Are Large Language Models Capable of Accurately Judging the Utility of Evidence for Open-Domain Question Answering?

Core Concepts

Large language models (LLMs) can distinguish between the relevance and utility of passages in supporting open-domain question answering, and their utility judgments can provide more valuable guidance than relevance judgments in identifying ground-truth evidence necessary for answering questions. However, the performance of LLMs in utility judgments is affected by various factors in the instruction design, such as the input form of passages, the sequence of input between the question and passages, and additional requirements like chain-of-thought and reasoning.

Abstract

The paper investigates the capabilities of large language models (LLMs) in judging the utility of evidence for open-domain question answering (QA). The key highlights are:

Experimental Setup:

The authors introduce the "utility judgments" task, where LLMs are prompted to identify supporting evidence with utility for answering a given question.
They construct two benchmark datasets, one with ground-truth inclusion (GTI) and the other with ground-truth uncertainty (GTU), to facilitate the study and evaluation of utility judgments.
They design various prompts to guide LLMs in making utility and relevance judgments, as well as prompts for answer generation.

Key Findings:

LLMs can distinguish between utility and relevance, and utility judgments provide more valuable guidance than relevance judgments in identifying ground-truth evidence.
LLMs exhibit a preference for selecting ground-truth evidence with utility when confronted with entity substitution-based counterfactual passages, compared to generated counterfactual passages.
The performance of utility judgments varies across different LLMs, with ChatGPT standing out as the most capable. There is a consistent improvement in utility judgments as the model scale increases.
Listwise approaches demonstrate superior performance compared to pointwise and pairwise approaches. LLMs are sensitive to the position of the ground-truth evidence in the input list.
Incorporating chain-of-thought, reasoning, and answer generation requirements in the prompts can impact the performance of utility judgments.

Practical Implications:

Employing LLMs as zero-shot utility judges or relevance judges proves more advantageous for answer generation than directly utilizing dense retrieval.
To reduce the dependency of LLMs on the position of ground-truth evidence, the authors propose a k-sampling listwise approach that combines multiple utility judgments results to derive the final outcome, thereby facilitating subsequent answer generation.

Stats

"The paleo diet primarily consisted of locally sourced wild game and foraged plants, emphasizing a high protein and low carbohydrate intake." (Counterfactual passage)
"After Margaret Thatcher became Prime Minister in May 1979, the legislation to implement the Right to Buy was passed in the Housing Act 1980." (Ground-truth evidence)
"A paleo diet, or paleolithic diet, is a modern diet designed to emulate the diet of wild animals and plants eaten by humans during the Paleolithic era, or as far as this is possible in the present day." (Ground-truth evidence)

Quotes

"Utility and relevance are distinct concepts: (i) Relevance signifies a connection between information and a context or question, and (ii) utility refers to the practical benefits of downstream tasks derived from consuming the information."
"LLMs may exhibit a preference for selecting ground-truth evidence with utility when confronted with entity substitution-based counterfactual passages, compared to generated counterfactual passages."
"Employing LLMs as zero-shot utility judges or relevance judges proves more advantageous for answer generation than directly utilizing dense retrieval."

Key Insights Distilled From

Are Large Language Models Good at Utility Judgments?

by Hengran Zhan... at arxiv.org 03-29-2024

https://arxiv.org/pdf/2403.19216.pdf

Deeper Inquiries

How can the performance of utility judgments be further improved, especially for open-source LLMs?

To improve the performance of utility judgments for open-source LLMs, several strategies can be implemented:

Fine-tuning: Fine-tuning the open-source LLMs on specific utility judgment tasks can enhance their performance. By training the models on relevant datasets and tasks, they can learn to better distinguish between utility and relevance in passages.
Data Augmentation: Increasing the diversity and quantity of training data can help the models generalize better to different types of passages and improve their utility judgment capabilities.
Ensemble Methods: Combining multiple open-source LLMs and leveraging ensemble methods can help mitigate individual model biases and improve overall performance in utility judgments.
Transfer Learning: Leveraging pre-trained models and transferring knowledge from models trained on similar tasks can help boost the utility judgment performance of open-source LLMs.
Regularization Techniques: Implementing regularization techniques like dropout or weight decay can prevent overfitting and improve the generalization ability of the models.

What are the potential biases or limitations in the construction of the benchmark datasets, and how can they be addressed?

Some potential biases or limitations in the construction of benchmark datasets for utility judgments include:

Imbalanced Data: The datasets may have an imbalance in the distribution of different types of passages, leading to biased model performance. This can be addressed by carefully balancing the dataset during construction.
Synthetic Data: The inclusion of synthetic data like counterfactual passages may introduce biases if not generated accurately. Ensuring the quality and relevance of synthetic data can help mitigate this bias.
Ground-Truth Uncertainty: The uncertainty regarding the presence of ground-truth evidence in the candidate passages can introduce variability in model performance. Providing clearer guidelines or annotations can address this limitation.
Limited Dataset Size: A small dataset size may limit the model's ability to learn diverse patterns and generalize well. Increasing the dataset size or applying data augmentation techniques can help overcome this limitation.
Human Annotation Bias: Human annotators may introduce biases in labeling passages. Implementing multiple annotators and inter-annotator agreement checks can reduce annotation bias.

How can the proposed k-sampling listwise approach be extended or combined with other techniques to reduce the dependency of LLMs on the position of ground-truth evidence?

The k-sampling listwise approach can be extended and combined with other techniques to further reduce the dependency of LLMs on the position of ground-truth evidence:

Dynamic k-Value: Instead of a fixed k-value, dynamically adjusting the sampling size based on the complexity of the question or the characteristics of the passages can enhance the effectiveness of the approach.
Multi-Stage Sampling: Implementing a multi-stage sampling process where the k-sampling is performed iteratively with different k-values can provide a more comprehensive view of the passages' utility.
Attention Mechanisms: Incorporating attention mechanisms to give more weight to passages that are sampled multiple times can help the model focus on passages that are consistently identified as useful.
Active Learning: Integrating active learning strategies to adaptively select passages for sampling based on the model's uncertainty can improve the efficiency of the k-sampling approach.
Model Fusion: Combining the k-sampling listwise approach with ensemble methods or model fusion techniques can leverage the strengths of multiple models to reduce the dependency on the position of ground-truth evidence and enhance overall performance.