking' layer is deployed to craft thoughtful content from the input query, supplementing the final output constructed by the topmost layer with nuanced, contextually informed text. By adapting an architecture that unites both thought generation and output articulation, this work establishes an LLM capable of emulating more nuanced human-like reasoned responses.

Quantitatively, the efficacy of this methodology is substantiated by significant results across a range of evaluative tasks, notably surpassing baseline models, including GPT-4-based techniques, in Theory-of-Mind benchmarks such as TOMI and BIGTOM. This success underscores the model's enhanced capability to discern and simulate intricate human-like reasoning patterns, achieving a higher degree of understanding and empathy reflected in both qualitative and quantitative outputs.

Qualitative and Quantitative Performance

Qualitative assessments of the TaS model spotlight its prowess in simulating coherent, logical internal monologue processes similar to human cognition, effectively visualizing its thought generation process. This innovation not only holds promise for enhanced clarity and context in responses but also underscores significant improvements in tasks demanding complex reasoning, emotional nuance, and open-domain dialogue proficiency.

Implications and Future Directions

The implications of this paper are profound, extending the theoretical landscape of AI models by delving deeper into cognitive mimicry and reasoning emulation. Practically, this approach can facilitate advancements in AI applications requiring nuanced interaction, such as customer support chatbots, educational tools, and creative content generation systems.

Future developments could explore contrasting methodologies, including agent-based systems with separate LLMs for thinking and speaking, and integration with psychological paradigms grounding thought content validation. Moreover, extending this framework to encompass further cognitive tasks like emotional response and problem-solving via a broader array of datasets may yield additional insights into the cognitive capacities of LLMs.

In conclusion, the Dual-Layer Training and Decoding framework marks a considerable stride in advancing LLMs toward more sophisticated, intelligent systems that better mimic human cognitive processes, offering pathways to both comprehend and craft thought-informed responses across diverse communicative contexts. By establishing a more structured approach to LLM reasoning, this research paves a crucial path toward the next generation of AI-driven communication and interaction solutions.