Papers with knowledge quality
CIKQA: Learning Commonsense Inference with a Unified Knowledge-in-the-loop QA Paradigm (2023.findings-eacl)
Copied to clipboard
| Challenge: | Existing commonsense reasoning datasets target different knowledge types, modalities, and formats, but how to help machines acquire and infer over commonsensical knowledge is still unclear. |
| Approach: | They propose a commonsense reasoning benchmark to motivate commonsensing progress from two perspectives: (1) Evaluating whether models can distinguish knowledge quality by predicting if the knowledge is enough to answer the question or not. |
| Outcome: | The proposed model outperforms existing models in evaluating their generalization capabilities across tasks while demonstrating that distinguishing knowledge quality remains challenging for current models. |
Knowledge-centered conversational agents with a drive to learn (2024.naacl-srw)
Copied to clipboard
| Challenge: | Unlike traditional task-oriented dialogue agents, knowledgeable agents can autonomously determine what they know and do not know, what is the epistemic status of what they do not understand, and what they need to learn. |
| Approach: | They propose an adaptive conversational agent that assesses the quality of its knowledge and is driven to become more knowledgeable. |
| Outcome: | The proposed agent can learn effective policies to acquire the knowledge needed by assessing the efficiency of these capabilities during interaction. |
Approximation of Response Knowledge Retrieval in Knowledge-grounded Dialogue Generation (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Recent studies have focused on improving dialogue generation models that include knowledge related to the posts. |
| Approach: | They propose to use a novel method to generate responses from posts and related knowledge by injecting knowledge into dialogue generation models. |
| Outcome: | The proposed method outperforms baseline models in terms of knowledge relevance and quality. |
Knowledge-Infused Multi-Bit Watermarking for RAG Knowledge Bases (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing RAG watermarking methods are limited in their encoding capacity and potential degradation of performance or knowledge quality. |
| Approach: | They propose knowledge-infused and multi-bit watermarking (KMW) for RAG knowledge bases by benign knowledge completion and a tailored generative watermark algorithm. |
| Outcome: | The proposed method extracts watermarks from adversarial RAGs while remaining stealthy and secure. |