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Found 22 papers, 11 papers with code
Modal Sense Classification At Large: Paraphrase-Driven Sense Projection, Semantically Enriched Classification Models and Cross-Genre Evaluations
Modal verbs have different interpretations depending on their context.
Chain-of-Thought Unfaithfulness as Disguised Accuracy
Understanding the extent to which Chain-of-Thought (CoT) generations align with a large language model's (LLM) internal computations is critical for deciding whether to trust an LLM's output.
Whispers of Doubt Amidst Echoes of Triumph in NLP Robustness
Do larger and more performant models resolve NLP's longstanding robustness issues?
How Much Consistency Is Your Accuracy Worth?
Contrast set consistency is a robustness measurement that evaluates the rate at which a model correctly responds to all instances in a bundle of minimally different examples relying on the same knowledge.
CONDAQA: A Contrastive Reading Comprehension Dataset for Reasoning about Negation
We also have workers make three kinds of edits to the passage -- paraphrasing the negated statement, changing the scope of the negation, and reversing the negation -- resulting in clusters of question-answer pairs that are difficult for models to answer with spurious shortcuts.
Does Self-Rationalization Improve Robustness to Spurious Correlations?
Specifically, we evaluate how training self-rationalization models with free-text rationales affects robustness to spurious correlations in fine-tuned encoder-decoder and decoder-only models of six different sizes.
Do Androids Laugh at Electric Sheep? Humor "Understanding" Benchmarks from The New Yorker Caption Contest
Large neural networks can now generate jokes, but do they really "understand" humor?
On Advances in Text Generation from Images Beyond Captioning: A Case Study in Self-Rationalization
Combining the visual modality with pretrained language models has been surprisingly effective for simple descriptive tasks such as image captioning.
Few-Shot Self-Rationalization with Natural Language Prompts
We identify the right prompting approach by extensively exploring natural language prompts on FEB. Then, by using this prompt and scaling the model size, we demonstrate that making progress on few-shot self-rationalization is possible.
Effective Attention Sheds Light On Interpretability
An attention matrix of a transformer self-attention sublayer can provably be decomposed into two components and only one of them (effective attention) contributes to the model output.
Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus
Finally, we conclude with some recommendations for how to created and document web-scale datasets from a scrape of the internet.
Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processing
Explainable NLP (ExNLP) has increasingly focused on collecting human-annotated textual explanations.
Promoting Graph Awareness in Linearized Graph-to-Text Generation
Generating text from structured inputs, such as meaning representations or RDF triples, has often involved the use of specialized graph-encoding neural networks.
Explaining NLP Models via Minimal Contrastive Editing (MiCE)
Humans have been shown to give contrastive explanations, which explain why an observed event happened rather than some other counterfactual event (the contrast case).
Measuring Association Between Labels and Free-Text Rationales
In interpretable NLP, we require faithful rationales that reflect the model's decision-making process for an explained instance.
Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI
We discuss a model of trust inspired by, but not identical to, sociology's interpersonal trust (i. e., trust between people).
Natural Language Rationales with Full-Stack Visual Reasoning: From Pixels to Semantic Frames to Commonsense Graphs
Natural language rationales could provide intuitive, higher-level explanations that are easily understandable by humans, complementing the more broadly studied lower-level explanations based on gradients or attention weights.
Don't Stop Pretraining: Adapt Language Models to Domains and Tasks
Language models pretrained on text from a wide variety of sources form the foundation of today's NLP.
Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning
Machine comprehension of texts longer than a single sentence often requires coreference resolution.
SRL4ORL: Improving Opinion Role Labeling using Multi-task Learning with Semantic Role Labeling
For over a decade, machine learning has been used to extract opinion-holder-target structures from text to answer the question "Who expressed what kind of sentiment towards what?".
Ranked #2 on
Fine-Grained Opinion Analysis
on MPQA
(using extra training data)
A Mention-Ranking Model for Abstract Anaphora Resolution
We found model variants that outperform the baselines for nominal anaphors, without training on individual anaphor data, but still lag behind for pronominal anaphors.
Ranked #1 on
Abstract Anaphora Resolution
on The ARRAU Corpus
Multilingual Modal Sense Classification using a Convolutional Neural Network
Modal sense classification (MSC) is a special WSD task that depends on the meaning of the proposition in the modal's scope.
