Giant language models have demonstrated impressive capabilities, such as crafting poetry or developing functional laptop software, yet they also possess the ability to predict forthcoming phrases within a given text stream.
These seemingly mundane styles and trends might actually be unwittingly exploring fundamental aspects of our reality.
Despite appearances suggesting otherwise, recent findings contradict this notion. Researchers found that a type of AI can provide turn-by-turn driving directions in New York City with near-perfect accuracy without needing to create an accurate internal map of the city.
Despite its impressive ability to navigate with ease, the mannequin’s performance suffered significantly when the researchers blocked certain roads and introduced detours.
As the researchers delved further, they found that the artificial maps created by the implicit generation process in the New York system featured numerous non-existent roads, twisting between the traditional grid patterns to link previously disparate junctions.
The potential consequences of these limitations could be far-reaching, as generative AI models deployed in real-world settings may exhibit critical failures when faced with novel tasks or environments, despite initially seeming robust and effective.
One potential implication is that the capabilities of LLMs to excel in various linguistic tasks may lead to their application in other areas of scientific inquiry as well. Whether LLMs are studying coherent world patterns is crucial in leveraging these methods to drive new discoveries, notes Ashesh Rambachan, senior creator and assistant professor of economics at MIT, principal investigator in the Laboratory for Information and Decision Systems.
Researchers Rambachan and Vafa, along with colleague Dr. Justin Y., Chen, a graduate student in Electrical Engineering and Computer Science (EECS) at MIT, Jon Kleinberg, Tisch Professor of Computer Science and Information Science at Cornell University, and Sendhil Mullainathan, an MIT professor in both the departments of EECS and Economics, as well as a member of LIDS. The upcoming analysis is scheduled to focus on the key takeaways from the Conference on Neural Information Processing Technologies.
The researchers focused on a type of generative AI model called a transformer, which forms the backbone of large language models (LLMs) such as GPT-4. Transformers excel at processing vast amounts of language-based data to predict the next token in a sequence, such as the next word in a sentence.
While evaluating the predictive accuracy of a Large Language Model (LLM) may be a crucial step in determining its success, scientists caution that it is not enough to determine whether the model has truly captured an accurate representation of reality?
Indeed, researchers found that a transformer can accurately predict legitimate moves in a game of Join 4 nearly every time without being privy to the rules?
The workforce has developed two novel metrics to assess a transformer’s world model. Researchers focused their assessments on deterministic finite automata, commonly referred to as DFAs.
A deterministic finite automaton (DFA) represents a sequence of states akin to mapping out a journey’s milestones, where each state serves as an interim destination, governed by a set of rules that ensure the desired outcome is reached.
Two problem domains were chosen to be formalized as deterministic finite automata (DFAs): navigating city streets in New York City and playing the board game Othello.
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They introduced Sequence Distinction, a key metric that determines whether a model has created a cohesive world model by recognizing the difference between distinct states, such as two unique Othello board configurations, and identifying their disparities. Transformers utilize sequences, which are organized lists of knowledge components, to produce outputs.
The second metric, termed sequence compression, posits that a well-designed transformer should recognize that identical sequences of possible next moves exist between equivalent states, analogous to comparing two identical Othello board configurations.
Using these metrics, they assessed two primary transformer lessons: one proficient in generating data from random sequence outputs, and another adept at processing information derived from specific methodologies.
Surprisingly, researchers found that transformers selecting at random produced more accurate world models, possibly due to their ability to explore a broader range of potential next moves during training.
“In Othello, the game’s strategic complexity is often underestimated, with even novice players able to visualize a wide range of potential moves, including those that even skilled champions might avoid.”
Despite generating accurate Othello moves and authentic game logic almost consistently, only one transformer successfully created a coherent board representation, while none demonstrated proficiency in constructing logical maps for navigation.
Researchers illustrated the consequences by incorporating deliberate misdirections into a map of New York City’s metropolitan area, thereby causing all navigation patterns to malfunction.
As I previously found myself astonished by the rapid decline in efficiency whenever we introduced an additional route. According to Vafa, a mere 1% reduction in accessible routes causes an instantaneous and drastic drop in accuracy, plummeting from nearly 100% to just 67%.
As they deciphered the town maps, the fashioners envisioned a sprawling metropolis akin to New York City, with countless streets intersecting and overlaying a grid-like infrastructure. Maps frequently featured arbitrary aerial views hovering over disparate street routes or clusters of streets exhibiting inexplicable alignments.
These findings suggest that transformers can excel in certain tasks without comprehending the underlying mechanisms. Scientists building Large Language Models that accurately capture current cultural trends must adopt a unique approach, experts argue.
Typically, we observe that these fleeting fashion trends often create a stir, and subsequently, people wonder if they should have anticipated something about the world. “Persuading people to adopt a rigorous approach when questioning the unknown requires us to abandon reliance on personal intuition, according to Rambachan.”
Eventually, researchers must confront a diverse array of challenges, including those where certain principles remain only partially understood. Additionally, students are required to apply their analytical skills and metrics to practical, real-world problems in the scientific community.
The research is supported in part by the Harvard Information Science Initiative, a National Science Foundation Graduate Research Fellowship, a Vannevar Bush Fellowship, a Simons Collaboration grant, and funding from the MacArthur Foundation.
