Improving Listwise Ranking in Large Language Models through Permutation Self-Consistency

Core Concepts

Permutation self-consistency, a novel decoding technique, can improve the quality, consistency, and position invariance of listwise ranking in black-box large language models.

Abstract

The paper proposes a novel decoding technique called "permutation self-consistency" to improve the listwise ranking ability of large language models (LLMs). The key idea is to marginalize out the positional biases in LLMs by repeatedly shuffling the input list, passing it through the LLM, and then aggregating the resulting rankings.
The authors first demonstrate that LLMs exhibit positional biases, especially in the middle of long input lists, which can lead to poor ranking performance. To address this, they introduce permutation self-consistency, which has two main steps:

Construct a diverse set of output rankings by randomly permuting the input list and passing it through the LLM multiple times.
Aggregate these output rankings into a single central ranking that minimizes the sum of Kendall tau distances to all the individual rankings, effectively marginalizing out the positional biases.

The authors provide theoretical guarantees, showing that the Kemeny-Young optimal ranking used in the aggregation step can recover the true ranking under certain noise distributions.
Empirically, the authors evaluate permutation self-consistency on three sorting tasks (math expressions, words, and sentences) and two passage reranking datasets. They consistently observe improvements of up to 34-52% for the Mistral model, 7-18% for GPT-3.5, and 8-16% for LLaMA v2 (70B) compared to conventional inference. The authors also conduct analyses to justify their design choices, such as the number of aggregated rankings and the use of Kemeny ranking over alternative methods.
Overall, the paper introduces a novel and effective technique for improving the listwise ranking capabilities of black-box LLMs, with potential applications in various domains that require high-quality ranking, such as information retrieval, recommendation systems, and question answering.

Stats

The correct output order for the example prompt on shrews is (2, 3, 1), from most to least relevant.
LLMs tend to get "lost in the middle" of long input lists and use the middle portion poorly, leading to misranking.
Prompt order can also affect the quality of LLM outputs, with some orders outperforming others.

Quotes

"Large language models (LLMs) exhibit positional bias in how they use context, which especially affects listwise ranking."
"Our key idea is to marginalize out different list orders in the prompt to produce an order-independent ranking with less positional bias."
"Theoretically, we prove the robustness of our method, showing convergence to the true ranking under random perturbations."

Key Insights Distilled From

Found in the Middle: Permutation Self-Consistency Improves Listwise Ranking in Large Language Models

by Raphael Tang... at arxiv.org 04-23-2024

https://arxiv.org/pdf/2310.07712.pdf

Deeper Inquiries

How could permutation self-consistency be extended to other types of language model tasks beyond listwise ranking, such as text generation or classification?

Permutation self-consistency (PSC) can be adapted to various language model tasks by modifying the input prompt structure and the aggregation method. For text generation tasks, PSC can be applied by shuffling the input prompts or context and generating multiple outputs for each permutation. The central ranking can then be determined based on the aggregated outputs, reducing biases related to prompt order. In classification tasks, PSC can be utilized by randomizing the order of input features or instances and aggregating the model predictions to obtain a more robust and unbiased classification result. By incorporating PSC into these tasks, the models can benefit from reduced positional biases and improved overall performance.

What are the potential limitations or drawbacks of the permutation self-consistency approach, and how could they be addressed in future work?

One potential limitation of the permutation self-consistency approach is the increased computational cost associated with generating multiple rankings for each input permutation. This can lead to longer inference times and higher resource requirements, especially when using large language models. To address this limitation, future work could focus on optimizing the aggregation process and exploring more efficient ways to generate diverse outputs without significantly increasing computational overhead. Additionally, the scalability of PSC to larger datasets and models could be improved by developing parallel processing techniques or leveraging distributed computing resources.
Another drawback of PSC is its reliance on the assumption of random noise distributions in the rankings, which may not always hold true in real-world scenarios. Future research could investigate the robustness of PSC to different types of noise and develop methods to handle non-random biases in the rankings effectively. Additionally, the generalizability of PSC across various tasks and model architectures could be further explored to ensure its applicability in diverse settings.

Could the insights from this paper on positional biases in LLMs be leveraged to improve other aspects of language model performance, such as in-context learning or few-shot adaptation?

The insights gained from studying positional biases in large language models (LLMs) can indeed be leveraged to enhance other aspects of language model performance, such as in-context learning and few-shot adaptation. By understanding how positional biases impact the model's ranking and decision-making processes, researchers can develop strategies to mitigate these biases and improve the model's overall performance.
In the context of in-context learning, the findings on positional biases can inform the design of prompts and input structures to minimize the impact of biases on the model's responses. By carefully crafting prompts and considering the positional relationships between input elements, researchers can ensure more accurate and contextually relevant outputs from the model.
Similarly, in few-shot adaptation scenarios, the insights on positional biases can guide the selection and organization of training examples to provide a more diverse and representative set of inputs to the model. By addressing biases related to the order of input instances or features, models can adapt more effectively to new tasks and domains with limited training data.
Overall, leveraging the insights from positional biases in LLMs can lead to more robust and reliable performance across a wide range of language model applications, ultimately enhancing the model's ability to understand and generate natural language text in various contexts.