Simple Linguistic Inferences of Large Language Models (LLMs): Blind Spots and Blinds (2305.14785v2)

Abstract: We evaluate LLMs' language understanding capacities on simple inference tasks that most humans find trivial. Specifically, we target (i) grammatically-specified entailments, (ii) premises with evidential adverbs of uncertainty, and (iii) monotonicity entailments. We design evaluation sets for these tasks and conduct experiments in both zero-shot and chain-of-thought setups, and with multiple prompts and LLMs. The models exhibit moderate to low performance on these evaluation sets. Subsequent experiments show that embedding the premise in syntactic constructions that should preserve the entailment relations (presupposition triggers) or change them (non-factives), further confuses the models, causing them to either under-predict or over-predict certain entailment labels regardless of the true relation, and often disregarding the nature of the embedding context. Overall these results suggest that, despite LLMs' celebrated language understanding capacity, even the strongest models have blindspots with respect to certain types of entailments, and certain information-packaging structures act as ``blinds'' overshadowing the semantics of the embedded premise.

• The paper demonstrates that even advanced LLMs, including GPT-4, exhibit systematic blind spots in basic linguistic inference tasks.
• The study employs zero-shot and chain-of-thought setups to isolate challenges in grammatically-specified entailments, evidential adverbs, and monotonicity entailments.
• These findings suggest that current pre-training paradigms need refinement to capture essential linguistic nuances and improve model comprehension.

Analyzing Linguistic Inference Capabilities and Limitations of LLMs

The paper "Simple Linguistic Inferences of LLMs: Blind Spots and Blinds" conducts a thorough exploration of LLMs in terms of their ability to make simple linguistic inferences that are trivial for humans. With a focus on specific inference tasks, the authors dissect both the strengths and notable limitations of LLMs, thereby advancing our understanding of these models' linguistic competence.

Core Linguistic Inference Tasks and Methodology

The exploration focuses on three types of linguistic inferences: grammatically-specified entailments, usage of evidential adverbs of uncertainty, and monotonicity entailments. Each type represents a fundamental aspect of linguistic understanding that humans typically process without difficulty. The paper encompasses experiments across several LLMs in zero-shot and chain-of-thought setups, covering both isolated linguistic phenomena and those embedded within syntactic structures designed to either reinforce or suppress entailment.

Results and Observations

The experimental results reveal a moderate to low performance of LLMs on the selected inference tasks, with a particularly stark contrast to human-level performance. Notably, even state-of-the-art models like GPT-4 failed to consistently achieve human-level accuracy across all tasks. The models frequently struggled with distinguishing entailments when premises were embedded within linguistic contexts like presupposition triggers or non-factives. This reveals systematic blind spots in their comprehension abilities.

While the GPT-4 model demonstrated some improvement over its predecessors, particularly in handling certain inference tasks more accurately, it still fell short in terms of reaching human-like performance. This suggests that fundamental limitations persist even in the most advanced models available today.

Practical and Theoretical Implications

The research highlights significant gaps in LLMs' ability to process natural language in a human-like manner. These gaps are particularly apparent in tasks involving nuances such as evidential adverbs and logically straightforward entailments, which have not been adequately captured by pre-training data and methodologies. These findings raise important questions about the models' linguistic competence, suggesting that current pre-training paradigms may not be sufficient for encoding all necessary linguistic nuances.

The persistence of these limitations indicates that future research should focus on developing techniques or architectures capable of overcoming these blind spots. This could involve refining training data, incorporating deeper linguistic theories into model development, or revisiting the models' interpretative frameworks.

Conclusion and Future Directions

The paper underscores the imperative need for continued research to address these systematic deficiencies in LLMs. It brings to the forefront the importance of developing richer, more nuanced evaluation benchmarks and methodologies aimed at capturing the extent of linguistic understanding. Consequently, this research not only enhances our grasp of current LLM capacities but also sets the stage for future advancements in artificial intelligence and natural language processing.

Overall, the paper provides invaluable insights into the nuanced domain of linguistic inferences and reaffirms the necessity for ongoing inquiry into the shortcomings of LLMs, encouraging a transition from superficial accuracy toward genuine semantic understanding.