Large Language Model Can Be a Foundation for Hidden Rationale-Based Retrieval (2412.16615v1)

Abstract: Despite the recent advancement in Retrieval-Augmented Generation (RAG) systems, most retrieval methodologies are often developed for factual retrieval, which assumes query and positive documents are semantically similar. In this paper, we instead propose and study a more challenging type of retrieval task, called hidden rationale retrieval, in which query and document are not similar but can be inferred by reasoning chains, logic relationships, or empirical experiences. To address such problems, an instruction-tuned LLM with a cross-encoder architecture could be a reasonable choice. To further strengthen pioneering LLM-based retrievers, we design a special instruction that transforms the retrieval task into a generative task by prompting LLM to answer a binary-choice question. The model can be fine-tuned with direct preference optimization (DPO). The framework is also optimized for computational efficiency with no performance degradation. We name this retrieval framework by RaHoRe and verify its zero-shot and fine-tuned performance superiority on Emotional Support Conversation (ESC), compared with previous retrieval works. Our study suggests the potential to employ LLM as a foundation for a wider scope of retrieval tasks. Our codes, models, and datasets are available on https://github.com/flyfree5/LaHoRe.

LLM as Foundation for Hidden Rationale-Based Retrieval

The research paper, "LLM Can Be a Foundation for Hidden Rationale-Based Retrieval," explores a nuanced dimension of retrieval tasks that transcends conventional factual retrieval. The proposed task, coined as hidden rationale retrieval, requires the retrieval of documents that, while not overtly similar to the query, can be substantiated through inferential reasoning, logical relationships, or empirical experience. The authors advocate using a LLM tuned through instruction, leveraging a cross-encoder architecture for these challenges. The methodology introduces an innovation by transforming the retrieval task into a generative one, through a binary-choice mechanism, fine-tuned further using Direct Preference Optimization (DPO).

Technical Methodology and Novel Contributions

The paper delineates a novel retrieval framework, RaHoRe, which stems from an LLM foundation. Key inventive steps include:

1. Transformation of Retrieval to Generative Tasks: By formulating retrieval as a generative task, the framework prompts the LLM to answer binary-choice questions related to retrieval relevance. This paradigm allows the model to utilize its inherent semantic and contextual understanding capabilities more effectively than traditional similarity-based methods.
2. Instruction-Driven Binary Choice: The researchers append instructions prompting a binary-choice question to ascertain retrieval relevance, enabling the model to evaluate based on inferred rationale rather than direct semantic similarity. The choice for relevance is graded using the probability of the binary outcomes.
3. Optimization with DPO: Going beyond standard fine-tuning modalities, the paper employs DPO to refine the LLM, drawing parallels to contrastive learning common in discrimination-based retrieval models, thus optimizing the model for retrieval tasks reliant on implicit rationale.
4. Computational Efficiency: The order of query and document is intentionally inverted to allow for cached prefix-decoding, ensuring that computational efficiency is not sacrificed despite the transformation to a generative architecture.

Experimental Validation and Results

The authors validate RaHoRe on datasets like Emotional Support Conversation (ESC) and various proprietary datasets, focusing on retrieving appropriate reply strategies or intents from dialogues. The model's performance is exemplary, especially in zero-shot learning scenarios—a testament to the inherent capabilities of LLMs to infer and adapt without extensive task-specific training. Fine-tuning results further consolidate its superiority, notably evident when leveraging DPO, which aligns well with the retrieval paradigm focused on hidden rationale.

Theoretical and Practical Implications

The theoretical implications of this research are profound. It suggests a shift from traditional retrieval approaches to those that can tap into the nuanced contextual and inferential capabilities of advanced LLMs. Practically, this opens pathways for deploying more intelligent information retrieval systems, especially in domains where queries and relevant information are contextually complex, such as dialogue systems for emotional support.

Future Research Directions

The success of RaHoRe indicates potential avenues for enhancing retrieval tasks in various AI applications, particularly those requiring sophisticated reasoning capabilities. The path forward may involve:

• Scalability Studies: Analyzing the framework's performance across a wider array of datasets and retrieval tasks.
• Efficiency Enhancements: Further exploring architectures or encoding strategies that marry the richness of LLMs with real-time retrieval efficiency.
• Interdisciplinary Applications: Applying the principles of hidden rationale retrieval in intersecting fields like legal document retrieval or scientific literature review, where context and inference play crucial roles.

In conclusion, the research paves a strategic course towards harnessing LLMs for complex retrieval tasks, marking a significant stride in the field's endeavor to tackle challenges that lie beyond simplistic similarity calculations. This work not only demonstrates the versatility and depth of LLMs in retrieval tasks but also sets a robust foundation for future explorations into reasoning-augmented AI systems.