Retrieving multimodal information for augmented generation: A survey
As Large Language Models (LLMs) become popular, there emerged an important trend of
using multimodality to augment the LLMs' generation ability, which enables LLMs to better …
using multimodality to augment the LLMs' generation ability, which enables LLMs to better …
WorldTree v2: A corpus of science-domain structured explanations and inference patterns supporting multi-hop inference
Z Xie, S Thiem, J Martin, E Wainwright… - Proceedings of the …, 2020 - aclanthology.org
Explainable question answering for complex questions often requires combining large
numbers of facts to answer a question while providing a human-readable explanation for the …
numbers of facts to answer a question while providing a human-readable explanation for the …
ExplaGraphs: An explanation graph generation task for structured commonsense reasoning
Recent commonsense-reasoning tasks are typically discriminative in nature, where a model
answers a multiple-choice question for a certain context. Discriminative tasks are limiting …
answers a multiple-choice question for a certain context. Discriminative tasks are limiting …
Unsupervised alignment-based iterative evidence retrieval for multi-hop question answering
Evidence retrieval is a critical stage of question answering (QA), necessary not only to
improve performance, but also to explain the decisions of the corresponding QA method. We …
improve performance, but also to explain the decisions of the corresponding QA method. We …
Metgen: A module-based entailment tree generation framework for answer explanation
Knowing the reasoning chains from knowledge to the predicted answers can help construct
an explainable question answering (QA) system. Advances on QA explanation propose to …
an explainable question answering (QA) system. Advances on QA explanation propose to …
A survey on explainability in machine reading comprehension
This paper presents a systematic review of benchmarks and approaches for explainability in
Machine Reading Comprehension (MRC). We present how the representation and …
Machine Reading Comprehension (MRC). We present how the representation and …
multiPRover: Generating multiple proofs for improved interpretability in rule reasoning
We focus on a type of linguistic formal reasoning where the goal is to reason over explicit
knowledge in the form of natural language facts and rules (Clark et al., 2020). A recent work …
knowledge in the form of natural language facts and rules (Clark et al., 2020). A recent work …
Hybrid autoregressive inference for scalable multi-hop explanation regeneration
Regenerating natural language explanations in the scientific domain has been proposed as
a benchmark to evaluate complex multi-hop and explainable inference. In this context, large …
a benchmark to evaluate complex multi-hop and explainable inference. In this context, large …
NLI4CT: Multi-evidence natural language inference for clinical trial reports
M Jullien, M Valentino, H Frost, P O'Regan… - arXiv preprint arXiv …, 2023 - arxiv.org
How can we interpret and retrieve medical evidence to support clinical decisions? Clinical
trial reports (CTR) amassed over the years contain indispensable information for the …
trial reports (CTR) amassed over the years contain indispensable information for the …
Unification-based reconstruction of multi-hop explanations for science questions
This paper presents a novel framework for reconstructing multi-hop explanations in science
Question Answering (QA). While existing approaches for multi-hop reasoning build …
Question Answering (QA). While existing approaches for multi-hop reasoning build …