This paper introduces a novel method for inferring the "phylogenetic" relationships between large language models (LLMs), treating their development like the evolution of species. By analyzing the outputs of various LLMs on a standardized set of tasks, the researchers construct a distance matrix reflecting the similarity of their behaviors. This matrix then informs the creation of a phylogenetic tree, visually representing the inferred evolutionary relationships. The resulting tree reveals clusters of models based on their architectural similarities and training data, providing insights into the influence of these factors on LLM behavior. This approach offers a new perspective on understanding the development and diversification of LLMs, moving beyond simple performance comparisons to explore the deeper connections between them.
The preprint "Inferring the Phylogeny of Large Language Models" by Mitchell et al. explores the relationships between different Large Language Models (LLMs) by applying phylogenetic methods traditionally used in evolutionary biology to trace the lineage of species. Instead of analyzing genetic data, the researchers leverage the outputs of these LLMs on a standardized set of tasks. They argue that the similarities and differences in how these models respond to prompts can be treated analogously to shared derived characteristics in biological organisms, thus allowing for the construction of a "family tree" of LLMs.
The authors curate a dataset encompassing a diverse range of LLMs, spanning various architectures, training datasets, and sizes. This collection includes both publicly available models and those accessible only through APIs. They then subject these models to a carefully chosen battery of "behavioral tasks." These tasks are designed to probe the models' capabilities across multiple dimensions, including question answering, logical reasoning, translation, and code generation. The specific choice of tasks aims to elicit responses that are sensitive to the underlying architecture and training of the model, effectively serving as a proxy for their "genetic makeup."
The core methodology of the paper involves converting the LLMs' responses into numerical representations suitable for phylogenetic analysis. This involves quantifying the similarity between the outputs of different models on each task. They employ several different distance metrics to capture these similarities, allowing for robustness in their analysis and accounting for potential biases introduced by any single metric. These distance matrices are then fed into standard phylogenetic reconstruction algorithms, borrowing techniques from the field of cladistics. These algorithms attempt to infer the most likely evolutionary relationships between the models based on the observed differences in their "behavior," represented by the distance matrices.
The resulting phylogenetic trees offer a visual representation of the hypothesized evolutionary relationships between the LLMs. The authors analyze these trees, exploring the clustering patterns and branching structures to identify potential correlations with known model characteristics, such as training data, architecture, and size. They investigate whether models trained on similar datasets tend to cluster together, and whether architectural differences are reflected in the branching patterns. Furthermore, they examine the placement of closed-source models within the tree, attempting to glean insights into their potential underlying architecture and training methodologies based on their proximity to open-source counterparts.
The paper concludes by discussing the implications of this phylogenetic approach for understanding the development and evolution of LLMs. The authors posit that this methodology can provide valuable insights into the influence of different design choices on model behavior, facilitate the identification of common ancestors and lineages, and potentially even predict the performance of future models based on their position within the phylogenetic tree. They also acknowledge the limitations of this initial exploration and suggest future research directions, including expanding the dataset of LLMs, refining the behavioral tasks, and exploring alternative phylogenetic methods. Ultimately, the authors propose that this "phylogenetic lens" offers a novel and promising framework for analyzing the increasingly complex landscape of large language models.
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https://news.ycombinator.com/item?id=43736366
Several Hacker News commenters express skepticism about the paper's methodology and conclusions. Some doubt the reliability of using log-likelihoods on cherry-picked datasets to infer relationships, suggesting it's more a measure of dataset similarity than true model ancestry. Others question the assumption that LLMs even have a meaningful "phylogeny" like biological organisms, given their development process. The idea of "model paleontology" is met with both interest and doubt, with some arguing that internal model parameters would offer more robust insights than behavioral comparisons. There's also discussion on the limitations of relying solely on public data and the potential biases introduced by fine-tuning. A few commenters raise ethical concerns around potential misuse of such analysis for IP infringement claims, highlighting the difference between code lineage and learned knowledge.
The Hacker News post titled "Inferring the Phylogeny of Large Language Models" discussing the arXiv preprint at https://arxiv.org/abs/2404.04671 generated a moderate amount of discussion with several interesting points raised.
One commenter expressed skepticism regarding the core premise of the paper, questioning whether treating LLMs as evolving entities within a phylogenetic framework is appropriate. They argued that LLMs are artifacts designed and built by humans, not organisms subject to natural selection, and therefore the analogy doesn't hold. They also pointed out that the "mutations" introduced in LLMs are deliberate design choices or errors, not random variations, which further undermines the comparison to biological evolution.
Another commenter elaborated on this point by suggesting that the observed similarities between LLMs are more likely due to convergent engineering, where different teams arrive at similar solutions to common problems, rather than evolutionary descent. They proposed that the shared characteristics of LLMs are a reflection of the shared goals and constraints faced by their developers.
A different line of discussion focused on the practical implications of the research. One commenter questioned the usefulness of building a phylogeny of LLMs, arguing that the relevant information about their architecture and training data is already known and accessible. They suggested that focusing on these known factors would be more productive than constructing an evolutionary tree.
However, a counterpoint was raised that understanding the relationships between LLMs in a phylogenetic context could be valuable for tasks like identifying the origins of specific behaviors or biases. This commenter argued that tracing the lineage of an LLM could help pinpoint the source of undesirable characteristics, potentially aiding in their mitigation.
One commenter expressed interest in the potential for using phylogenetic methods to analyze the evolution of codebases in general, seeing this as a broader application of the principles explored in the paper.
Finally, some commenters discussed the technical details of the paper, such as the specific methods used for constructing the phylogenetic tree and the limitations of the approach. One pointed out the challenge of defining meaningful "traits" for LLMs, given their complexity.
In summary, the comments on the Hacker News post presented a range of perspectives on the paper, from skepticism about the underlying framework to enthusiasm for its potential applications. The discussion touched upon the appropriateness of the evolutionary analogy, the practical implications of the research, and the technical challenges involved in analyzing LLMs in a phylogenetic context.