Summary Embers of Autoregression Understanding Large Language Models arxiv.org
48,510 words - PDF document - View PDF document
One Line
Language models are biased towards common tasks and patterns, making systematic reasoning challenging, though careful prompting may provide some assistance.
Slides
Slide Presentation (14 slides)
Key Points
- Large language models (LLMs) are heavily influenced by the probability of their outputs, even for deterministic tasks, favoring high-probability sequences over low-probability ones
- LLMs exhibit systematic biases and limitations in tasks like shift ciphers, reversals, and counting, suggesting their reasoning is constrained by autoregressive structure
- However, LLMs can also exhibit more flexible reasoning, as shown in their ability to solve Pig Latin and article swapping tasks
- LLMs' performance is shaped by the statistical properties of the language they are trained on, highlighting the importance of understanding their biases and limitations
- Probing LLMs' capabilities across diverse tasks provides valuable insights into their inner workings and the interplay of different cognitive capabilities
Summaries
22 word summary
LLMs are biased towards common tasks and patterns, not systematic reasoning. Careful prompting may help, but overcoming language modeling constraints remains challenging.
47 word summary
LLMs like GPT-3.5 and GPT-4 are biased towards common tasks, high-probability outputs/inputs, and statistical patterns in training data, rather than systematic reasoning. While impressive, they are language models, not general reasoning systems. Careful prompting and calibration may help, but overcoming language modeling constraints remains a key challenge.
109 word summary
Large language models (LLMs) like GPT-3.5 and GPT-4 are trained for next-word prediction, leading to three "embers of autoregression": sensitivity to task frequency, output probability, and input probability. LLMs perform better on common tasks and high-probability outputs/inputs, even for deterministic tasks. This suggests LLMs are heavily influenced by statistical patterns in their training data rather than systematic logical reasoning. While LLMs can achieve impressive results, they are fundamentally language models, not general reasoning systems. Careful prompting, scaling, and calibration may help, but overcoming the constraints of language modeling remains a key challenge. Adopting a teleological perspective can aid in understanding LLM strengths, weaknesses, and future developments in language modeling.
406 word summary
Large language models (LLMs) like GPT-3.5 and GPT-4 are trained to perform the task of next-word prediction on internet text. This "teleological approach" to their training can help explain certain quirks and limitations of these models when applied to tasks beyond next-word prediction.
We identify three key "embers of autoregression" that stem from this training objective:
1. Sensitivity to task frequency: LLMs perform better on tasks that are common in their training data, even if the tasks have equivalent complexity. For example, GPT-4 achieves higher accuracy on common Pig Latin variants compared to rare ones.
2. Sensitivity to output probability: LLMs are more accurate when the correct answer is high-probability text, even for deterministic tasks. For instance, GPT-4 is more accurate at reversing high-probability sentences than low-probability ones.
3. Sensitivity to input probability: LLMs sometimes perform better on deterministic tasks when the input text is high-probability, though input probability is less influential than output probability. GPT-4 is more accurate at decoding high-probability sentences in a simple cipher compared to low-probability ones.
We test these predictions across a range of tasks, including shift ciphers, Pig Latin, acronyms, and linear functions. The results demonstrate that LLMs' autoregressive training causes them to be highly sensitive to the statistical properties of their inputs and outputs, even for deterministic tasks.
For example, in shift cipher decoding, GPT-4 had 51% accuracy for high-probability outputs but only 13% for low-probability ones. Similar effects were seen in word reversal and Pig Latin translation tasks, where accuracy increased with output probability.
However, input probability had mixed effects, with significant impacts only in some cases. This suggests output probability is a more pervasive factor in influencing LLM performance.
Beyond these three effects, the teleological approach also provides a unifying perspective on other LLM behaviors, such as lack of embodiment, sensitivity to wording, difficulty on meaning-dependent tasks, and limitations in compositionality and systematicity.
While LLMs can be scaled up to achieve impressive results on many tasks, our findings highlight that they are fundamentally language models, not general reasoning systems. Their performance is heavily influenced by statistical patterns in their training data rather than systematic application of logical rules.
Careful prompting, scaling, and calibration may help mitigate these issues, but overcoming the fundamental constraints of language modeling remains an important challenge for advancing AI capabilities. Adopting a teleological perspective when analyzing LLMs can help us better understand their strengths and weaknesses and guide future developments in language modeling.
