NILE: Internal Consistency Alignment in Large Language Models

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TL;DR

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Key Takeaways

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Why does it matter?

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This paper is crucial because it addresses a critical challenge in large language model (LLM) training: the inconsistency between pre-trained LLM knowledge and knowledge in instruction-tuning datasets. The proposed NILE framework offers a novel solution to improve dataset quality which directly impacts LLM performance. It opens up new avenues for research on high-quality data generation and LLM alignment.

Visual Insights

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🔼 This figure illustrates the difference between an LLM’s internal knowledge (knowledge learned during pre-training) and the world knowledge present in instruction fine-tuning (IFT) datasets. It shows how an instruction, processed by the pretrained LLM, elicits an answer that incorporates both internal knowledge (e.g., implicit understanding of emotions) and world knowledge (e.g., explicit facts about project outcomes). The NILE framework aims to improve alignment between these two knowledge sources in the IFT dataset, leading to better LLM performance.

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Figure 1: Demonstration of LLM internal knowledge and world knowledge from IFT datasets.

🔼 This table describes the prompt used to generate internal knowledge demonstrations from a pre-trained large language model (LLM). The prompt takes as input an instruction (instruction^d_i) and its corresponding input (input^d_i) from the instruction fine-tuning (IFT) dataset. The LLM then generates knowledge (ik^d_i) related to the given instruction, which is used in the next step of the NILE framework to improve the consistency between the LLM’s internal knowledge and the world knowledge expressed in the IFT dataset.

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Table 1: Prompt for generating internal knowledge demonstration i⁢kid𝑖subscriptsuperscript𝑘𝑑𝑖ik^{d}_{i}italic_i italic_k start_POSTSUPERSCRIPT italic_d end_POSTSUPERSCRIPT start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT related to qidsubscriptsuperscript𝑞𝑑𝑖q^{d}_{i}italic_q start_POSTSUPERSCRIPT italic_d end_POSTSUPERSCRIPT start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT.

In-depth insights

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LLM Internal Knowledge

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LLM internal knowledge represents the implicit understanding encoded within a large language model’s (LLM) parameter weights, learned during pre-training on massive datasets. This knowledge isn’t explicitly stored as facts but manifests in the model’s ability to generate coherent and contextually relevant text. It’s crucial to understand that this internal knowledge can be inconsistent with the world knowledge presented in fine-tuning datasets, leading to misalignment and suboptimal performance. Aligning the world knowledge of fine-tuning datasets with the LLM’s internal knowledge is, therefore, a critical challenge and an active area of research. Effective alignment techniques aim to bridge this gap, optimizing dataset quality and leading to improved LLM performance, as demonstrated in the provided research paper. The paper’s focus on improving the consistency between these knowledge sources highlights the importance of understanding the nuances of LLM internal knowledge for enhancing overall LLM capabilities. Further research should continue to explore methods for effectively assessing, understanding, and aligning LLM internal knowledge with external information to unlock the full potential of LLMs.

NILE Framework

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The NILE framework, designed for internal consistency alignment in large language models (LLMs), addresses the critical issue of knowledge discrepancies between pre-trained LLMs and instruction-tuning datasets. It tackles this by first extracting the LLM’s internal knowledge related to given instructions. Then, it leverages this knowledge to revise existing answers in the dataset, ensuring better alignment. A crucial component is the Internal Consistency Filtering (ICF) method, which filters out low-quality revisions, ultimately improving the quality and consistency of the instruction-tuning data. The framework’s effectiveness is demonstrated through significant performance gains across various evaluation benchmarks, highlighting the importance of addressing data-model consistency in enhancing LLM capabilities. NILE’s innovative approach offers a significant contribution to the field of LLM alignment, showcasing the considerable impact of dataset optimization on improving downstream model performance.

