FactCheckmate: Preemptively Detecting and Mitigating Hallucinations in LMs (2410.02899v2)

Abstract: LLMs (LMs) hallucinate. We inquire: Can we detect and mitigate hallucinations before they happen? This work answers this research question in the positive, by showing that the internal representations of LMs provide rich signals that can be used for this purpose. We introduce FactCheckmate, which preemptively detects hallucinations by learning a classifier that predicts whether the LM will hallucinate, based on the model's hidden states produced over the inputs, before decoding begins. If a hallucination is detected, FactCheckmate then intervenes by adjusting the LM's hidden states such that the model will produce more factual outputs. FactCheckmate provides fresh insights that the inner workings of LMs can be revealed by their hidden states. Practically, both its detection and mitigation models are lightweight, adding little inference overhead; FactCheckmate proves a more efficient approach for mitigating hallucinations compared to many post-hoc alternatives. We evaluate FactCheckmate over LMs of different scales and model families (including Llama, Mistral, Qwen and Gemma), across a variety of QA datasets from different domains. Our results demonstrate the effectiveness of FactCheckmate, achieving over 70% preemptive detection accuracy. On average, outputs generated by LMs with intervention are 34.4% more factual compared to those without.

• The paper introduces FactCheckmate, a framework that preemptively detects potential LM hallucinations with over 70% accuracy using a classifier on hidden states and mitigates them by adjusting these states.
• When potential hallucination is detected, FactCheckmate employs an intervention model to adjust hidden states, resulting in outputs that are 34.4% more factual with minimal inference overhead.
• Evaluated across diverse datasets and models (Llama, Mistral, Gemma), FactCheckmate consistently demonstrates superior performance in improving factual accuracy compared to baseline and other methods.

Overview of "Preemptively Detecting and Mitigating Hallucinations in LMs"

The paper entitled "Preemptively Detecting and Mitigating Hallucinations in LMs" presents an innovative methodology for addressing a significant challenge in the domain of LLMs (LMs): the phenomenon of hallucination. Hallucination refers to the propensity of LMs to generate outputs that are factually incorrect yet appear plausible. Traditional approaches to mitigating this issue often focus on post-processing methods, which are both computationally intensive and reactive. This paper proposes a novel alternative by employing the internal representations of LMs as a means of preemptive detection and intervention.

Methodology and Technical Contribution

The authors introduce a framework, denoted as \name, designed to preemptively detect and mitigate hallucinations. The process involves a two-stage mechanism:

1. Detection: The core premise is that the internal hidden states of an LM contain signals that can forewarn potential hallucinations before they manifest in the output. The authors develop a lightweight binary classifier that operates on the LM's hidden states, generated over the input prior to initiating the decoding process. This classifier predicts the likelihood of the LM hallucinating. Impressively, this preemptive detection mechanism achieved over 70% accuracy, significantly outperforming a random baseline of 50%.
2. Mitigation: Upon detecting a high probability of hallucination, \name intervenes by adjusting the LM's hidden states. The intervention is facilitated by an intervention model, which modifies the final hidden state of the LM input to steer the generation towards factual outputs. This adjustment leads to outputs that are, on average, 34.4% more factual compared to the baseline outputs without intervention. The design of the intervention model, being lightweight, ensures minimal impact on inference time, with an overhead of approximately 3.16 seconds.

Experimental Evaluation

The efficacy of \name was evaluated across multiple benchmarks using diverse datasets, including NQ-open, MMLU, and MedMCQA, spanning domains like general knowledge, STEM, and medical exams. Moreover, the framework was tested on various LMs encompassing Llama, Mistral, and Gemma models of different scales. The results consistently demonstrated the efficacy of preemptive intervention in improving factual accuracy. Additionally, the intervention model was compared to existing techniques, such as PCA-based approaches and sample-based decoding strategies, with \name showing superior performance in terms of win rates in output factuality.

Implications and Future Work

The findings signify a pivotal shift in hallucination mitigation strategies, highlighting the potential of leveraging internal LM states for enhanced supervision of outputs. The implications are profound: a more efficient prediction and mitigation system improve the reliability of LMs in critical applications like question answering in varied domains.

Further research may explore broader generalization across tasks and datasets, potentially incorporating other internal mechanisms within the LMs. There is also scope for developing a universally applicable model that can adapt across different LM architectures and specificities.

In summary, the paper presents substantial advances in the proactive handling of hallucinations in LMs, contributing vital insights into the architectures' operational workings and setting a foundation for more reliable language generation systems.