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Found 12 papers, 5 papers with code
Interrogating LLM design under a fair learning doctrine
The current discourse on large language models (LLMs) and copyright largely takes a "behavioral" perspective, focusing on model outputs and evaluating whether they are substantially similar to training data.
Verify with Caution: The Pitfalls of Relying on Imperfect Factuality Metrics
Improvements in large language models have led to increasing optimism that they can serve as reliable evaluators of natural language generation outputs.
Analysis of Plan-based Retrieval for Grounded Text Generation
In text generation, hallucinations refer to the generation of seemingly coherent text that contradicts established knowledge.
SCENE: Self-Labeled Counterfactuals for Extrapolating to Negative Examples
In this work, we propose Self-labeled Counterfactuals for Extrapolating to Negative Examples (SCENE), an automatic method for synthesizing training data that greatly improves models' ability to detect challenging negative examples.
Benchmarking Long-tail Generalization with Likelihood Splits
In order to reliably process natural language, NLP systems must generalize to the long tail of rare utterances.
Knowledge Base Question Answering by Case-based Reasoning over Subgraphs
Question answering (QA) over knowledge bases (KBs) is challenging because of the diverse, essentially unbounded, types of reasoning patterns needed.
Case-based Reasoning for Natural Language Queries over Knowledge Bases
It is often challenging to solve a complex problem from scratch, but much easier if we can access other similar problems with their solutions -- a paradigm known as case-based reasoning (CBR).
Knowledge Base Question Answering
Natural Language Queries
+1
Probabilistic Case-based Reasoning for Open-World Knowledge Graph Completion
A case-based reasoning (CBR) system solves a new problem by retrieving `cases' that are similar to the given problem.
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on NELL-995
A Simple Approach to Case-Based Reasoning in Knowledge Bases
We present a surprisingly simple yet accurate approach to reasoning in knowledge graphs (KGs) that requires \emph{no training}, and is reminiscent of case-based reasoning in classical artificial intelligence (AI).
Chains-of-Reasoning at TextGraphs 2019 Shared Task: Reasoning over Chains of Facts for Explainable Multi-hop Inference
This paper describes our submission to the shared task on {``}Multi-hop Inference Explanation Regeneration{''} in TextGraphs workshop at EMNLP 2019 (Jansen and Ustalov, 2019).
Multi-step Entity-centric Information Retrieval for Multi-Hop Question Answering
Multi-hop question answering (QA) requires an information retrieval (IR) system that can find \emph{multiple} supporting evidence needed to answer the question, making the retrieval process very challenging.
Siamese Neural Networks with Random Forest for detecting duplicate question pairs
We got the best result by using the Siamese adaptation of a Bidirectional GRU with a Random Forest classifier, which landed us among the top 24% in the competition Quora Question Pairs hosted on Kaggle.