1712 word summary
Large language models (LLMs) have become widely adopted, but understanding their strengths and limitations is crucial. The authors argue that to develop a holistic understanding of these systems, we must consider the problem they were trained to solve: next-word prediction over internet text. This "teleological approach" leads to three key factors that influence LLM accuracy: task probability, target output probability, and input probability.
The authors predict that LLMs will achieve higher accuracy when these probabilities are high, even in deterministic settings. To test this, they evaluate GPT-3.5 and GPT-4 on various tasks, revealing surprising failure modes. For example, GPT-4's accuracy at decoding a simple cipher drops from 51% for high-probability outputs to only 13% for low-probability outputs.
These results show that AI practitioners should be cautious when using LLMs in low-probability situations. The authors conclude that we should not evaluate LLMs as if they are human, but rather treat them as a distinct system shaped by the pressures of next-word prediction. This teleological approach provides a framework for understanding LLM behavior and predicting their performance across different tasks and contexts.
Large language models (LLMs) like GPT-4 are trained to perform the task of next-word prediction on Internet text. This training objective, which we refer to as the "teleological approach," can help explain certain quirks and limitations of these models when applied to tasks beyond next-word prediction.
We hypothesize three key "embers of autoregression" that stem from this training objective:
1. Sensitivity to task frequency: LLMs perform better on tasks that are common in their training data, even if the tasks have equivalent complexity. For example, GPT-4 achieves higher accuracy on common Pig Latin variants compared to rare ones.
2. Sensitivity to output probability: LLMs are more accurate when the correct answer is high-probability text, even for deterministic tasks. For instance, GPT-4 is more accurate at reversing high-probability sentences than low-probability ones.
3. Sensitivity to input probability: LLMs sometimes perform better on deterministic tasks when the input text is high-probability, though input probability is less influential than output probability. GPT-4 is more accurate at decoding high-probability sentences in a simple cipher compared to low-probability ones.
Beyond these three effects, the teleological approach also provides a unifying perspective on other LLM behaviors, such as lack of embodiment, sensitivity to wording, difficulty on meaning-dependent tasks, and limitations in compositionality and systematicity. Understanding these "embers of autoregression" is crucial as LLMs become more widely deployed, to anticipate their failures and biases.
Large language models (LLMs) are statistical systems trained on internet text, making them sensitive to the statistics of word sequences in that text. We make three key predictions about LLM performance:
1) LLMs will perform worse on rare tasks than on frequent tasks, even when task complexity is equal.
2) LLMs will be sensitive to the probability of the answers they must produce, even for deterministic tasks.
3) LLMs will be sensitive to the probability of their inputs, but to a lesser degree than output probability.
We test these predictions across a range of tasks, including shift ciphers, Pig Latin, acronyms, and linear functions. For shift ciphers, LLMs perform much better on the common rot-13 cipher than on rare shift levels. Similarly, they excel at the frequent Pig Latin variant but struggle with the rare Boar Etruscan. For acronyms, they are far more accurate at forming first-letter acronyms than second-letter ones. And they apply the common Celsius-to-Fahrenheit linear function much better than a rare but similar function.
These results cannot be explained by task complexity, as the rare and common variants are equally straightforward computationally. Rather, they demonstrate that LLMs' autoregressive training causes them to be highly sensitive to the statistical properties of their inputs and outputs, even for deterministic tasks. This highlights the importance of considering LLMs' probabilistic nature when evaluating their capabilities.
Large language models (LLMs) like GPT-3.5 and GPT-4 perform better on common task variants than rare ones, even when the tasks are deterministic. This suggests LLMs are sensitive to the probability of the task or output.
In shift cipher decoding, GPT-4 had 51% accuracy for high-probability outputs but only 13% for low-probability ones. Models often produced incorrect but higher-probability outputs. Similar effects were seen in word reversal and Pig Latin translation tasks.
For article swapping and acronym formation, accuracy increased with output probability, even when input probability was controlled. This indicates output probability is a key factor.
Counting performance also varied with output probability - models were more accurate when counting to common numbers. Input probability had mixed effects, with significant impacts only in some cases. This suggests input probability is less pervasive than output probability in influencing LLM performance.
Overall, these results demonstrate LLMs are heavily influenced by the probability of their outputs, even for deterministic tasks. This highlights their tendency to favor high-probability sequences, which can lead to errors when the correct answer is low-probability. The findings situate LLM abilities within a broader teleological perspective focused on probability.
Our experiments have revealed several key insights about the behavior of large language models (LLMs) like GPT-3.5 and GPT-4:
LLMs are more sensitive to output probability than input probability. They perform better on tasks involving high-probability outputs, even when the inputs are low-probability. This effect was observed for tasks like decoding shift ciphers and forming acronyms.
