toplogo
Sign In

SHROOM-INDElab's Hallucination Detection System for SemEval-2024 Task 6: Zero- and Few-Shot LLM-Based Classification


Core Concepts
The SHROOM-INDElab system uses prompt engineering and in-context learning with large language models (LLMs) to build classifiers for hallucination detection, achieving competitive performance in the SemEval-2024 Task 6 competition.
Abstract
The SHROOM-INDElab team participated in the SemEval-2024 Task 6 competition, which focused on hallucination detection in the outputs of language models. The team developed a two-stage system that leverages prompt engineering and in-context learning with LLMs to classify whether a given model output contains hallucination or not. In the first stage, the system uses a zero-shot approach, where the LLM is prompted with task, role, and concept definitions to classify the data points without any examples. The classified data points from this stage are then used to select a few-shot example set for the second stage. In the second stage, the system uses the selected examples along with the task, role, and concept definitions to prompt the LLM for a few-shot classification. The team experimented with different hyperparameters, such as temperature, number of examples, and number of samples, to optimize the classifier's performance. The SHROOM-INDElab system achieved competitive results, ranking fourth and sixth in the model-agnostic and model-aware tracks of the competition, respectively. The system's classifications were also found to be consistent with the crowd-sourced human labelers, as indicated by the Spearman's correlation coefficient. The team's ablation study revealed that the explicit definition of the hallucination concept was a crucial component of the system's performance, suggesting the importance of including intentional definitions of concepts in prompts for LLM-based classifiers. The team plans to further investigate this approach for evaluating natural language rationale generation in the context of zero- and few-shot chain-of-thought classifiers.
Stats
The Dutch would sometimes <define>inundate</define> the land to hinder the Spanish army. Well, 'Smiley says, easy and careless, 'he's good enough for one thing, I should judge — he can <define>outjump</define> any frog in Calaveras Country.' The writer had just entered into his eighteenth year, when he met at the table of a certain Anglo-Germanist an individual, apparently somewhat under thirty, of middle stature, a thin and <define>weaselly</define> figure, a sallow complexion, a certain obliquity of vision, and a large pair of spectacles.
Quotes
"To cover with large amounts of water; to flood." "(transitive) To jump better than; particularly higher than, or further than." "Resembling or characteristic of a weasel."

Key Insights Distilled From

by Bradley P. A... at arxiv.org 04-08-2024

https://arxiv.org/pdf/2404.03732.pdf
SHROOM-INDElab at SemEval-2024 Task 6

Deeper Inquiries

How can the example selection process be further improved to better capture the diversity of hallucination patterns in the data?

In order to enhance the example selection process for capturing a wider range of hallucination patterns, several strategies can be implemented: Diversified Sampling: Instead of relying solely on entropy-based selection, incorporating techniques like diversity sampling can help ensure a broader representation of hallucination instances. This can involve clustering data points based on similarity and selecting examples from different clusters. Adaptive Sampling: Implementing an adaptive sampling strategy that adjusts the selection criteria based on the distribution of hallucination patterns in the data can lead to a more balanced and comprehensive set of examples. Active Learning: Introducing an active learning component where the system iteratively selects examples based on the uncertainty of the classifier can help target areas of the data that are more challenging or ambiguous in terms of hallucination detection. Semantic Embeddings: Utilizing semantic embeddings to capture the underlying meaning and context of the examples can aid in selecting diverse instances that cover a wide spectrum of hallucination patterns. Human-in-the-Loop: Incorporating human feedback in the example selection process can provide valuable insights into the nuances of hallucination patterns that may not be captured effectively through automated methods alone. By integrating these approaches, the example selection process can be refined to encompass a more diverse and representative set of hallucination patterns, thereby improving the overall performance and robustness of the hallucination detection system.

How can the insights from this work on hallucination detection be applied to improve the transparency and interpretability of large language model outputs in other natural language processing tasks?

The insights gained from the work on hallucination detection can be leveraged to enhance the transparency and interpretability of large language model outputs in various natural language processing tasks through the following methods: Explainable AI Techniques: Implementing explainable AI techniques such as attention mechanisms, saliency maps, and feature visualization can provide insights into the decision-making process of the language model, making the outputs more interpretable. Prompt Design: Designing specific prompts that encourage the model to provide reasoning or explanations for its outputs can improve transparency by forcing the model to justify its predictions. Error Analysis: Conducting thorough error analysis, similar to the hallucination detection process, can help identify common pitfalls or biases in the model's outputs, leading to more transparent and reliable results. Human-AI Collaboration: Facilitating collaboration between human annotators and the AI system can enhance interpretability by incorporating human judgment and domain expertise to validate and explain model outputs. Model Calibration: Implementing calibration techniques to ensure that the model's confidence scores align with the accuracy of its predictions can improve the reliability and transparency of the outputs. By applying these insights, natural language processing tasks can benefit from increased transparency, interpretability, and trustworthiness in the outputs generated by large language models, ultimately enhancing the overall performance and usability of AI systems.
0