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Found 10 papers, 3 papers with code
MedHalu: Hallucinations in Responses to Healthcare Queries by Large Language Models
In this work, we conduct a pioneering study of hallucinations in LLM-generated responses to real-world healthcare queries from patients.
CodeMirage: Hallucinations in Code Generated by Large Language Models
We propose the first benchmark CodeMirage dataset for code hallucinations.
Decentralised Moderation for Interoperable Social Networks: A Conversation-based Approach for Pleroma and the Fediverse
Recent work has exploited the conversational context of a post to improve this automatic tagging, e. g. using the replies to a post to help classify if it contains toxic speech.
"Which LLM should I use?": Evaluating LLMs for tasks performed by Undergraduate Computer Science Students
This study evaluates the effectiveness of various large language models (LLMs) in performing tasks common among undergraduate computer science students.
GASCOM: Graph-based Attentive Semantic Context Modeling for Online Conversation Understanding
Specifically, we design two novel algorithms that utilise both the graph structure of the online conversation as well as the semantic information from individual posts for retrieving relevant context nodes from the whole conversation.
HateRephrase: Zero- and Few-Shot Reduction of Hate Intensity in Online Posts using Large Language Models
We develop 4 different prompts based on task description, hate definition, few-shot demonstrations and chain-of-thoughts for comprehensive experiments and conduct experiments on open-source LLMs such as LLaMA-1, LLaMA-2 chat, Vicuna as well as OpenAI's GPT-3. 5.
AI in the Gray: Exploring Moderation Policies in Dialogic Large Language Models vs. Human Answers in Controversial Topics
The introduction of ChatGPT and the subsequent improvement of Large Language Models (LLMs) have prompted more and more individuals to turn to the use of ChatBots, both for information and assistance with decision-making.
AnnoBERT: Effectively Representing Multiple Annotators' Label Choices to Improve Hate Speech Detection
During training, the model associates annotators with their label choices given a piece of text; during evaluation, when label information is not available, the model predicts the aggregated label given by the participating annotators by utilising the learnt association.
A Graph-Based Context-Aware Model to Understand Online Conversations
We evaluate GraphNLI on two such tasks - polarity prediction and misogynistic hate speech detection - and found that our model consistently outperforms all relevant baselines for both tasks.
GraphNLI: A Graph-based Natural Language Inference Model for Polarity Prediction in Online Debates
We then use these embeddings to predict the polarity relation between a reply and the post it is replying to.
