565 citations · 2.3k across the 68 of their papers we have counts for
8 papers · 2 filters
Leveraging Code to Improve In-context Learning for Semantic Parsing
Ben Bogin, Shivanshu Gupta, Peter Clark +1
In-context learning (ICL) is an appealing approach for semantic parsing due to its few-shot nature and improved generalization. However, learning to parse to rare domain-specific l…
Bias Runs Deep: Implicit Reasoning Biases in Persona-Assigned LLMs
Shashank Gupta, Vaishnavi Shrivastava, Ameet Deshpande +4
Recent works have showcased the ability of LLMs to embody diverse personas in their responses, exemplified by prompts like 'You are Yoda. Explain the Theory of Relativity.' While t…
Closing the Curious Case of Neural Text Degeneration
Matthew Finlayson, John Hewitt, Alexander Koller +2
Despite their ubiquity in language generation, it remains unknown why truncation sampling heuristics like nucleus sampling are so effective. We provide a theoretical explanation fo…
IfQA: A Dataset for Open-domain Question Answering under Counterfactual Presuppositions
Wenhao Yu, Meng Jiang, Peter Clark +1
Although counterfactual reasoning is a fundamental aspect of intelligence, the lack of large-scale counterfactual open-domain question-answering (QA) benchmarks makes it difficult…
Improving Language Models via Plug-and-Play Retrieval Feedback
Wenhao Yu, Zhihan Zhang, Zhenwen Liang +2
Large language models (LLMs) exhibit remarkable performance across various NLP tasks. However, they often generate incorrect or hallucinated information, which hinders their practi…
Increasing Probability Mass on Answer Choices Does Not Always Improve Accuracy
Sarah Wiegreffe, Matthew Finlayson, Oyvind Tafjord +2
When pretrained language models (LMs) are applied to discriminative tasks such as multiple-choice questions, they place probability mass on vocabulary tokens that aren't among the…