Rethinking Interpretability in the Era of Large Language Models
(2402.01761)Abstract
Interpretable machine learning has exploded as an area of interest over the last decade, sparked by the rise of increasingly large datasets and deep neural networks. Simultaneously, LLMs have demonstrated remarkable capabilities across a wide array of tasks, offering a chance to rethink opportunities in interpretable machine learning. Notably, the capability to explain in natural language allows LLMs to expand the scale and complexity of patterns that can be given to a human. However, these new capabilities raise new challenges, such as hallucinated explanations and immense computational costs. In this position paper, we start by reviewing existing methods to evaluate the emerging field of LLM interpretation (both interpreting LLMs and using LLMs for explanation). We contend that, despite their limitations, LLMs hold the opportunity to redefine interpretability with a more ambitious scope across many applications, including in auditing LLMs themselves. We highlight two emerging research priorities for LLM interpretation: using LLMs to directly analyze new datasets and to generate interactive explanations.
Overview
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The paper discusses the importance of interpretability in AI and the role of LLMs in enhancing the understanding of machine learning systems.
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It points out the limitations of traditional interpretation methods with respect to LLMs and describes how LLMs can directly provide natural language explanations.
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The work highlights the challenges in ensuring the accuracy of explanations from LLMs, as well as the necessity for scalable interpretability solutions capable of handling their size and complexity.
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It suggests future research priorities, emphasizing the need for reliable, user-specific, and interactive explanations to aid human comprehension and trustworthy AI.
Introduction to Interpretability and LLMs
Interpretable machine learning has become an integral part of developing effective and trustworthy AI systems. With the emergence of LLMs, there is now an unprecedented opportunity to reshape the realm of interpretability. LLMs, with their expansive datasets and neural networks, outperform traditional methods in complex tasks and provide natural language explanations that can communicate intricate data patterns to users. However, these advancements come with their own set of concerns such as the generation of incorrect or baseless explanations (hallucination) and substantial computational costs.
Rethinking Interpretation Methods
The paper under consideration expounds on the dual role of LLMs—both as objects of interpretability and as tools for generating explanations of other systems. Traditional techniques offer insights into predictions made by models, like feature importance, but they present limitations, especially when evaluating complex (and often opaque) LLM behaviors. Crucially, the approach of soliciting direct natural language explanations from LLMs opens the door to user-friendly interpretations without complex technical jargon. Nevertheless, to leverage this capability, one must confront new issues like ensuring the validity of LLM explanations and managing the prohibitive size of state-of-the-art models.
Challenges and New Research Avenues
The authors underscore the need to develop effective solutions to combat hallucinated explanations, which can mislead users and erode trust in AI systems. Moreover, they emphasize the importance of creating accessible and efficient interpretability methods for LLMs that have grown beyond the capacity for conventional analysis techniques. The paper neatly categorizes research into explaining a single output from an LLM (local explanation) and understanding the LLM as a whole (global or mechanistic explanation). Notably, cutting-edge LLMs can integrate explanation directly within the generation process, yielding more faithful and accurate reasoning through techniques such as chain-of-thought prompting. Another focal area is dataset explanation, where LLMs help analyze and elucidate patterns within datasets, potentially transforming areas like scientific discovery and data analysis.
Future Priorities and Conclusion
The paper concludes by spotlighting matters vital to advancing interpretability research. These include bolstering explanation reliability, fostering dataset explanation for genuine knowledge discovery, and developing interactive explanations that align with specific user needs. The future trajectory of LLMs in interpretability hinges on addressing these challenges; strategic emphasis in these areas could accelerate the progression towards reliable, user-oriented explanations. As the complexity of available information grows, so too does the significance of LLMs in translating this complexity into comprehensible insights, promising a new chapter in the synergy between AI and human understanding.
Overall, this paper illustrates not merely incremental improvements but a paradigm shift in how we conceptualize and leverage interpretability in the age of LLMs, with vast implications for the broader AI industry and numerous high-stakes domains.
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