Papers by Tiwalayo Eisape
A Systematic Comparison of Syllogistic Reasoning in Humans and Language Models (2024.naacl-long)
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| Challenge: | Psychologists have documented several ways in which humans’ inferences deviate from the rules of logic. |
| Approach: | They focus on syllogisms, which are inferences from two simple premises, and show that larger models are more logical than smaller ones. |
| Outcome: | The results show that language models often mimic human biases, but overcome them in some cases. |
Is this the real life? Is this just fantasy? The Misleading Success of Simulating Social Interactions With LLMs (2024.emnlp-main)
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| Challenge: | Recent advances in large language models have enabled richer social simulations . however, the role of information asymmetry in these simulations has been overlooked . |
| Approach: | They develop an evaluation framework to simulate social interactions with LLMs in different settings. |
| Outcome: | The proposed framework performs better in unrealistic, omniscient simulation settings but struggles in those with information asymmetry. |
When Does Syntax Mediate Neural Language Model Performance? Evidence from Dropout Probes (2022.naacl-main)
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| Challenge: | Recent studies show that models encode syntactic information redundantly . this allows researchers to boost models' performance by injecting syntaktic information into embeddings . |
| Approach: | They propose a new probe design that guides probes to consider all syntactic information present in embeddings. |
| Outcome: | The proposed model improves performance by injecting syntactic information into models. |
Probing for Incremental Parse States in Autoregressive Language Models (2022.findings-emnlp)
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| Challenge: | Existing work on autoregressive language models shows impressive command of syntax . implicit incremental syntactic inferences underlie next-word predictions . |
| Approach: | They propose a probe for extracting incomplete syntactic structure from autoregressive language models. |
| Outcome: | The proposed probes can predict model preferences on ambiguous sentence prefixes and causally intervene on model representations and steer model behavior. |