most citedRaw Text is All you Need: Knowledge-intensive Multi-turn Instruction Tuning for Large Language Model

1 citations · 2 across the 3 of their papers we have counts for

collaborators

3 papers

cs.CL2024★ 1 cited

Raw Text is All you Need: Knowledge-intensive Multi-turn Instruction Tuning for Large Language Model

Xia Hou, Qifeng Li, Jian Yang +8

Instruction tuning as an effective technique aligns the outputs of large language models (LLMs) with human preference. But how to generate the seasonal multi-turn dialogues from ra…

cs.PL2024★ 1 cited

McEval: Massively Multilingual Code Evaluation

Linzheng Chai, Shukai Liu, Jian Yang +15

Code large language models (LLMs) have shown remarkable advances in code understanding, completion, and generation tasks. Programming benchmarks, comprised of a selection of code c…

cs.CL2023

Unleashing Potential of Evidence in Knowledge-Intensive Dialogue Generation

Xianjie Wu, Jian Yang, Tongliang Li +4

Incorporating external knowledge into dialogue generation (KIDG) is crucial for improving the correctness of response, where evidence fragments serve as knowledgeable snippets supp…