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Improving Faithfulness in Knowledge-to-Text Generation through Hypothesis Verification

Core Concepts
Hallucinations in knowledge-to-text generation can be mitigated by incorporating hypothesis verification during decoding, which ranks generation candidates based on how well their hypotheses are supported by the input facts.
The paper addresses the problem of hallucinations in knowledge-to-text (K2T) generation, where models tend to produce outputs that contradict or are not supported by the input facts. The authors propose a model-agnostic decoding method called TWEAK, which incorporates hypothesis verification to improve the faithfulness of the generated text. Key highlights: TWEAK treats the generated sequence at each decoding step and its possible future sequence as hypotheses, and ranks each generation candidate based on the extent to which their hypotheses are supported by the input facts using a Hypothesis Verification Model (HVM). The authors first experiment with using a Natural Language Inference (NLI) model as the HVM, and observe improved faithfulness with a minimal impact on the quality. They then propose a task-specific HVM trained on a novel dataset called FATE, which pairs input facts with their original and perturbed descriptions. This TWEAK-HVM variant further improves faithfulness while maintaining quality. Experiments on in-distribution and out-of-distribution datasets show that the best TWEAK variants improve faithfulness by 2.24/7.17 points on average for two base models, with only a 0.14/0.32-point decline in quality. The authors also analyze the impact of dynamic aggregation of backward and forward hypotheses, and where hallucinations are typically detected during the decoding process.
The K2T task involves generating a natural language description y for a list of input facts x. Autoregressive language models estimate the probability of the token sequence y given the input facts x. Common decoding strategies like greedy and beam search rank candidates solely based on the predicted likelihood, without considering faithfulness.
"Knowledge-to-text generators often struggle to faithfully generate descriptions for the input facts: they may produce hallucinations that contradict the input, or describe facts not present in the input." "TWEAK mitigates this problem by verifying the faithfulness of the candidates at each decoding step to reduce hallucinations."

Key Insights Distilled From

by Yifu Qiu,Var... at 04-04-2024
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Deeper Inquiries

How can the task-specific HVM be further improved to generalize better to out-of-distribution settings?

To enhance the generalization of the task-specific Hypothesis Verification Model (HVM) to out-of-distribution (OOD) settings, several strategies can be considered: Data Augmentation: Increasing the diversity of the training data by incorporating more varied examples from different domains can help the HVM learn to identify hallucinations across a wider range of contexts. Transfer Learning: Pre-training the HVM on a larger and more diverse dataset before fine-tuning on the specific K2T task can improve its ability to generalize to OOD settings. Adversarial Training: Introducing adversarial examples during training can help the HVM become more robust to variations and anomalies in the input data, improving its performance in OOD scenarios. Ensemble Methods: Combining multiple HVMs trained on different datasets or with different architectures can help capture a broader range of patterns and improve generalization.

How can the additional computational cost of TWEAK be reduced, for example through knowledge distillation or other techniques, while maintaining its benefits?

Reducing the computational cost of TWEAK while preserving its benefits can be achieved through various techniques: Knowledge Distillation: Training a smaller, more efficient model to mimic the behavior of the full TWEAK model can help reduce computational overhead while retaining performance. Pruning: Identifying and removing redundant or less important parameters in the TWEAK model can lead to a more compact and efficient model without sacrificing accuracy. Quantization: Converting the model's weights from floating-point to lower precision formats can reduce memory and computational requirements, making inference faster and more resource-efficient. Hardware Acceleration: Utilizing specialized hardware like GPUs or TPUs can speed up the inference process and reduce computational costs. Batch Processing: Optimizing the batch size and parallelizing computations can improve efficiency and reduce the overall computational burden of TWEAK.

What other decoding strategies, beyond hypothesis verification, could be explored to balance faithfulness and quality in K2T generation?

In addition to hypothesis verification, several other decoding strategies can be explored to balance faithfulness and quality in Knowledge-to-Text (K2T) generation: Reinforcement Learning: Incorporating reinforcement learning techniques to reward faithful outputs and penalize hallucinations during the decoding process can improve overall performance. Multi-Objective Optimization: Formulating the decoding task as a multi-objective optimization problem, considering both faithfulness and quality metrics, can lead to a more balanced output. Post-Editing Mechanisms: Implementing a post-editing step where generated text is reviewed and corrected by a human or another model can enhance faithfulness without compromising quality. Structured Prediction: Leveraging structured prediction techniques to ensure that the generated text adheres to the input facts and maintains coherence throughout the output. Adaptive Beam Search: Modifying the beam search algorithm to dynamically adjust the beam size or exploration strategy based on the faithfulness and quality scores of candidate outputs can lead to better results.