Are Language Models Worse than Humans at Following Prompts? It’s Complicated (2023.findings-emnlp)
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| Challenge: | Recent work shows that language models perform surprisingly well when given intentionally irrelevant or misleading prompts. |
| Approach: | They challenge an assumption that humans would perform badly when given pathological instructions by ignoring irrelevant prompts and following them faithfully when given misleading instructions. |
| Outcome: | The proposed model performs well when given intentionally irrelevant or misleading prompts, whereas models do not. |
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| Challenge: | Recent studies show that prompts help models to learn faster in the same way that humans learn faster when provided with task instructions expressed in natural language. |
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Are Prompt-based Models Clueless? (2022.acl-long)
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| Challenge: | Large Language Models (LLMs) have demonstrated exceptional proficiency in instruction-following, making them increasingly integral to various applications. |
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| Challenge: | a high prompt sensitivity has been widely accepted as a core limitation of large language models . a recent study suggests that prompt senescence may be an artifact of evaluation processes . |
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Psychometric Predictive Power of Large Language Models (2024.findings-naacl)
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| Challenge: | despite efforts to align large language models with human preferences, instruction tuning does not always make LLMs human-like from a cognitive modeling perspective. |
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Demystifying Prompts in Language Models via Perplexity Estimation (2023.findings-emnlp)
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| Challenge: | Language models can be prompted to perform a wide variety of tasks with zero- and few-shot learning. |
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Unnatural language processing: How do language models handle machine-generated prompts? (2023.findings-emnlp)
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| Challenge: | Language model prompt optimization research has shown that semantically and grammatically well-formed manually crafted prompts are outperformed by automatically generated token sequences with no apparent meaning or syntactic structure. |
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Can Prompt Probe Pretrained Language Models? Understanding the Invisible Risks from a Causal View (2022.acl-long)
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| Challenge: | Recent studies have found prompt-based probing evaluations inaccurate, inconsistent and unreliable. |
| Approach: | They propose to conduct debiasing via causal intervention to uncover biases in probing evaluations . authors argue that prompt-based probing is inaccurate, inconsistent and unreliable . |
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