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TableBench: A Comprehensive and Complex Benchmark for Table Question Answering
Recent advancements in Large Language Models (LLMs) have markedly enhanced the interpretation and processing of tabular data, introducing previously unimaginable capabilities.
Raw Text is All you Need: Knowledge-intensive Multi-turn Instruction Tuning for Large Language Model
In this paper, we present a novel framework named R2S that leverages the CoD-Chain of Dialogue logic to guide large language models (LLMs) in generating knowledge-intensive multi-turn dialogues for instruction tuning.
Unleashing Potential of Evidence in Knowledge-Intensive Dialogue Generation
To fully Unleash the potential of evidence, we propose a framework to effectively incorporate Evidence in knowledge-Intensive Dialogue Generation (u-EIDG).
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