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Found 10 papers, 7 papers with code
ReIFE: Re-evaluating Instruction-Following Evaluation
The automatic evaluation of instruction following typically involves using large language models (LLMs) to assess response quality.
Large Language Models as Misleading Assistants in Conversation
Large Language Models (LLMs) are able to provide assistance on a wide range of information-seeking tasks.
SciRIFF: A Resource to Enhance Language Model Instruction-Following over Scientific Literature
We present SciRIFF (Scientific Resource for Instruction-Following and Finetuning), a dataset of 137K instruction-following demonstrations for 54 tasks covering five essential scientific literature understanding capabilities: information extraction, summarization, question answering, claim verification, and classification.
Medical Text Simplification: Optimizing for Readability with Unlikelihood Training and Reranked Beam Search Decoding
Text simplification has emerged as an increasingly useful application of AI for bridging the communication gap in specialized fields such as medicine, where the lexicon is often dominated by technical jargon and complex constructs.
ODSum: New Benchmarks for Open Domain Multi-Document Summarization
Open-domain Multi-Document Summarization (ODMDS) is a critical tool for condensing vast arrays of documents into coherent, concise summaries.
On Learning to Summarize with Large Language Models as References
Recent studies have found that summaries generated by large language models (LLMs) are favored by human annotators over the original reference summaries in commonly used summarization datasets.
Pretraining Language Models with Human Preferences
Language models (LMs) are pretrained to imitate internet text, including content that would violate human preferences if generated by an LM: falsehoods, offensive comments, personally identifiable information, low-quality or buggy code, and more.
Automatic Error Analysis for Document-level Information Extraction
Document-level information extraction (IE) tasks have recently begun to be revisited in earnest using the end-to-end neural network techniques that have been successful on their sentence-level IE counterparts.
V2I Connectivity-Based Dynamic Queue-Jump Lane for Emergency Vehicles: A Deep Reinforcement Learning Approach
Emergency vehicle (EMV) service is a key function of cities and is exceedingly challenging due to urban traffic congestion.
Dynamic Queue-Jump Lane for Emergency Vehicles under Partially Connected Settings: A Multi-Agent Deep Reinforcement Learning Approach
Based on pairs of neural networks representing actors and critics for agent vehicles, we develop a multi-agent actor-critic deep reinforcement learning algorithm that handles a varying number of vehicles and a random proportion of connected vehicles in the traffic.
