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Found 23 papers, 10 papers with code
DelucionQA: Detecting Hallucinations in Domain-specific Question Answering
Hallucination is a well-known phenomenon in text generated by large language models (LLMs).
Learning to Filter Context for Retrieval-Augmented Generation
To alleviate these problems, we propose FILCO, a method that improves the quality of the context provided to the generator by (1) identifying useful context based on lexical and information-theoretic approaches, and (2) training context filtering models that can filter retrieved contexts at test time.
Knowledge-grounded Natural Language Recommendation Explanation
Explanations accompanied by a recommendation can assist users in understanding the decision made by recommendation systems, which in turn increases a user's confidence and trust in the system.
SwitchPrompt: Learning Domain-Specific Gated Soft Prompts for Classification in Low-Resource Domains
Prompting pre-trained language models leads to promising results across natural language processing tasks but is less effective when applied in low-resource domains, due to the domain gap between the pre-training data and the downstream task.
Retrieval as Attention: End-to-end Learning of Retrieval and Reading within a Single Transformer
Systems for knowledge-intensive tasks such as open-domain question answering (QA) usually consist of two stages: efficient retrieval of relevant documents from a large corpus and detailed reading of the selected documents to generate answers.
Ranked #1 on
Passage Retrieval
on Natural Questions
Understanding and Improving Zero-shot Multi-hop Reasoning in Generative Question Answering
In sum, these results demonstrate that multi-hop reasoning does not emerge naturally in generative QA models, but can be encouraged by advances in training or modeling techniques.
Explicitly Capturing Relations between Entity Mentions via Graph Neural Networks for Domain-specific Named Entity Recognition
Named entity recognition (NER) is well studied for the general domain, and recent systems have achieved human-level performance for identifying common entity types.
How Can We Know When Language Models Know? On the Calibration of Language Models for Question Answering
We examine this question from the point of view of calibration, the property of a probabilistic model's predicted probabilities actually being well correlated with the probabilities of correctness.
X-FACTR: Multilingual Factual Knowledge Retrieval from Pretrained Language Models
We further propose a code-switching-based method to improve the ability of multilingual LMs to access knowledge, and verify its effectiveness on several benchmark languages.
Learning Relation Entailment with Structured and Textual Information
We propose several methods that incorporate both structured and textual information to represent relations for this task.
How Can We Know What Language Models Know?
Recent work has presented intriguing results examining the knowledge contained in language models (LM) by having the LM fill in the blanks of prompts such as "Obama is a _ by profession".
Generalizing Natural Language Analysis through Span-relation Representations
Natural language processing covers a wide variety of tasks predicting syntax, semantics, and information content, and usually each type of output is generated with specially designed architectures.
Ranked #1 on
Relation Extraction
on WLPC
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+8
Open-Domain Event Detection using Distant Supervision
This paper introduces open-domain event detection, a new event detection paradigm to address issues of prior work on restricted domains and event annotation.
Generating Questions and Multiple-Choice Answers using Semantic Analysis of Texts
We present a novel approach to automated question generation that improves upon prior work both from a technology perspective and from an assessment perspective.
Detecting Subevent Structure for Event Coreference Resolution
First, we introduce a multiclass logistic regression model that can detect subevent relations in addition to full coreference.
Supervised Within-Document Event Coreference using Information Propagation
Event coreference is an important task for full text analysis.
