Can We Count on LLMs? The Fixed-Effect Fallacy and Claims of GPT-4 Capabilities (2409.07638v2)

Abstract: In this paper we explore evaluation of LLM capabilities. We present measurements of GPT-4 performance on several deterministic tasks; each task involves a basic calculation and takes as input parameter some element drawn from a large well-defined population (e.g., count elements in a list, multiply two k-digit numbers, etc). We examine several conditions per-task and perform enough trials so that statistically significant differences can be detected. This allows us to investigate the sensitivity of task-accuracy both to query phrasing and input parameter population. We find that seemingly trivial modifications in the task-prompt or input population can yield differences far larger than can be explained by sampling effects. For example, performance on a simple list-counting task varies with query-phrasing and list-length, but also with list composition (i.e., the thing-to-be-counted) and object frequency (e.g., success when an element accounts for $\approx$ 50\% of a list is different from when it accounts for $\approx$ 70\% etc). We conclude that efforts to quantify LLM capabilities easily succumb to the language-as-fixed-effect fallacy, where experimental observations are improperly generalized beyond what the data supports. A consequence appears to be that intuitions that have been formed based on interactions with humans form a very unreliable guide as to which input modifications should ``make no difference'' to LLM performance.

Can We Count on LLMs? The Fixed-Effect Fallacy and Claims of GPT-4 Capabilities

The discussed paper, authored by Thomas Ball, Shuo Chen, and Cormac Herley, explores the intricacies of evaluating the performance of LLMs, specifically GPT-4, on deterministic tasks. This paper provides pivotal insights into the capabilities and limitations of LLMs in performing seemingly simple tasks such as counting, multiplication, and sorting, among others. Importantly, it highlights the often overlooked fixed-effect fallacy and its implications on the perceived effectiveness and generalizability of LLM performance metrics.

Summary of Findings

The authors conducted extensive experiments across various deterministic tasks, ensuring that the analytical setup allowed for a systematic measurement of performance unaffected by subjective or erroneous assessments. They found the following key points:

1. Sensitivity to Prompt and Input Variability: The paper shows that LLM performance is highly sensitive to both the phrasing of the prompt and modifications in the input parameters. For instance, changing the wording of a query or the composition of the input list profoundly impacted GPT-4's accuracy in counting tasks. Performance varied significantly with seemingly trivial modifications, resulting in a drastic change in success rates.
2. Unreliable Generalization: The authors argue that many attempts to quantify LLM capabilities fall prey to the language-as-fixed-effect fallacy. This fallacy occurs when experimental observations are inappropriately generalized beyond the specific conditions under which they were gathered. The experiments demonstrated that minor changes to task wording or input characteristics could lead to significant performance variations, challenging the reliability of observed accuracy.
3. Performance Metrics: Numerical results from tasks such as counting showed that accuracy rates deteriorated rapidly with increased task complexity and variation in input conditions. For example, in the counting task with a fixed query phrasing using 'mango/peach', the accuracy dropped from 89.0% for length-10 lists to just 12.6% for length-40 lists. This degradation underscores the sharp impact of input length and composition on task performance.
4. Implications for LLM Capabilities: The findings warn against overgeneralizing LLM capabilities based on performance observed under narrowly defined conditions. The authors emphasize that skills exhibited by LLMs in specific scenarios do not necessarily translate into general, robust capabilities.

Implications and Future Directions

Practical Implications

From a practical standpoint, the paper signals caution to practitioners relying on LLMs for tasks requiring consistent and reliable performance across varied inputs and phrasings. The sensitivity of LLM performance to minor prompt modifications implies that applications in domains such as automated coding, legal text processing, and other high-stakes environments must be rigorously tested under multiple conditions to ensure robustness.

Theoretical Implications

Theoretically, this research challenges the prevailing notions about the emergent capabilities of LLMs, suggesting that some observed competencies might indeed be artefacts of specific experimental setups. This underscores the necessity for refined evaluation methodologies that can account for variability in both prompt phrasing and input parameters.

Future Developments in AI

Future developments in AI research should focus on mitigating the variabilities observed in LLM performance. This could involve:

• Enhancing the robustness of LLMs through advanced prompt-engineering techniques and fine-tuning processes.
• Developing standardized testing protocols that can simulate a broader range of conditions to better gauge the generalizability of LLM capabilities.
• Investigating the potential of Chain-of-Thought (CoT) and other reasoning-based methods to improve performance consistency across varied tasks.

Conclusion

The paper by Ball, Chen, and Herley provides a critical examination of LLM capabilities, drawing attention to the susceptibility of performance metrics to the fixed-effect fallacy. By illustrating the profound impact of minor variations in task setup, it underscores the need for cautious interpretation of LLM ability and advocates for more rigorous and comprehensive evaluation frameworks. This work serves as an important reminder to both researchers and practitioners of the complexities inherent in assessing the true capabilities of advanced AI systems like GPT-4.