Search Results for author:
Found 9 papers, 4 papers with code
Imposter.AI: Adversarial Attacks with Hidden Intentions towards Aligned Large Language Models
Unlike conventional methods that target explicit malicious responses, our approach delves deeper into the nature of the information provided in responses.
Can multiple-choice questions really be useful in detecting the abilities of LLMs?
Additionally, we propose two methods to quantify the consistency and confidence of LLMs' output, which can be generalized to other QA evaluation benchmarks.
Concatenated Masked Autoencoders as Spatial-Temporal Learner
Learning representations from videos requires understanding continuous motion and visual correspondences between frames.
TCRA-LLM: Token Compression Retrieval Augmented Large Language Model for Inference Cost Reduction
Our summarization compression can reduce 65% of the retrieval token size with further 0. 3% improvement on the accuracy; semantic compression provides a more flexible way to trade-off the token size with performance, for which we can reduce the token size by 20% with only 1. 6% of accuracy drop.
CARE-MI: Chinese Benchmark for Misinformation Evaluation in Maternity and Infant Care
In an effort to minimize the reliance on human resources for performance evaluation, we offer off-the-shelf judgment models for automatically assessing the LF output of LLMs given benchmark questions.
Tell Me How to Survey: Literature Review Made Simple with Automatic Reading Path Generation
In this paper, we introduce a new task named Reading Path Generation (RPG) which aims at automatically producing a path of papers to read for a given query.
Linguistic Characterization of Divisive Topics Online: Case Studies on Contentiousness in Abortion, Climate Change, and Gun Control
As public discourse continues to move and grow online, conversations about divisive topics on social media platforms have also increased.
ToxCCIn: Toxic Content Classification with Interpretability
Despite the recent successes of transformer-based models in terms of effectiveness on a variety of tasks, their decisions often remain opaque to humans.
GUIR at SemEval-2020 Task 12: Domain-Tuned Contextualized Models for Offensive Language Detection
Offensive language detection is an important and challenging task in natural language processing.
