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Found 4 papers, 1 papers with code
The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning
To measure these risks of malicious use, government institutions and major AI labs are developing evaluations for hazardous capabilities in LLMs.
Do Large GPT Models Discover Moral Dimensions in Language Representations? A Topological Study Of Sentence Embeddings
This indicates that GPT-based language models develop a moral dimension within their representation spaces and induce an understanding of fairness during their training process.
Personality Traits in Large Language Models
The advent of large language models (LLMs) has revolutionized natural language processing, enabling the generation of coherent and contextually relevant human-like text.
Parameter-Efficient Neural Question Answering Models via Graph-Enriched Document Representations
In this paper, we suggest that large language models are not necessary for good performance by showing a na\"{i}ve implementation of a GCN performs comparably to SoTA models based on pretrained language models.
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