Tootfinder

Im using case_when() quite a lot, case_match() is new to me: #rstats

Filling in some daft evaluation form and the "very poor" / "very good" choice things are amazing.

Colour for "very poor"? Yellow. Obviously.

Colour for "very good"? Deep red. OBVIOUSLY.

Who wouldn't like prices to start falling? Careful what you wish for, economists say (Paul Wiseman/Associated Press)
https://apnews.com/article/inflation-deflation-prices-economy-eggs-cars-consumers-e3c816373344d54b2e6bdb4be5ed999a
http://www.memeorandum.com/240330/p34#a240330p34

This https://arxiv.org/abs/2403.15454 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Emotion Detection with Transformers: A Comparative Study
In this study, we explore the application of transformer-based models for emotion classification on text data. We train and evaluate several pre-trained transformer models, on the Emotion dataset using different variants of transformers. The paper also analyzes some factors that in-fluence the performance of the model, such as the fine-tuning of the transformer layer, the trainability of the layer, and the preprocessing of the text data. Our analysis reveals that commonly applied techniques lik…

This https://arxiv.org/abs/2403.04299 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

LitSim: A Conflict-aware Policy for Long-term Interactive Traffic Simulation
Simulation is pivotal in evaluating the performance of autonomous driving systems due to the advantages of high efficiency and low cost compared to on-road testing. Bridging the gap between simulation and the real world requires realistic agent behaviors. However, the existing works have the following shortcomings in achieving this goal: (1) log replay offers realistic scenarios but often leads to collisions due to the absence of dynamic interactions, and (2) both heuristic-based and data-based…

Im using case_when() quite a lot, case_match() is new to me: #rstats

Filling in some daft evaluation form and the "very poor" / "very good" choice things are amazing.

Colour for "very poor"? Yellow. Obviously.

Colour for "very good"? Deep red. OBVIOUSLY.

Vortex glass transition and thermal creep in niobium films
Sameh M. Altanany, I. Zajcewa, T. Zajarniuk, A. Szewczyk, Marta Z. Cieplak
https://arxiv.org/abs/2403.20121

Vortex glass transition and thermal creep in niobium films
The evolution of the vortex glass (VG) phase transition and vortex creep with decreasing film thickness is studied in ultrathin, polycrystalline niobium films, with thickness in the range 7.4 nm to 44 nm, using current-voltage characteristics measurements in perpendicular magnetic field. Standard methods, including scaling laws, allow to identify VG transition in the thickest film, while in thinner films creep produces large uncertainty in the putative VG transition temperature and scaling expo…

This https://arxiv.org/abs/2402.18060 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Benchmarking Large Language Models on Answering and Explaining Challenging Medical Questions
LLMs have demonstrated impressive performance in answering medical questions, such as passing medical licensing examinations. However, most existing benchmarks rely on board exam questions or general medical questions, falling short in capturing the complexity of realistic clinical cases. Moreover, the lack of reference explanations for answers hampers the evaluation of model explanations, which are crucial to supporting doctors in making complex medical decisions. To address these challenges, …

When to Retrieve: Teaching LLMs to Utilize Information Retrieval Effectively
Tiziano Labruna, Jon Ander Campos, Gorka Azkune
https://arxiv.org/abs/2404.19705 https://arxiv.org/pdf/2404.19705
arXiv:2404.19705v1 Announce Type: new
Abstract: In this paper, we demonstrate how Large Language Models (LLMs) can effectively learn to use an off-the-shelf information retrieval (IR) system specifically when additional context is required to answer a given question. Given the performance of IR systems, the optimal strategy for question answering does not always entail external information retrieval; rather, it often involves leveraging the parametric memory of the LLM itself. Prior research has identified this phenomenon in the PopQA dataset, wherein the most popular questions are effectively addressed using the LLM's parametric memory, while less popular ones require IR system usage. Following this, we propose a tailored training approach for LLMs, leveraging existing open-domain question answering datasets. Here, LLMs are trained to generate a special token, , when they do not know the answer to a question. Our evaluation of the Adaptive Retrieval LLM (Adapt-LLM) on the PopQA dataset showcases improvements over the same LLM under three configurations: (i) retrieving information for all the questions, (ii) using always the parametric memory of the LLM, and (iii) using a popularity threshold to decide when to use a retriever. Through our analysis, we demonstrate that Adapt-LLM is able to generate the token when it determines that it does not know how to answer a question, indicating the need for IR, while it achieves notably high accuracy levels when it chooses to rely only on its parametric memory.