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Found 6 papers, 1 papers with code
Learning to Imagine: Integrating Counterfactual Thinking in Neural Discrete Reasoning
Neural discrete reasoning (NDR) has shown remarkable progress in combining deep models with discrete reasoning.
Think Twice Before Assure: Confidence Estimation for Large Language Models through Reflection on Multiple Answers
Confidence estimation aiming to evaluate output trustability is crucial for the application of large language models (LLM), especially the black-box ones.
Gotcha! Don't trick me with unanswerable questions! Self-aligning Large Language Models for Responding to Unknown Questions
Despite the remarkable abilities of Large Language Models (LLMs) to answer questions, they often display a considerable level of overconfidence even when the question does not have a definitive answer.
TAT-LLM: A Specialized Language Model for Discrete Reasoning over Tabular and Textual Data
In this work, we address question answering (QA) over a hybrid of tabular and textual data that are very common content on the Web (e. g. SEC filings), where discrete reasoning capabilities are often required.
Robust Prompt Optimization for Large Language Models Against Distribution Shifts
In this light, we propose a new problem of robust prompt optimization for LLMs against distribution shifts, which requires the prompt optimized over the labeled source group can simultaneously generalize to an unlabeled target group.
Doc2SoarGraph: Discrete Reasoning over Visually-Rich Table-Text Documents via Semantic-Oriented Hierarchical Graphs
Discrete reasoning over table-text documents (e. g., financial reports) gains increasing attention in recent two years.
