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Found 7 papers, 1 papers with code
EcoRank: Budget-Constrained Text Re-ranking Using Large Language Models
We study how to maximize the re-ranking performance given a budget, by navigating the vast search spaces of prompt choices, LLM APIs, and budget splits.
PAT-Questions: A Self-Updating Benchmark for Present-Anchored Temporal Question-Answering
Existing work on Temporal Question Answering (TQA) has predominantly focused on questions anchored to specific timestamps or events (e. g. "Who was the US president in 1970?").
NORMY: Non-Uniform History Modeling for Open Retrieval Conversational Question Answering
A typical OrConvQA pipeline consists of three modules: a Retriever to retrieve relevant documents from the collection, a Reranker to rerank them given the question and the context, and a Reader to extract an answer span.
Generalized Zero-shot Intent Detection via Commonsense Knowledge
Thus, these models should seamlessly adapt and classify utterances with both seen and unseen intents -- unseen intents emerge after deployment and they do not have training data.
Linguistically-Enriched and Context-Aware Zero-shot Slot Filling
Thus, it is imperative that these models seamlessly adapt and fill slots from both seen and unseen domains -- unseen domains contain unseen slot types with no training data, and even seen slots in unseen domains are typically presented in different contexts.
App-Aware Response Synthesis for User Reviews
Solving the challenge by simply building a model per app (i. e., training with review-response pairs of a single app) may be insufficient because individual apps have limited review-response pairs, and such pairs typically lack the relevant information needed to respond to a new review.
Unsupervised Paraphrasing via Deep Reinforcement Learning
Our evaluation also shows that PUP achieves a great trade-off between semantic similarity and diversity of expression.
