Specifically, the team focused on two main tasks: legal document retrieval and legal question answering (LQA). For the legal document retrieval task, the goal was to return articles related to a given question. An article is considered "relevant" if it contains information that helps answer the...
原文链接: 2311.06503 (arxiv.org)Knowledgeable Preference Alignment for LLMs in Domain-specific Question Answering 大语言模型在特定领域问答中的知识偏好对齐 AbstractDeploying large language models (L…
LLMs之Benchmark之TableBench:《TableBench: A Comprehensive and Complex Benchmark for Table Question Answering一个全面且复杂的表格问答基准测试》翻译与解读 导读:TableBench为评估和改进LLM在TableQA任务中的能力提供了宝贵的工具,为推动AI在现实世界表格数据分析中的发展做出了重大贡献。这个研究为表格问答提供了一...
Existing KG-based LLM reasoning methods often neglect the importance of KG's structural information for reasoning, facing challenges when dealing with complex structures and large amounts of irrelevant information, particularly in knowledge graph question answering (KGQA). To address this issue, this ...
To evaluate MEDIQ, we convert MEDQA and CRAFT-MD — medical benchmarks for diagnostic question answering — into an interactive setup. We develop a reliable Patient system and prototype several Expert systems, first showing that directly prompting state-of-the-art LLMs to ...
原文链接:Knowledge Graphs & LLMs: Multi-Hop Question Answering 回答一个问题往往需要把问题切割成多个子问题,所以本质上回答这类问题就是一个multi-hop question-answering task. 上面图中只是首先chunking文档然后embedding数据库中的文档之后plain vector similarity search用于multi-hop questions存在如下问题: (1)re...
Fine-tuning LLM for medical multiple-choice questions Full Fine-Tuning: Fine-tune BERT and ViLT for (visual) multiple choice questions using HuggingFace's Trainer() class. Parameter-Efficient Fine-Tuning (PEFT): With HuggingFace's PEFT library, fine-tune casual LLMs (GPT-like models) on medica...
A follow up question: Did she find them again? The solution can rewrite ("disambiguate") that question to provide all the context required to search for the relevant FAQ or passage: Did Little Bo Peep find her sheep again? Text generation for question answering ...
In this paper, we study the KG-augmented language model approach for solving the knowledge graph question answering (KGQA) task that requires rich world knowledge. Existing work has shown that retrieving KG knowledge to enhance LLMs prompting can significantly improve LLMs performance in KGQA. ...
Extensive experiments and valuable insights suggest that our proposed CDQA is challenging and worthy of more further study. We believe that the benchmark we provide will become one of the key data resources for improving LLMs' Chinese question-answering ability in the future. PDF Abstract ...