Search Results for author:
Found 14 papers, 6 papers with code
LLM as a Mastermind: A Survey of Strategic Reasoning with Large Language Models
This paper presents a comprehensive survey of the current status and opportunities for Large Language Models (LLMs) in strategic reasoning, a sophisticated form of reasoning that necessitates understanding and predicting adversary actions in multi-agent settings while adjusting strategies accordingly.
Will GPT-4 Run DOOM?
We show that GPT-4's reasoning and planning capabilities extend to the 1993 first-person shooter Doom.
I Wish to Have an Argument: Argumentative Reasoning in Large Language Models
We frame our experiments in terms of the argument mining (AM) and argument pair extraction (APE) tasks, and evaluate their ability to perform reasoning at increasing levels of abstraction in the input and output representations (e. g., arbitrary label sets, semantic graphs).
Are Large Language Model-based Evaluators the Solution to Scaling Up Multilingual Evaluation?
Large Language Models (LLMs) excel in various Natural Language Processing (NLP) tasks, yet their evaluation, particularly in languages beyond the top $20$, remains inadequate due to existing benchmarks and metrics limitations.
A User-Centered Evaluation of Spanish Text Simplification
We present an evaluation of text simplification (TS) in Spanish for a production system, by means of two corpora focused in both complex-sentence and complex-word identification.
An Evaluation on Large Language Model Outputs: Discourse and Memorization
We present an empirical evaluation of various outputs generated by nine of the most widely-available large language models (LLMs).
Turing Completeness and Sid Meier's Civilization
We prove that three strategy video games from the Sid Meier's Civilization series: Sid Meier's Civilization: Beyond Earth, Sid Meier's Civilization V, and Sid Meier's Civilization VI, are Turing complete.
Optimal Subarchitecture Extraction For BERT
We extract an optimal subset of architectural parameters for the BERT architecture from Devlin et al. (2018) by applying recent breakthroughs in algorithms for neural architecture search.
Mischief: A Simple Black-Box Attack Against Transformer Architectures
Our findings show that the presence of Mischief-generated adversarial samples in the test set significantly degrades (by up to $20\%$) the performance of these models with respect to their reported baselines.
An Approximation Algorithm for Optimal Subarchitecture Extraction
We consider the problem of finding the set of architectural parameters for a chosen deep neural network which is optimal under three metrics: parameter size, inference speed, and error rate.
An Algorithm for Learning Smaller Representations of Models With Scarce Data
In the former case we obtain an asymptotic bound of $O\left(|\Theta^2|\left(\log{|\Theta|} + |\theta^2| + T_f\left(| D|\right)\right) + \bar{S}|\Theta||{E}|\right)$, where $|{\Theta}|$ is the cardinality of the set of hyperparameters $\theta$ to be searched; $|{E}|$ and $|{D}|$ are the sizes of the evaluation and training datasets, respectively; $\bar{S}$ and $\bar{f}$ are the inference times for the trained model and the candidate model; and $T_f({|{D}|})$ is a polynomial on $|{D}|$ and $\bar{f}$.
On the Bounds of Function Approximations
For this, we first reformulate the function approximation problem in terms of sequences of functions, and we call it the Function Approximation (FA) problem; then we show that it is computationally infeasible to devise a procedure that solves FA for all functions to zero error, regardless of the search space.
Leveraging External Knowledge for Out-Of-Vocabulary Entity Labeling
This network projects the slot into an attribute space derived from the KB, and, by leveraging similarities in this space, we propose candidate slot keys and values to the dialogue state tracker.
