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Found 10 papers, 7 papers with code
Q^{2}: Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering
Neural knowledge-grounded generative models for dialogue often produce content that is factually inconsistent with the knowledge they rely on, making them unreliable and limiting their applicability.
Abstractive Text Summarization
Natural Language Inference
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A Chain-of-Thought Is as Strong as Its Weakest Link: A Benchmark for Verifiers of Reasoning Chains
REVEAL includes comprehensive labels for the relevance, attribution to evidence passages, and logical correctness of each reasoning step in a language model's answer, across a variety of datasets and state-of-the-art language models.
Surfacing Biases in Large Language Models using Contrastive Input Decoding
We use CID to highlight context-specific biases that are hard to detect with standard decoding strategies and quantify the effect of different input perturbations.
Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor
We collect 64, 000 examples by prompting a language model with three seed examples of instructions and eliciting a fourth.
DisentQA: Disentangling Parametric and Contextual Knowledge with Counterfactual Question Answering
Question answering models commonly have access to two sources of "knowledge" during inference time: (1) parametric knowledge - the factual knowledge encoded in the model weights, and (2) contextual knowledge - external knowledge (e. g., a Wikipedia passage) given to the model to generate a grounded answer.
LMentry: A Language Model Benchmark of Elementary Language Tasks
As the performance of large language models rapidly improves, benchmarks are getting larger and more complex as well.
Instruction Induction: From Few Examples to Natural Language Task Descriptions
Large language models are able to perform a task by conditioning on a few input-output demonstrations - a paradigm known as in-context learning.
TRUE: Re-evaluating Factual Consistency Evaluation
Grounded text generation systems often generate text that contains factual inconsistencies, hindering their real-world applicability.
$Q^{2}$: Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering
Neural knowledge-grounded generative models for dialogue often produce content that is factually inconsistent with the knowledge they rely on, making them unreliable and limiting their applicability.
Machine Reading of Historical Events
Machine reading is an ambitious goal in NLP that subsumes a wide range of text understanding capabilities.
