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Found 11 papers, 5 papers with code
Reasoning in Token Economies: Budget-Aware Evaluation of LLM Reasoning Strategies
A diverse array of reasoning strategies has been proposed to elicit the capabilities of large language models.
Mixture-of-Agents Enhances Large Language Model Capabilities
With the growing number of LLMs, how to harness the collective expertise of multiple LLMs is an exciting open direction.
FinRobot: An Open-Source AI Agent Platform for Financial Applications using Large Language Models
As financial institutions and professionals increasingly incorporate Large Language Models (LLMs) into their workflows, substantial barriers, including proprietary data and specialized knowledge, persist between the finance sector and the AI community.
LLM-Resistant Math Word Problem Generation via Adversarial Attacks
Large language models (LLMs) have significantly transformed the educational landscape.
Maestro: A Gamified Platform for Teaching AI Robustness
We assessed Maestro's influence on students' engagement, motivation, and learning success in robust AI.
NeuroComparatives: Neuro-Symbolic Distillation of Comparative Knowledge
We introduce NeuroComparatives, a novel framework for comparative knowledge distillation overgenerated from language models such as GPT-variants and LLaMA, followed by stringent filtering of the generated knowledge.
Primal Dual Alternating Proximal Gradient Algorithms for Nonsmooth Nonconvex Minimax Problems with Coupled Linear Constraints
The iteration complexity of the two algorithms are proved to be $\mathcal{O}\left( \varepsilon ^{-2} \right)$ (resp.
Attention Augmented ConvNeXt UNet For Rectal Tumour Segmentation
It is a challenge to segment the location and size of rectal cancer tumours through deep learning.
GAP-Gen: Guided Automatic Python Code Generation
In this work, we propose GAP-Gen, a Guided Automatic Python Code Generation method based on Python syntactic constraints and semantic constraints.
Ranked #2 on
Code Generation
on CodeXGLUE - CodeSearchNet
(using extra training data)
Gradient-based Analysis of NLP Models is Manipulable
Gradient-based analysis methods, such as saliency map visualizations and adversarial input perturbations, have found widespread use in interpreting neural NLP models due to their simplicity, flexibility, and most importantly, their faithfulness.
AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models
Neural NLP models are increasingly accurate but are imperfect and opaque---they break in counterintuitive ways and leave end users puzzled at their behavior.
