How catastrophic is your LLM?

Human Conversations Display LLM-Like Failure Modes: Limited Context, Overgeneration, and Hallucination

This reflective essay explores how classic Large Language Model (LLM) failure modes—such as limited context, overgeneration, poor generaliza

Comprehensive Survey of Reasoning Failures in Large Language Models

This article presents a comprehensive survey of reasoning failures in Large Language Models (LLMs), introducing a novel categorization frame

Scaling Laws Limit Reliability of Large Language Models, Study Finds

This research paper demonstrates that the scaling laws governing large language models (LLMs) fundamentally limit their ability to improve p

LLM Skirmish: An Adversarial In-Context Learning Benchmark for Evaluating Large Language Models

The article discusses LLM Skirmish, an adversarial in-context learning benchmark designed to test large language models through competitive

LK Losses: A New Training Objective to Optimize Acceptance Rate in Speculative Decoding for LLMs

This paper introduces LK losses, a novel training objective for speculative decoding in large language models (LLMs). Speculative decoding a

An investigation into code injection vulnerabilities caused by generative AI