Finally, we show LLM generated decision tree explanations correlate highly with human ratings of readability, quality, and use of background knowledge while simultaneously providing better understanding of decision boundaries.
Our paper investigates the use of discourse embedding techniques to develop a community recommendation system that focuses on mental health support groups on social media.
To overcome this limitation, recent autoregressive search engines replace the dual-encoder architecture by directly generating identifiers for relevant documents in the candidate pool.
In an effort to improve the performance of code search, we have investigated docid representation strategies, impact of tokenization on docid structure, and dataset sizes on overall code search performance.
Detecting security vulnerabilities in software before they are exploited has been a challenging problem for decades.