The shelf life of LLM findings

Things we learned about LLMs in 2024

A lot has happened in the world of Large Language Models over the course of 2024. Here’s a review of things we figured out about the field in the past …

Thought experiment in the National Library of Thailand

With the advent of ChatGPT, large language models (LLMs) went from a relatively niche topic to something that many, many people have been…

Beyond Model Collapse: Scaling Up with Synthesized Data Requires...

Large Language Models (LLM) are increasingly trained on data generated by other LLM, either because generated text and images become part of the pre-training corpus, or because synthetized data is...

Hallucination is Inevitable: An Innate Limitation of Large Language Models

Hallucination has been widely recognized to be a significant drawback for large language models (LLMs). There have been many works that attempt to reduce the extent of hallucination. These efforts...

LLMs and World Models, Part 1

How do Large Language Models Make Sense of Their “Worlds”?

Mitigating Cross-Lingual Cultural Inconsistencies in LLMs via...

Despite their impressive capabilities, multilingual large language models (MLLMs) frequently exhibit inconsistent behaviour when the prompt's language changes. While such adaptation is generally...

Augmenting Research Ideation with Data: An Empirical Investigation...

Recent advancements in large language models (LLMs) demonstrate strong potential for generating novel research ideas, yet such ideas often struggle with feasibility and effectiveness. In this...

NovGauge: A Fine-Grained Benchmark for Diagnosing LLMs'...

Large language models (LLMs) are increasingly used in peer review at major AI conferences, yet novelty remains a persistent weak point. Existing benchmarks assess novelty as a single holistic...

Hypothesis generation and updating in large language models

Large language models (LLMs) increasingly help people solve problems, from debugging code to repairing machinery. This process requires generating plausible hypotheses from partial descriptions,...

Alex Veremeyenko on Twitter / X

what are large language models actually doing?i read the 2025 textbook "Foundations of Large Language Models" by tong xiao and jingbo zhu and for the first time, i truly understood how they work.here’s everything you need to know about llms in 3 minutes↓ pic.twitter.com/OeYLuU3cWh— Alex Veremeyenko (@alex_verem) July 26, 2025

LLMs are good at playing you

Large language models seem just like humans, but some of this is in our heads.

Data Prior to Language

If the author is dead, why isn't the LLM?

The Case Against LLMs as Rerankers

Authors: Apoorva Joshi, Zhenmei Shi, Akshay Goindani, Hong LiuResearch Leads: Zhenmei Shi, Akshay Goindani, Hong Liu Large language models are increasingly being used for a broad range of tasks, in…

Scaling Laws Across Model Architectures: A Comparative Analysis of...

The scaling of large language models (LLMs) is a critical research area for the efficiency and effectiveness of model training and deployment. Our work investigates the transferability and...

Large language model

A large language model (LLM) is a neural network trained on a vast amount of text for natural language processing tasks, especially language generation. LLMs can typically generate, summarize, translate, and analyze text in many contexts, and are a foundational technology behind modern chatbots.[1] Biased or inaccurate training data can make an LLM's output less reliable.[2]