Besides, we introduce contrastive learning method and in-sample attention mechanism within each sample to better distinguish the answers from the distractors. Then we successively fine-tune a multiple-choice model on RACE and QuALITY. Model description: We extract a short context from the original passage by DPR (using questions and options). The chosen nodes are then combined with the query and used as context, which is subsequently passed to the LLM to generate the final answers.ĬoLISA: DPR & DeBERTaV3-large architecture plus contrastive learning & in-sample attention SUDA NLP & I2R at Soochow University This is accomplished by starting at the topmost level and performing a beam search until a specified number of layers is reached, with node selection predicated on the cosine similarity with the query vector. The outcome of this iterative process is a bottom-up hierarchical tree structure, wherein each node signifies a cluster of related text chunks.ĭuring querying, the hierarchical tree structure allows for efficient traversal and retrieval of relevant information. This process is iteratively performed for a predetermined number of layers. The generated summary text is again subjected to clustering. ![]() Then, the formed clusters are summarized using a large language model (LLM). It then employs a novel variant of the Gaussian Mixture Model (GMM) for text clustering and clusters the text after dimensionality reduction on the embeddings using Uniform Manifold Approximation and Projection (UMAP). Model description: RAPTOR is a novel retrieval system that works by chunking the text and creating embedding vectors of the chunked texts with an embedding model. RAPTOR (Reading, Attending, and Processing Tree-Organized Retrieval): employs text-embedding-ada-002 for creating embeddings, gpt-3.5-turbo for summarizing clusters, and utilizes GPT-4 for generating answers Anonymous (temporary) Each question is annotated by 3 new validation annotators who had not previously annotated that passage, and whose labels do not contribute to the assignment of the gold label. Model description: We estimate human accuracy on QuALITY on a random sample of 20 passages (367 questions).
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