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ColBERT Retrieval and Ensemble Response Scoring for Language Model Question Answering

arXiv 2408.10808 ↗First posted 20 Aug 2024cs.CL cs.IRcited by 0

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Abstract (arXiv)

Domain-specific question answering remains challenging for language models, given the deep technical knowledge required to answer questions correctly. This difficulty is amplified for smaller language models that cannot encode as much information in their parameters as larger models. The "Specializing Large Language Models for Telecom Networks" challenge aimed to enhance the performance of two small language models, Phi-2 and Falcon-7B in telecommunication question answering. In this paper, we present our question answering systems for this challenge. Our solutions achieved leading marks of 81.9% accuracy for Phi-2 and 57.3% for Falcon-7B. We have publicly released our code and fine-tuned models.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 27 references that are in the map. First posted on arXiv 20 Aug 2024.

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References · 18

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6 more
  1. 2021ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interactioncited by 464
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  3. 2023TeleQnA: A Benchmark Dataset to Assess Large Language Models Telecommunications Knowledgecited by 62
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  5. 2024Telco-RAG: Navigating the Challenges of Retrieval-Augmented Language Models for Telecommunicationscited by 26
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