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Found 16 papers, 6 papers with code
Can NLI Models Verify QA Systems’ Predictions?
To build robust question answering systems, we need the ability to verify whether answers to questions are truly correct, not just “good enough” in the context of imperfect QA datasets.
longhorns at DADC 2022: How many linguists does it take to fool a Question Answering model? A systematic approach to adversarial attacks.
Developing methods to adversarially challenge NLP systems is a promising avenue for improving both model performance and interpretability.
Contemporary NLP Modeling in Six Comprehensive Programming Assignments
We present a series of programming assignments, adaptable to a range of experience levels from advanced undergraduate to PhD, to teach students design and implementation of modern NLP systems.
Dancing in Chains: Reconciling Instruction Following and Faithfulness in Language Models
Surprisingly, we find that less is more, as training ReSet with high-quality, yet substantially smaller data (three-fold less) yields superior results.
Using Natural Language Explanations to Rescale Human Judgments
These scores should reflect the annotators' underlying assessments of the example.
Complex Claim Verification with Evidence Retrieved in the Wild
Evidence retrieval is a core part of automatic fact-checking.
Improving Cross-task Generalization of Unified Table-to-text Models with Compositional Task Configurations
There has been great progress in unifying various table-to-text tasks using a single encoder-decoder model trained via multi-task learning (Xie et al., 2022).
longhorns at DADC 2022: How many linguists does it take to fool a Question Answering model? A systematic approach to adversarial attacks
Developing methods to adversarially challenge NLP systems is a promising avenue for improving both model performance and interpretability.
Generating Literal and Implied Subquestions to Fact-check Complex Claims
Verifying complex political claims is a challenging task, especially when politicians use various tactics to subtly misrepresent the facts.
Can NLI Models Verify QA Systems' Predictions?
To build robust question answering systems, we need the ability to verify whether answers to questions are truly correct, not just "good enough" in the context of imperfect QA datasets.
Robust Question Answering Through Sub-part Alignment
Current textual question answering models achieve strong performance on in-domain test sets, but often do so by fitting surface-level patterns in the data, so they fail to generalize to out-of-distribution settings.
Multi-hop Question Answering via Reasoning Chains
Our analysis shows the properties of chains that are crucial for high performance: in particular, modeling extraction sequentially is important, as is dealing with each candidate sentence in a context-aware way.
Ranked #4 on
Question Answering
on WikiHop
Understanding Dataset Design Choices for Multi-hop Reasoning
First, we explore sentence-factored models for these tasks; by design, these models cannot do multi-hop reasoning, but they are still able to solve a large number of examples in both datasets.
Learning Word Embeddings from Intrinsic and Extrinsic Views
To prove the effectiveness of our model, we evaluate it on four tasks, including word similarity, reverse dictionaries, Wiki link prediction, and document classification.
