Distilling the Knowledge of Large-scale Generative Models into Retrieval Models for Efficient Open-domain Conversation

Kim, Beomsu; Seo, Seokjun; Han, Seungju; Erdenee, Enkhbayar; Chang, Buru

doi:10.18653/v1/2021.findings-emnlp.286

Cited by 4 publications

(4 citation statements)

References 32 publications

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“…Exemplar-based generative models (Wu et al, 2019;Cai et al, 2019b;Gupta et al, 2021) for open-domain conversation combine a retrieval model (Humeau et al, 2019;Mazare et al, 2018;Kim et al, 2021) and a generative model (Adiwardana et al, 2020;Roller et al, 2021; † Equal contribution * Corresponding author…”

Section: Introductionmentioning

confidence: 99%

Understanding and Improving the Exemplar-based Generation for Open-domain Conversation

Han¹,

Kim²,

Seo³

et al. 2022

Proceedings of the 4th Workshop on NLP for Conversational AI

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Exemplar-based generative models for opendomain conversation produce responses based on the exemplars provided by the retriever, taking advantage of generative models and retrieval models. However, due to the oneto-many problem of the open-domain conversation, they often ignore the retrieved exemplars while generating responses or produce responses over-fitted to the retrieved exemplars. To address these advantages, we introduce a training method selecting exemplars that are semantically relevant to the gold response but lexically distanced from the gold response. In the training phase, our training method first uses the gold response instead of dialogue context as a query to select exemplars that are semantically relevant to the gold response. And then, it eliminates the exemplars that lexically resemble the gold responses to alleviate the dependency of the generative models on that exemplars. The remaining exemplars could be irrelevant to the given context since they are searched depending on the gold response. Thus, our training method further utilizes the relevance scores between the given context and the exemplars to penalize the irrelevant exemplars. Extensive experiments demonstrate that our proposed training method alleviates the drawbacks of the existing exemplar-based generative models and significantly improves the performance in terms of appropriateness and informativeness.

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Section: Introductionmentioning

confidence: 99%

Understanding and Improving the Exemplar-based Generation for Open-domain Conversation

Han¹,

Kim²,

Seo³

et al. 2022

Proceedings of the 4th Workshop on NLP for Conversational AI

Self Cite

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show abstract

“…Enhanced strategies were developed to detect and adjust to anomalies in task performance, ensuring a more stable learning curve and reducing the likelihood of overfitting on specific types of tasks [13][14][15]. Techniques to systematically evaluate the efficacy of adaptive learning algorithms were also explored, revealing that continuous adaptation leads to better long-term performance retention [16,17]. Strategies that incorporate elements of predictive modeling to anticipate future learning needs based on past performance data were also highlighted, showcasing a proactive approach to fine-tuning [1].…”

Section: Adaptive Learning Strategies For Dynamic Benchmarkingmentioning

confidence: 99%

Fine-tuning Llama For Better Performance With the MMLU Benchmark

Yim,

Yip

2024

Preprint

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Enhancements in the performance of Llama 2 on the Massive Multitask Language Understanding (MMLU) benchmark reflect a significant leap forward in language model development. The application of sophisticated fine-tuning techniques, including adaptive learning strategies and advanced data preprocessing, has resulted in notable increases in accuracy and adaptability across diverse domains. These results not only underscore the model's improved proficiency in handling complex language tasks but also enhance its potential for deployment in various practical applications requiring precise and reliable AI-driven communication. Additionally, the study highlights the effectiveness of employing differential learning rates and regularization techniques to refine model behavior, thereby reducing overfitting and improving the model’s generalization capabilities across tasks. Statistical analyses affirm that the observed improvements are significant, with p-values well below the conventional thresholds, thereby validating the effectiveness of the fine-tuning processes employed. Furthermore, comparative assessments with baseline models demonstrate Llama 2’s superior performance, confirming its enhanced capability to handle the multifaceted demands of the MMLU benchmark. The research also delves into the implications of these improvements for future AI applications, suggesting that such enhanced models could play pivotal roles in sectors like healthcare, education, and customer service. Moving forward, the study identifies opportunities for further research, particularly in expanding the training datasets to include more diverse linguistic inputs and reducing the computational demands of model training. The progress documented here provides a promising direction for future endeavors aiming to harness advanced AI capabilities in real-world scenarios, advocating for a continuous evolution towards more efficient, ethical, and universally applicable language models.

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“…The integration of scoring functions that incorporate ethical considerations has further enhanced the efficacy of best-of-N strategies in value alignment tasks [43,44]. Advanced optimization techniques have been employed to improve the computational efficiency of best-of-N strategies, making them more feasible for large-scale LLM applications [45,46]. The combination of best-of-N strategies with attention mechanisms has led to more contextually aware and relevant outputs, particularly in complex text generation tasks [47,48].…”

Section: Related Studiesmentioning

confidence: 99%

Improved Value Alignment in Large Language Models Using Variational Best-of-N Techniques

Wang,

Li,

Zhang

2024

Preprint

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Large language models have shown high capabilities in generating human-like text and performing complex language-related tasks, yet they face significant challenges regarding value alignment to prevent the generation of harmful or biased content. The novel integration of the Variational Best-of-N technique within the Llama model enhances the ability to generate ethically aligned content by evaluating multiple candidate outputs and selecting the most appropriate one based on predefined ethical criteria. This research involved modifying the core architecture of Llama, introducing additional layers for variational inference, and implementing a sophisticated scoring mechanism to evaluate ethical alignment. Comprehensive preprocessing, balanced training data, and rigorous fine-tuning were employed to optimize the model's performance, resulting in significant improvements in coherence, relevance, and adherence to ethical standards. The modified model was rigorously evaluated using metrics such as perplexity, BLEU score, ROUGE score, and a custom ethicality score, and the results were compared with baseline models like GPT-3 and BERT. Statistical analyses confirmed that the observed improvements were statistically significant. The findings demonstrate the effectiveness of the proposed modifications and their potential to enhance the ethical alignment of language models, thereby contributing to the development of more trustworthy and reliable AI systems. This study sets a precedent for future innovations in the field of ethical AI, ensuring that AI systems serve the broader good of society.

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Distilling the Knowledge of Large-scale Generative Models into Retrieval Models for Efficient Open-domain Conversation

Cited by 4 publications

References 32 publications

Understanding and Improving the Exemplar-based Generation for Open-domain Conversation

Understanding and Improving the Exemplar-based Generation for Open-domain Conversation

Fine-tuning Llama For Better Performance With the MMLU Benchmark

Improved Value Alignment in Large Language Models Using Variational Best-of-N Techniques

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