However, input probability can have a large effect in certain cases, such as when models are asked to provide the birthdays of public figures. Models performed much better on common figures than rare ones, suggesting they have memorized information about frequent entities rather than applying a systematic function.
LLMs also display sensitivity to the wording and formatting of prompts. For example, they performed better at applying a linear function (the Celsius-to-Fahrenheit conversion) when the prompt explicitly mentioned the context, compared to just presenting the abstract equation. Similarly, their accuracy on multiplication varied widely depending on whether the numbers were presented as digits, words, or in alternating capitalization.
These findings highlight that LLMs are fundamentally language models, not general reasoning systems. Their performance is heavily influenced by statistical patterns in their training data rather than systematic application of logical rules.
While these limitations may seem obvious in hindsight, they are not always accounted for when evaluating or deploying LLMs. Our teleological approach of analyzing the problems LLMs were trained to solve can help uncover such model-specific biases and failure modes. Careful prompting, scaling, and calibration may help mitigate these issues, but overcoming the fundamental constraints of language modeling remains an important challenge for advancing AI capabilities.
Our analysis of large language models (LLMs) reveals that they are fundamentally shaped by the statistical properties of their training data and architecture, rather than exhibiting human-like reasoning or understanding. We find that LLMs struggle with tasks that require meaning-based processing, such as tracking entities or handling negation, as they are primarily models of language rather than thought. Additionally, LLMs are limited by their autoregressive nature, which prevents them from planning ahead or correcting errors in their previous output.
We also show that LLM performance is heavily influenced by the probability distribution of their training data. They exhibit biases and rely on spurious correlations, and can even memorize specific sentences that are frequently repeated in their training corpus. Furthermore, the choice of input representation, such as subword tokenization, can impact their performance on character-level manipulations.
While LLMs can be scaled up to achieve impressive results on many tasks, we find that this scaling does not fully overcome the fundamental limitations imposed by their training objective and architecture. Nonetheless, we recognize that LLMs also have beneficial properties, such as their generality and ability to learn efficiently from natural data.
Our findings highlight the importance of adopting a teleological perspective when analyzing LLMs, focusing on the specific problem they are designed to solve rather than anthropomorphizing their capabilities. This approach can help us better understand the strengths and weaknesses of these systems and guide future developments in language modeling.
Large language models (LLMs) have shown impressive capabilities, but also raise important questions about their inner workings and limitations. This paper explores the "embers of autoregression" - the residual autoregressive structure that can persist in LLMs even after pretraining on vast amounts of data. The authors investigate how this autoregressive structure affects the models' reasoning abilities through a series of targeted regression tests.
Key findings include: - LLMs exhibit systematic biases and limitations in tasks like shift ciphers, reversals, and counting, suggesting their reasoning is constrained by autoregressive structure. - However, LLMs can also exhibit more flexible reasoning, as shown in their ability to solve Pig Latin and article swapping tasks. - The authors propose that LLMs may combine autoregressive and more flexible reasoning, with the balance depending on the task and prompting. - Overall, the results highlight the need to better understand the interplay of different cognitive capabilities in large language models, and how to effectively leverage their strengths while mitigating their limitations.
Large language models (LLMs) like GPT-3.5 and GPT-4 can perform a variety of language tasks, from shift ciphers and Pig Latin to counting and sorting. Statistical analyses reveal key insights about their capabilities:
For shift ciphers, LLMs show better performance on the more common rot-13 cipher compared to rot-2. Their accuracy is influenced by the probability of the input and output, with higher-probability examples yielding better results. Prompting techniques like step-by-step and chain-of-thought can further improve performance.
In Pig Latin tasks, LLMs are sensitive to the specific variant used, with more common variants like "-way" yielding higher accuracy. As with shift ciphers, output probability is a key factor, while input probability has little effect.
For sorting tasks, LLMs are much better at alphabetical order than reverse alphabetical order, reflecting the relative frequencies of these orderings in natural language. Ascending and descending order show a smaller gap in performance.
Counting tasks reveal that LLMs are highly sensitive to the probability of the output number, while the magnitude of the number matters more for character-based counting than word-based. Input probability can also influence counting accuracy, especially for rare numbers.
Overall, these findings demonstrate that LLMs' performance is shaped by the statistical properties of the language they are trained on, highlighting the importance of understanding their biases and limitations. Probing their capabilities across diverse tasks provides valuable insights into their inner workings.