1 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.LG2024
Predicting Emergent Capabilities by Finetuning
Charlie Snell, Eric Wallace, Dan Klein +1
A fundamental open challenge in modern LLM scaling is the lack of understanding around emergent capabilities. In particular, language model pretraining loss is known to be highly p…
cs.CL2024★ 1 cited
Beyond Performance: Quantifying and Mitigating Label Bias in LLMs
Yuval Reif, Roy Schwartz
Large language models (LLMs) have shown remarkable adaptability to diverse tasks, by leveraging context prompts containing instructions, or minimal input-output examples. However,…
cs.CL2024★ 1 cited
What Evidence Do Language Models Find Convincing?
Alexander Wan, Eric Wallace, Dan Klein
Retrieval-augmented language models are being increasingly tasked with subjective, contentious, and conflicting queries such as "is aspartame linked to cancer". To resolve these am…