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Computer Science > Machine Learning

arXiv:2406.19292 (cs)
[Submitted on 27 Jun 2024]

Title:From Artificial Needles to Real Haystacks: Improving Retrieval
Capabilities in LLMs by Finetuning on Synthetic Data

Authors:Zheyang Xiong, Vasilis Papageorgiou, Kangwook Lee, Dimitris
Papailiopoulos
View a PDF of the paper titled From Artificial Needles to Real
Haystacks: Improving Retrieval Capabilities in LLMs by Finetuning on
Synthetic Data, by Zheyang Xiong and 3 other authors
View PDF

    Abstract:Recent studies have shown that Large Language Models
    (LLMs) struggle to accurately retrieve information and maintain
    reasoning capabilities when processing long-context inputs. To
    address these limitations, we propose a finetuning approach
    utilizing a carefully designed synthetic dataset comprising
    numerical key-value retrieval tasks. Our experiments on models
    like GPT-3.5 Turbo and Mistral 7B demonstrate that finetuning
    LLMs on this dataset significantly improves LLMs' information
    retrieval and reasoning capabilities in longer-context settings.
    We present an analysis of the finetuned models, illustrating the
    transfer of skills from synthetic to real task evaluations (e.g.,
    $10.5\%$ improvement on $20$ documents MDQA at position $10$ for
    GPT-3.5 Turbo). We also find that finetuned LLMs' performance on
    general benchmarks remains almost constant while LLMs finetuned
    on other baseline long-context augmentation data can encourage
    hallucination (e.g., on TriviaQA, Mistral 7B finetuned on our
    synthetic data cause no performance drop while other baseline
    data can cause a drop that ranges from $2.33\%$ to $6.19\%$). Our
    study highlights the potential of finetuning on synthetic data
    for improving the performance of LLMs on longer-context tasks.

Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI);
          Computation and Language (cs.CL)
Cite as:  arXiv:2406.19292 [cs.LG]
          (or arXiv:2406.19292v1 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2406.19292
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Zheyang Xiong [view email]
[v1] Thu, 27 Jun 2024 16:05:13 UTC (165 KB)
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    Haystacks: Improving Retrieval Capabilities in LLMs by Finetuning
    on Synthetic Data, by Zheyang Xiong and 3 other authors
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