Synchronous Faithfulness Monitoring for Trustworthy Retrieval-Augmented Generation (2406.13692v2)

Abstract: Retrieval-augmented LLMs (RALMs) have shown strong performance and wide applicability in knowledge-intensive tasks. However, there are significant trustworthiness concerns as RALMs are prone to generating unfaithful outputs, including baseless information or contradictions with the retrieved context. This paper proposes SynCheck, a lightweight monitor that leverages fine-grained decoding dynamics including sequence likelihood, uncertainty quantification, context influence, and semantic alignment to synchronously detect unfaithful sentences. By integrating efficiently measurable and complementary signals, SynCheck enables accurate and immediate feedback and intervention, achieving 0.85 AUROC in detecting faithfulness errors across six long-form retrieval-augmented generation tasks, improving prior best method by 4%. Leveraging SynCheck, we further introduce FOD, a faithfulness-oriented decoding algorithm guided by beam search for long-form retrieval-augmented generation. Empirical results demonstrate that FOD outperforms traditional strategies such as abstention, reranking, or contrastive decoding significantly in terms of faithfulness, achieving over 10% improvement across six datasets.

Synchronous Faithfulness Monitoring for Trustworthy Retrieval-Augmented Generation

The paper "Synchronous Faithfulness Monitoring for Trustworthy Retrieval-Augmented Generation" by Di Wu et al. offers a novel approach to enhancing the reliability of retrieval-augmented LLMs (RALMs) by introducing a lightweight, on-the-fly monitoring system called SynCheck and a new decoding strategy termed Faithfulness-Oriented Decoding (FOD). The paper is set against a backdrop of increasing concern over the trustworthiness of RALMs, which, despite their robust performance on knowledge-intensive tasks, frequently generate unfaithful or contradictory outputs.

Overview of SynCheck

SynCheck is conceptualized as an efficient, synchronous monitoring tool designed to detect unfaithful segments during the decoding stage of an RALM. The monitoring system leverages multiple complementary signals, including:

1. Sequence Likelihood: The likelihood of generated sequences, with both minimum and mean probability metrics.
2. Uncertainty Quantification: Measures of predictive uncertainty such as token-level entropy and local intrinsic dimension (LID) of intermediate layer activations.
3. Context Influence: The influence of the retrieved context is assessed by contrasting token-wise distributions with and without the retrieved context, measuring Kullback-Leibler divergence.
4. Semantic Alignment: The use of entailment checking to ensure semantic consistency between the generated segment and the retrieved context.

These features are integrated using a lightweight aggregator, which combines the various signals to provide real-time, accurate assessments of faithfulness. SynCheck achieves a notable average AUROC of 0.85 on six diverse long-form generation tasks, surpassing prior methods by 4%.

Faithfulness-Oriented Decoding (FOD)

Building on SynCheck, the paper introduces FOD, a novel decoding algorithm that aims to ensure higher faithfulness in long-form retrieval-augmented generation. FOD operates in two stages:

1. Greedy Search and Backtracking: Initially, sentences are generated greedily until an unfaithful sentence is detected, triggering a backtrack to retain only the faithful prefix.
2. Beam Search Guided by Faithfulness: From the backtracked position, a beam search is initiated where the faithfulness of each beam is continuously monitored using SynCheck. Samples with faithfulness below a certain threshold are pruned, ensuring the generation of more faithful outputs.

FOD demonstrates superior performance in maintaining a balance between faithfulness and informativeness, resulting in over 10% improvements across six datasets compared to other strategies like abstention, reranking, or contrastive decoding.

Experimental Validation

The evaluation covers four common retrieval-augmented generation tasks: biography generation, question answering, summarization, and data-to-text generation. The datasets used included RAGTruth for QA, Summ, Data2txt, and a newly curated dataset for biography generation (F-100 and F-100-anti). The evaluation highlights several key findings:

• SynCheck outperforms traditional methods such as CriticTok and FLARE, which were hindered by their focus on heuristic measures or simple instruction-tuning.
• Cross-task analysis reveals that SynCheck trained on one task can generalize well across other tasks, indicating robust feature set effectiveness.
• Cross-model evaluation shows that SynCheck trained on Llama 2 7B Chat generalizes effectively to outputs from other models like Llama 2 13B and Mistral 7B, underscoring its potential as a universal faithfulness monitor.

Implications and Future Developments

The introduction of SynCheck and FOD presents significant implications for the field of RALMs:

1. Practical Implications: The tools offer a pragmatic solution to the prevalent issue of unfaithfulness in RALMs, enhancing their reliability in real-world applications. The ability to monitor and correct outputs in real-time can lead to more transparent and trustworthy AI systems.
2. Theoretical Contributions: The multipronged approach to faithfulness detection through sequence likelihood, uncertainty quantification, context influence, and semantic alignment, provides a comprehensive framework that can be extended and refined in future research.
3. Speculative Future Directions: The paper hints at the potential for SynCheck to be integrated into more complex, multi-source retrieval systems and its adaptability to different granularity levels of generated content. Moreover, future research might explore refining the balance between computational efficiency and the depth of faithfulness monitoring to further optimize performance.

In summary, this paper offers a significant contribution to the ongoing efforts to enhance the trustworthiness of RALMs. SynCheck and FOD collectively address a crucial gap in real-time faithfulness monitoring and intervention, paving the way for more reliable and interpretable AI systems in knowledge-intensive contexts.