Why Context Still Tricks Big Language Models
While large language models handle irrelevant context well in aggregate, specific instances reveal vulnerabilities. This inconsistency highlights the need for more precise evaluation.
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Large Language Models: More Than Autocomplete
Why large language models do more than complete text, and how context, attention, and chain-of-thought prompting change their answers.
Literature Review: Large Language Models For Practical Exploitation
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Common Anti-Patterns to Avoid When Working with Large Language Models
Anti-patterns observed while working extensively with LLMs — from redundant context to over‑engineering.
instavm.io·7mo ago
Beyond Autoregression: LLaDA2.1 and the Case for Self-Editing Language Models
Introduction Every mainstream large language model today generates text the same way: one token at a time, left to right, no looking back. I
Study reveals why in-context learning fails on complex specification-heavy tasks and how fine-tuning can help
In-context learning (ICL) has become the default method for using large language models (LLMs), making the exploration of its limitations an
arxiv.org·26d ago
Are Large Language Models a Large Research Problem?
There are use cases for LLMs in online research but behavioral scientists have growing concerns about the impact large language models might
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