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Found 7 papers, 0 papers with code
A Formalism and Approach for Improving Robustness of Large Language Models Using Risk-Adjusted Confidence Scores
We also propose a risk-centric evaluation framework, and four novel metrics, for assessing LLMs on these risks in both in-domain and out-of-domain settings.
Understanding Prior Bias and Choice Paralysis in Transformer-based Language Representation Models through Four Experimental Probes
Recent work on transformer-based neural networks has led to impressive advances on multiple-choice natural language understanding (NLU) problems, such as Question Answering (QA) and abductive reasoning.
Understanding Substructures in Commonsense Relations in ConceptNet
A potential source of structured commonsense knowledge that could be used to derive insights is ConceptNet.
A Theoretically Grounded Benchmark for Evaluating Machine Commonsense
Preliminary results suggest that the benchmark is challenging even for advanced language representation models designed for discriminative CSR question answering tasks.
A Data-Driven Study of Commonsense Knowledge using the ConceptNet Knowledge Base
Acquiring commonsense knowledge and reasoning is recognized as an important frontier in achieving general Artificial Intelligence (AI).
Do Fine-tuned Commonsense Language Models Really Generalize?
According to influential leaderboards hosted by the Allen Institute (evaluating state-of-the-art performance on commonsense reasoning benchmarks), models based on such transformer methods are approaching human-like performance and have average accuracy well over 80% on many benchmarks.
Ranking sentences from product description & bullets for better search
Products in an ecommerce catalog contain information-rich fields like description and bullets that can be useful to extract entities (attributes) using NER based systems.
