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Found 7 papers, 6 papers with code
Logits of API-Protected LLMs Leak Proprietary Information
The commercialization of large language models (LLMs) has led to the common practice of high-level API-only access to proprietary models.
Closing the Curious Case of Neural Text Degeneration
We provide a theoretical explanation for the effectiveness of the truncation sampling by proving that truncation methods that discard tokens below some probability threshold (the most common type of truncation) can guarantee that all sampled tokens have nonzero true probability.
Increasing Probability Mass on Answer Choices Does Not Always Improve Accuracy
For example, both normalization and prompting methods for reducing SFC can be ineffective or even detrimental to task performance for some LMs.
Lila: A Unified Benchmark for Mathematical Reasoning
Mathematical reasoning skills are essential for general-purpose intelligent systems to perform tasks from grocery shopping to climate modeling.
Ranked #1 on
Mathematical Reasoning
on Lila (OOD)
Decomposed Prompting: A Modular Approach for Solving Complex Tasks
On symbolic reasoning tasks, we can further decompose sub-tasks that are hard for LLMs into even simpler solvable sub-tasks.
What Makes Instruction Learning Hard? An Investigation and a New Challenge in a Synthetic Environment
We propose Hard RegSet as a challenging instruction learning task, and a controlled environment for studying instruction learning.
Causal Analysis of Syntactic Agreement Mechanisms in Neural Language Models
Targeted syntactic evaluations have demonstrated the ability of language models to perform subject-verb agreement given difficult contexts.
