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Found 8 papers, 6 papers with code
Ask Optimal Questions: Aligning Large Language Models with Retriever's Preference in Conversational Search
Furthermore, we fine-tune a smaller LM using this dataset to align it with the retrievers' preferences as feedback.
Tree of Clarifications: Answering Ambiguous Questions with Retrieval-Augmented Large Language Models
To cope with the challenge, we propose a novel framework, Tree of Clarifications (ToC): It recursively constructs a tree of disambiguations for the AQ -- via few-shot prompting leveraging external knowledge -- and uses it to generate a long-form answer.
KU-DMIS-MSRA at RadSum23: Pre-trained Vision-Language Model for Radiology Report Summarization
In this paper, we introduce CheXOFA, a new pre-trained vision-language model (VLM) for the chest X-ray domain.
Generating Information-Seeking Conversations from Unlabeled Documents
In this paper, we introduce a novel framework, SIMSEEK, (Simulating information-Seeking conversation from unlabeled documents), and compare its two variants.
Saving Dense Retriever from Shortcut Dependency in Conversational Search
In this paper, we demonstrate the existence of a retrieval shortcut in CS, which causes models to retrieve passages solely relying on partial history while disregarding the latest question.
Learn to Resolve Conversational Dependency: A Consistency Training Framework for Conversational Question Answering
One of the main challenges in conversational question answering (CQA) is to resolve the conversational dependency, such as anaphora and ellipsis.
Transferability of Natural Language Inference to Biomedical Question Answering
We observe that BioBERT trained on the NLI dataset obtains better performance on Yes/No (+5. 59%), Factoid (+0. 53%), List type (+13. 58%) questions compared to performance obtained in a previous challenge (BioASQ 7B Phase B).
Look at the First Sentence: Position Bias in Question Answering
In this study, we hypothesize that when the distribution of the answer positions is highly skewed in the training set (e. g., answers lie only in the k-th sentence of each passage), QA models predicting answers as positions can learn spurious positional cues and fail to give answers in different positions.
