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Found 13 papers, 6 papers with code
Immunization against harmful fine-tuning attacks
Approaches to aligning large language models (LLMs) with human values has focused on correcting misalignment that emerges from pretraining.
Long-form evaluation of model editing
Evaluations of model editing currently only use the `next few token' completions after a prompt.
Self-Consistency of Large Language Models under Ambiguity
Using this test, we find that despite increases in self-consistency, models usually place significant weight on alternative, inconsistent answers.
Semantic Consistency for Assuring Reliability of Large Language Models
Large Language Models (LLMs) exhibit remarkable fluency and competence across various natural language tasks.
Background Knowledge Grounding for Readable, Relevant, and Factual Biomedical Lay Summaries
We found that grounding source documents improves the relevance and readability of lay summaries but does not improve factuality of lay summaries.
Mixture of Soft Prompts for Controllable Data Generation
Large language models (LLMs) effectively generate fluent text when the target output follows natural language patterns.
Measuring Reliability of Large Language Models through Semantic Consistency
While large pretrained language models (PLMs) demonstrate incredible fluency and performance on many natural language tasks, recent work has shown that well-performing PLMs are very sensitive to what prompts are feed into them.
Moving beyond word lists: towards abstractive topic labels for human-like topics of scientific documents
Our case study shows that in addition to more human-like topics there are additional advantages to evaluation by using clustering and summarization measures instead of topic model measures.
Using contradictions improves question answering systems
This work examines the use of contradiction in natural language inference (NLI) for question answering (QA).
SynSciPass: detecting appropriate uses of scientific text generation
By training the same model that performed well on DAGPap22 on SynSciPass, we show that not only is the model more robust to domain shifts but also is able to uncover the type of technology used for machine generated text.
Learning to pronounce as measuring cross-lingual joint orthography-phonology complexity
Machine learning models allow us to compare languages by showing how hard a task in each language might be to learn and perform well on.
Citations are not opinions: a corpus linguistics approach to understanding how citations are made
A key issue in citation content analysis is looking for linguistic structures that characterize distinct classes of citations for the purposes of understanding the intent and function of a citation.
How are journals cited? characterizing journal citations by type of citation
Using this citation index containing citation types based on citation function (supporting, disputing, or mentioning) we present initial results on the statistical characterization of citations to journals based on citation function.
