Large Language Model
On the Noise Robustness of In-Context Learning for Text Generation
Large language models (LLMs) have shown impressive performance on downstream tasks by in-context learning (ICL), which heavily relies on the quality of demonstrations selected from a large set of annotated examples.
DPIC: Decoupling Prompt and Intrinsic Characteristics for LLM Generated Text Detection Xiao Y u 1,2, Y uang Qi
Consequently, detecting whether a text is generated by LLMs has become increasingly important.
AR-Pro: Counterfactual Explanations for Anomaly Repair with Formal Properties
Anomaly detection is widely used for identifying critical errors and suspicious behaviors, but current methods lack interpretability.
Embedding-Aligned Language Models Guy Tennenholtz
In this paper, we present a novel framework which accomplishes this by exploiting latent embedding spaces to define an objective function for an LLM in an iterative RL-driven process. As an example, consider the challenge of assisting content creators in generating valuable content within a recommender ecosystem (e.g., Y ouTube, Reddit, Spotify) [Boutilier et al., 2024].
Observational Scaling Laws and the Predictability of Language Model Performance
Self-Consistency as language model capabilities continue to improve.
Stress-T esting Long-Context Language Models with Lifelong ICL and Task Haystack
Task Haystack draws inspiration from the widely-adopted "needle-in-a-haystack"