Data Consistency

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Data consistency in large language models (LLMs) is crucial for effective instruction fine-tuning (IFT). Inconsistent knowledge between an LLM’s internal knowledge (from pre-training) and the external knowledge presented in IFT datasets significantly impacts performance. A core issue is the discrepancy between world knowledge in training data and the LLM’s internal representations. This inconsistency can lead to models that are unable to generalize well, and instead rely on surface-level patterns rather than true understanding. Addressing this requires careful curation of IFT datasets to ensure alignment with the LLM’s internal knowledge, potentially involving methods to filter or revise data to enhance consistency. Strategies for improving data consistency might include using the LLM itself to identify and correct inconsistencies, or employing techniques to assess and prioritize data samples based on their alignment with the pre-trained model’s knowledge base. Successfully achieving data consistency is key to unlocking the full potential of LLMs in various downstream applications.

Future Research

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Future research directions stemming from this paper could explore iterative instruction refinement techniques within the NILE framework, moving beyond the single-pass revision currently implemented. Investigating the impact of using the complete OpenOrca dataset instead of the subset would provide a more comprehensive evaluation and potentially reveal new insights. A comparative analysis of NILE’s performance against alternative data selection and revision methods would strengthen its positioning within the field. Further studies are warranted to fully understand how NILE interacts with LLMs possessing different internal knowledge structures, thereby enhancing the generalizability of this methodology. Finally, exploring the ethical implications of using automatically generated datasets at scale for fine-tuning LLMs, focusing on biases, discrimination, and appropriate safeguards, is vital.

NILE Limitations

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The NILE framework, while demonstrating significant improvements in LLM performance, has limitations. Data limitations are key; the study used only a subset of the OpenOrca dataset due to computational constraints. Future work should leverage the entire dataset to further evaluate NILE’s capabilities. Another important limitation is the single-pass revision. NILE currently employs only one revision cycle. Implementing iterative refinement would likely lead to further improvements and a more robust alignment process. Finally, computational resource requirements present a hurdle for wider application. The framework’s computational intensity should be addressed, possibly through optimization techniques, to broaden its accessibility and practicality for a wider range of users and models. Addressing these limitations is crucial to unleashing NILE’s full potential for enhanced LLM performance.

More visual insights

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More on figures

🔼 The NILE framework is depicted in this flowchart, which is broken down into three primary phases: Internal Knowledge Extraction (IKE), Knowledge-Aware Sample Revision (KSR), and Internal Consistency Filtering (ICF). The framework starts with original instruction-response pairs from an instruction tuning dataset. IKE uses a pre-trained LLM to extract internal knowledge associated with the instruction. KSR leverages this knowledge to revise the original answer. Finally, ICF filters revised samples based on their internal consistency with the pre-trained LLM. The resulting aligned dataset is then used for instruction fine-tuning.

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Figure 2: Overview of our NILE framework.

🔼 This figure displays the distribution of sentence embedding similarity scores calculated for the Mistral language model using the Alpaca dataset. The distribution shows the similarity between sentence embeddings generated by the original answers in the dataset, answers revised using the Knowledge-aware Sample Revision (KSR) method, and answers revised using the Sample Revision (SR) method. This visual representation helps to evaluate the effectiveness of the KSR method in enhancing the affinity between the model’s internal knowledge and the information provided in the dataset. Specifically, higher similarity scores indicate a stronger alignment between the revised answers and the internal knowledge of the model.

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Figure 3: Distribution plot of sentence embedding similarity score in Alpaca dataset for Mistral model.

🔼 This figure displays the distribution of sentence embedding similarity scores obtained from three different methods: Vanilla (original responses), KSR (responses revised using knowledge-aware sample revision), and SR (responses revised without using internal LLM knowledge). The distributions are shown separately for Alpaca and Orca datasets and for two different LLMs, Mistral and Llama. The plots visually compare how similar the generated responses are to the internal knowledge of the LLMs, illustrating the impact of the KSR technique on aligning generated text with pre-trained knowledge.

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Figure 4: Distribution of sentence embedding similarity across different LLMs and IFT datasets.

Full paper

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