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Imagine you’re engrossed in a narrative, or engaged in a game of chess. You might not realize it, but at every moment, your brain monitored how the circumstances (or “condition of the universe”) evolved. You can picture this as a type of chronology of events list, which we utilize to revise our expectations of what will come next.
Language models like ChatGPT also monitor transformations within their own “mind” when completing a segment of code or anticipating what you’ll input next. They generally make informed predictions using transformers — internal frameworks that assist the models in comprehending sequential data — yet the systems can occasionally err due to faulty reasoning patterns. Recognizing and modifying these fundamental mechanisms aids language models in becoming more dependable forecasters, particularly with more fluid tasks such as predicting weather or financial trends.
But do these AI systems interpret evolving situations as we do? A recent study from researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Department of Electrical Engineering and Computer Science demonstrates that the models utilize clever mathematical shortcuts at each sequential step, ultimately making plausible predictions. The team made this finding by delving into the internals of language models, assessing how effectively they could track rapidly changing objects. Their results reveal that engineers can influence when language models apply specific workarounds to enhance the systems’ predictive abilities.
Shell games
The researchers examined the core functionalities of these models using an inventive experiment reminiscent of a traditional concentration game. Have you ever had to determine the final position of an object after it’s placed under a cup and shuffled among identical containers? The team employed a similar challenge, where the model predicted the ultimate arrangement of particular digits (also known as a permutation). The models were provided with an initial sequence, such as “42135,” and directives regarding when and where to shift each digit, such as moving the “4” to the third position and so on, without knowledge of the final outcome.
In these trials, transformer-based models progressively learned to foresee the correct final configurations. Instead of merely shuffling the digits according to the instructions provided, the systems aggregated information across successive states (or individual steps within the series) and computed the final permutation.
One prevalent pattern the team identified, termed the “Associative Algorithm,” essentially organizes nearby steps into clusters and then calculates a final prediction. You might envision this process as structured like a tree, where the initial numerical configuration represents the “root.” As you ascend the tree, adjacent steps are grouped into distinct branches and multiplied together. At the pinnacle of the tree is the concluding combination of numbers, derived by multiplying each resulting sequence across the branches.
The alternate method by which language models inferred the final permutation utilized an ingenious mechanism known as the “Parity-Associative Algorithm,” which essentially narrows down possibilities prior to grouping them. It establishes whether the final arrangement stems from an even or odd number of rearrangements of individual digits. Subsequently, the mechanism groups adjacent sequences from diverse steps before multiplying them, similar to the Associative Algorithm.
“These behaviors indicate that transformers engage in simulation via associative scanning. Rather than tracking state changes sequentially, the models categorize them into hierarchies,” remarks MIT PhD student and CSAIL affiliate Belinda Li SM ’23, a principal author on the study. “How do we motivate transformers to enhance their state tracking? Rather than enforcing that these systems formulate inferences about data in a human-like, sequential manner, we might consider accommodating the methods they intuitively employ when monitoring state changes.”
“One line of inquiry has involved expanding test-time computation along the depth dimension instead of the token dimension — by amplifying the number of transformer layers rather than the quantity of chain-of-thought tokens during test-time reasoning,” adds Li. “Our findings suggest that this methodology would enable transformers to construct deeper reasoning trees.”
Through the looking glass
Li and her co-authors scrutinized the workings of the Associative and Parity-Associative algorithms utilizing tools that allowed them to peer into the “mind” of language models.
They initially employed a technique called “probing,” which reveals what information flows through an AI system. Picture being able to look into a model’s mind to observe its thoughts at a particular moment — similarly, this technique maps the system’s mid-experiment predictions regarding the final configuration of digits.
A tool named “activation patching” was subsequently utilized to illustrate where the language model processes shifts in a situation. It involves tampering with some of the system’s “ideas,” injecting erroneous data into specific areas of the network while maintaining other segments constant, and observing how the system adjusts its forecasts.
These instruments uncovered when the algorithms would err and when the systems “realized” how to accurately predict the final permutations. They noted that the Associative Algorithm learned more swiftly than the Parity-Associative Algorithm, while also excelling on longer sequences. Li links the latter’s challenges with more complex instructions to an over-dependence on heuristics (or rules that enable us to compute a plausible solution quickly) for predicting permutations.
“We’ve discovered that when language models employ a heuristic early in training, they begin to integrate these shortcuts into their mechanisms,” says Li. “However, those models tend to generalize more poorly than ones that do not rely on heuristics. We found that certain pre-training objectives can either discourage or encourage these tendencies, so in the future, we may aim to devise strategies that prevent models from adopting detrimental habits.”
The researchers observe that their experiments were conducted on smaller-scale language models fine-tuned on synthetic data, yet found the model size had minimal effect on the outcomes. This implies that fine-tuning larger language models, like GPT 4.1, would likely yield comparable results. The team intends to explore their hypotheses further by testing language models of varying sizes that haven’t been fine-tuned, assessing their performance on dynamic real-world tasks such as tracking code and monitoring how stories unfold.
Harvard University postdoctoral researcher Keyon Vafa, who was not part of the study, remarks that the researchers’ discoveries could lead to advancements in language models. “Numerous applications of extensive language models hinge on tracking state: from offering recipes to coding to maintaining details in conversations,” he explains. “This study makes notable strides in understanding how language models execute these tasks. This advancement provides intriguing insights into what language models are accomplishing and presents promising new strategies for enhancing them.”
Li co-authored the paper with MIT undergraduate student Zifan “Carl” Guo and senior author Jacob Andreas, an MIT associate professor of electrical engineering and computer science and CSAIL principal investigator. Their research was partially supported by Open Philanthropy, the MIT Quest for Intelligence, the National Science Foundation, the Clare Boothe Luce Program for Women in STEM, and a Sloan Research Fellowship.
The researchers showcased their work at the International Conference on Machine Learning (ICML) this week.
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