Allies: Prompting Large Language Model with Beam Search (2305.14766v3)

Abstract: With the advance of LLMs, the research field of LLM applications becomes more and more popular and the idea of constructing pipelines to accomplish complex tasks by stacking LLM API calls come true. However, this kind of methods face two limitations: narrow information coverage and low fault tolerance. In this work, we propose a novel method called ALLIES. Given an input query, ALLIES leverages LLMs to iteratively generate new queries related to the original query, enabling an iterative reasoning process. By iteratively refining and expanding the scope of the original query, ALLIES captures and utilizes hidden knowledge that may not be directly obtainable through retrieval. We take zero-shot open-domain question answering (ODQA) as an application scene and evaluate ALLIES on the widely-used benchmarks, such as NQ, WebQ and TriviaQA. The experimental results demonstrate that ALLIES significantly outperforms other zero-shot baselines, indicating its effectiveness in tackling those challenges. Our code is available in https://github.com/microsoft/SimXNS/tree/main/ALLIES.

Allies: Prompting LLMs with Beam Search

The paper "Allies: Prompting LLMs with Beam Search" presents a novel approach to enhancing the performance of LLMs in complex tasks by introducing a method called Allies. The method addresses crucial limitations of LLM applications, including narrow information coverage and low fault tolerance, through an iterative querying and refining process utilizing beam search.

Key Conceptual Developments

Allies is proposed as a solution to the restricted information retrieval capabilities of LLMs. It operates by iteratively generating new queries related to an original query, effectively expanding the information coverage. This enhanced querying process facilitates the capture of implicit knowledge that is not directly accessible through simple retrieval methods. Moreover, Allies provides a mechanism to iteratively refine these queries and responses, thereby improving the fault tolerance of the query-answering pipeline. This iterative cycle is crucial for handling the inherent uncertainties in question-answering tasks.

Methodological Approach

The Allies framework chiefly employs an interactive process involving several key stages:

1. Beam Initialization: Initial responses are generated using the LLM without additional evidence and through baseline retrieval models. These responses serve as points of departure for further refinement.
2. Beam Expansion: New queries are generated based on current understanding and are used to retrieve additional evidence. The expanded beam provides a richer set of information for reasoning.
3. Beam Pruning: Iterative pruning selections ensure only the most promising responses are retained.
4. Beam Termination: The iterative process continues until a sufficiently confident response is obtained or a predefined depth limit is reached, reducing computational overhead while maintaining quality output.

Evaluation and Results

The paper demonstrates the effectiveness of Allies within the context of zero-shot open-domain question answering (ODQA) tasks, applying it to widely recognized benchmarks like Natural Questions (NQ), WebQuestions (WebQ), and TriviaQA. Allies showcases a meaningful performance boost compared to various baselines. For instance, the method achieves significant improvements in Exact Match (EM) scores across all tested datasets, indicating the efficacy of enhanced query refinement through beam search.

Implications and Future Prospects

Practically, the Allies method has profound implications for improving the robustness and coverage of LLM-driven applications. The iterative refinement mechanism fosters a more comprehensive exploration of potential answers, a valuable trait for domains requiring detailed and expansive knowledge discovery such as legal search tasks and complex scientific inquiries. Theoretically, it highlights the benefits of integrating classical search techniques like beam search within modern machine learning frameworks, offering a bridge between probabilistic reasoning and deterministic search strategies.

Looking forward, there is significant scope for extending Allies to other complex reasoning tasks, including mathematical problem solving and multi-step instructional applications. Future work may also explore optimizing computational efficiency, potentially through hybrid models that strategically balance retrieval and generation costs or incorporating more refined scoring mechanisms that better handle ambiguity and uncertainty in generated responses. Additionally, leveraging fine-tuned models that are domain-specific could further enhance the applicability of Allies.

In summary, the Allies approach represents an informed step towards building more resilient and capable AI systems by creatively leveraging iterative refinement mechanisms combined with the adept querying capabilities of LLMs, laying the groundwork for future advancements in AI reasoning abilities.