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Found 20 papers, 13 papers with code
“A Little Birdie Told Me ... ” - Inductive Biases for Rumour Stance Detection on Social Media
The rise in the usage of social media has placed it in a central position for news dissemination and consumption.
Investigating the Effect of Background Knowledge on Natural Questions
We create a subset of the NQ data, Factual Questions (FQ), where the questions have evidence in the KB in the form of paths that link question entities to answer entities but still must be answered using text, to facilitate further research into KB integration methods.
Don't Hallucinate, Abstain: Identifying LLM Knowledge Gaps via Multi-LLM Collaboration
Despite efforts to expand the knowledge of large language models (LLMs), knowledge gaps -- missing or outdated information in LLMs -- might always persist given the evolving nature of knowledge.
Fine-grained Hallucination Detection and Editing for Language Models
On our benchmark, our automatic and human evaluations show that FAVA significantly outperforms ChatGPT and GPT-4 on fine-grained hallucination detection, and edits suggested by FAVA improve the factuality of LM-generated text.
P^3SUM: Preserving Author's Perspective in News Summarization with Diffusion Language Models
In this work, we take a first step towards designing summarization systems that are faithful to the author's intent, not only the semantic content of the article.
KGQuiz: Evaluating the Generalization of Encoded Knowledge in Large Language Models
To gain a better understanding of LLMs' knowledge abilities and their generalization, we evaluate 10 open-source and black-box LLMs on the KGQuiz benchmark across the five knowledge-intensive tasks and knowledge domains.
Resolving Knowledge Conflicts in Large Language Models
To this end, we introduce KNOWLEDGE CONFLICT, an evaluation framework for simulating contextual knowledge conflicts and quantitatively evaluating to what extent LLMs achieve these goals.
Knowledge Crosswords: Geometric Reasoning over Structured Knowledge with Large Language Models
We additionally propose two new approaches, Staged Prompting and Verify-All, to augment LLMs' ability to backtrack and verify structured constraints.
Knowledge Card: Filling LLMs' Knowledge Gaps with Plug-in Specialized Language Models
Ultimately, Knowledge Card framework enables dynamic synthesis and updates of knowledge from diverse domains.
FactKB: Generalizable Factuality Evaluation using Language Models Enhanced with Factual Knowledge
We propose FactKB, a simple new approach to factuality evaluation that is generalizable across domains, in particular with respect to entities and relations.
Assessing Language Model Deployment with Risk Cards
However, there is no risk-centric framework for documenting the complexity of a landscape in which some risks are shared across models and contexts, while others are specific, and where certain conditions may be required for risks to manifest as harms.
Correcting Diverse Factual Errors in Abstractive Summarization via Post-Editing and Language Model Infilling
Abstractive summarization models often generate inconsistent summaries containing factual errors or hallucinated content.
Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey
Recent advances in the capacity of large language models to generate human-like text have resulted in their increased adoption in user-facing settings.
Unsupervised Keyphrase Extraction via Interpretable Neural Networks
Keyphrase extraction aims at automatically extracting a list of "important" phrases representing the key concepts in a document.
DialoGraph: Incorporating Interpretable Strategy-Graph Networks into Negotiation Dialogues
To successfully negotiate a deal, it is not enough to communicate fluently: pragmatic planning of persuasive negotiation strategies is essential.
Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics
Modern summarization models generate highly fluent but often factually unreliable outputs.
Simple and Efficient ways to Improve REALM
Dense retrieval has been shown to be effective for retrieving relevant documents for Open Domain QA, surpassing popular sparse retrieval methods like BM25.
SelfExplain: A Self-Explaining Architecture for Neural Text Classifiers
We introduce SelfExplain, a novel self-explaining model that explains a text classifier's predictions using phrase-based concepts.
StructSum: Summarization via Structured Representations
To this end, we propose incorporating latent and explicit dependencies across sentences in the source document into end-to-end single-document summarization models.
Differentiable Reasoning over a Virtual Knowledge Base
In particular, we describe a neural module, DrKIT, that traverses textual data like a KB, softly following paths of relations between mentions of entities in the corpus.
