Asking clarification questions to handle ambiguity in open-domain qa

[PDF] arxiv.org

Tree of clarifications: Answering ambiguous questions with retrieval-augmented large language models

G Kim, S Kim, B Jeon, J Park, J Kang - arXiv preprint arXiv:2310.14696, 2023 - arxiv.org
Questions in open-domain question answering are often ambiguous, allowing multiple
interpretations. One approach to handling them is to identify all possible interpretations of …
被引用次数：39 相关文章 所有 6 个版本
[PDF] arxiv.org

A survey of conversational search

F Mo, K Mao, Z Zhao, H Qian, H Chen, Y Cheng… - arXiv preprint arXiv …, 2024 - arxiv.org
As a cornerstone of modern information access, search engines have become
indispensable in everyday life. With the rapid advancements in AI and natural language …
被引用次数：7 相关文章 所有 2 个版本
[PDF] arxiv.org

Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning Perspective

Z Zeng, Q Cheng, Z Yin, B Wang, S Li, Y Zhou… - arXiv preprint arXiv …, 2024 - arxiv.org
OpenAI o1 represents a significant milestone in Artificial Inteiligence, which achieves expert-
level performances on many challanging tasks that require strong reasoning ability. OpenAI …
被引用次数：2 相关文章 所有 2 个版本
[PDF] arxiv.org

Enhancing Retrieval and Managing Retrieval: A Four-Module Synergy for Improved Quality and Efficiency in RAG Systems

Y Shi, X Zi, Z Shi, H Zhang, Q Wu, M Xu - arXiv preprint arXiv:2407.10670, 2024 - arxiv.org
Retrieval-augmented generation (RAG) techniques leverage the in-context learning
capabilities of large language models (LLMs) to produce more accurate and relevant …
被引用次数：4 相关文章 所有 3 个版本
[PDF] aclanthology.org

QPaug: Question and Passage Augmentation for Open-Domain Question Answering of LLMs

M Kim, C Park, S Baek - Findings of the Association for …, 2024 - aclanthology.org
Retrieval-augmented generation (RAG) has received much attention for Open-domain
question-answering (ODQA) tasks as a means to compensate for the parametric knowledge …
被引用次数：1 相关文章
[PDF] arxiv.org

AmbigDocs: Reasoning across Documents on Different Entities under the Same Name

Y Lee, X Ye, E Choi - arXiv preprint arXiv:2404.12447, 2024 - arxiv.org
Different entities with the same name can be difficult to distinguish. Handling confusing entity
mentions is a crucial skill for language models (LMs). For example, given the question" …
相关文章 所有 2 个版本
[PDF] arxiv.org

To Know or Not To Know? Analyzing Self-Consistency of Large Language Models under Ambiguity

A Sedova, R Litschko, D Frassinelli, B Roth… - arXiv preprint arXiv …, 2024 - arxiv.org
One of the major aspects contributing to the striking performance of large language models
(LLMs) is the vast amount of factual knowledge accumulated during pre-training. Yet, many …
相关文章 所有 3 个版本