Exposing Attention Glitches with Flip-Flop Language Modeling

Literature review

Podcast

Abstract

Why do large language models sometimes output factual inaccuracies and exhibit erroneous reasoning? The brittleness of these models, particularly when executing long chains of reasoning, currently seems to be an inevitable price to pay for their advanced capabilities of coherently synthesizing knowledge, pragmatics, and abstract thought. Towards making sense of this fundamentally unsolved problem, this work identifies and analyzes the phenomenon of attention glitches, in which the Transformer architecture’s inductive biases intermittently fail to capture robust reasoning. To isolate the issue, we introduce flip-flop language modeling (FFLM), a parametric family of synthetic benchmarks designed to probe the extrapolative behavior of neural language models. This simple generative task requires a model to copy binary symbols over long-range dependencies, ignoring the tokens in between. We find that Transformer FFLMs suffer from a long tail of sporadic reasoning errors, some of which we can eliminate using various regularization techniques. Our preliminary mechanistic analyses show why the remaining errors may be very difficult to diagnose and resolve. We hypothesize that attention glitches account for (some of) the closed-domain hallucinations in natural LLMs. – (Liu et al. 2023)

Outline

• Introduction
  • Describes the problem of factual inaccuracies and erroneous reasoning in large language models (LLMs), particularly in long chains of reasoning.
  • Presents integer addition problems as a simple example of algorithmic reasoning where LLMs exhibit sporadic errors, highlighting both their capabilities and limitations.
  • Introduces the concept of “attention glitches” as a potential explanation for these errors, suggesting that the Transformer architecture’s inductive biases may intermittently fail to capture robust reasoning.
• Flip-flop Automata and the FFLM Task
  • Defines flip-flop strings and flip-flop languages, focusing on a canonical family parameterized by the probabilities of write, read, and ignore instructions.
  • Introduces the flip-flop language modeling (FFLM) task, which involves training language models to generate or predict continuations of flip-flop strings, emphasizing the importance of perfect read operation accuracy.
  • Discusses the rationale for focusing on flip-flops, highlighting their role as fundamental building blocks of memory and their relevance to various reasoning tasks.
• Attention Glitches: A Long Tail of Errors for Transformer FFLMs
  • Presents the main empirical result: Transformer models trained on FFLM exhibit a long tail of unpredictable reasoning errors (attention glitches), even on simple tasks like remembering one bit.
  • Highlights the contrast between Transformers and LSTMs, showing that LSTMs achieve perfect accuracy on FFLM with significantly fewer resources.
  • Notes that similar attention glitches are observed in real LLMs when prompted to complete natural language embeddings of flip-flop tasks.
  • Discusses multiple potential mechanisms for attention glitches, including implicit n-gram models, Lipschitz limitations of soft attention, and the difficulty of non-commutative tiebreaking.
• Mitigations for Attention Glitches
  • Investigates various approaches to eliminate attention glitches in Transformer FFLMs, using a 6-layer 19M-parameter model as a canonical baseline.
  • Discusses the effects of training data and scale, showing that training on rare sequences significantly reduces errors, while resource scaling provides weaker improvements.
  • Explores indirect algorithmic controls, including standard regularization techniques and attention-sharpening regularizers, finding that some choices improve extrapolation but none completely eliminate glitches.
  • Presents a preliminary mechanistic study of trained networks, showing that attention-sharpening promotes hard attention but errors persist due to the complexity and redundancy of attention patterns.
• Conclusion and Future Challenges
  • Summarizes the findings, emphasizing that attention glitches represent a systematic architectural flaw in Transformers that may contribute to closed-domain hallucinations in natural LLMs.
  • Discusses the challenges of confirming or refuting the hypothesis that attention glitches cause hallucinations in natural LLMs, highlighting the need for further research.
  • Suggests potential paths to hallucination-free Transformers, including data diversity, scale, regularization, and architectural innovations inspired by recurrent models.
  • Mentions the broader impacts and limitations of the work, emphasizing its foundational nature and the potential for unintended consequences of improved factual reliability in LLMs.
• Appendix
  • Provides deferred background information on flip-flop terminology and history, including the definition of the flip-flop automaton and its transformation monoid.
  • Discusses additional related work on hallucinations, long-range dependencies, explicit memory mechanisms, and Transformers’ performance on algorithmic tasks.
  • Explains the rationale for the specific flip-flop language used in the study, highlighting its compatibility with standard language modeling and its parsimonious encoding.
  • Elaborates on the hypothesis that attention glitches cause hallucinations in natural LLMs, discussing the challenges of formalizing and testing this hypothesis.
  • Presents full experimental results, including details for LLM addition prompts, extrapolation failures of standard Transformers, effects of training data and scale, indirect algorithmic controls, and preliminary mechanistic studies.
  • Provides proofs for propositions related to the realizability of FFL by small Transformers, the failure of soft attention due to attention dilution, and the failure of hard attention due to bad margins for positional embeddings.
  • Notes the software, compute infrastructure, and resource costs associated with the experiments.

The Paper

paper

References

Liu, Bingbin, Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, and Cyril Zhang. 2023. “Exposing Attention Glitches with Flip-Flop Language Modeling.” https://arxiv.org/abs/2306.00946.