Artificial Needles to Real Haystacks: Improving Retrieval Capabilities in LLMs
The study presents a method to boost Large Language Models' retrieval and reasoning abilities for long-context inputs by fine-tuning on a synthetic dataset. Results show significant improvements in information retrieval and reasoning skills.
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The paper discusses a method to enhance the retrieval and reasoning capabilities of Large Language Models (LLMs) when processing long-context inputs. The proposed approach involves fine-tuning LLMs on a synthetic dataset designed for numerical key-value retrieval tasks. Experiments conducted on models like GPT-3.5 Turbo and Mistral 7B show that fine-tuning on this dataset significantly improves the models' information retrieval and reasoning abilities in longer-context scenarios. The study demonstrates a transfer of skills from synthetic to real task evaluations, with notable improvements in performance metrics. Additionally, the research highlights that fine-tuning LLMs on synthetic data can prevent performance drops compared to using other baseline data, particularly in tasks like TriviaQA. Overall, the study emphasizes the potential of fine-tuning on synthetic data to enhance LLM performance on longer-context tasks without compromising general benchmark performance.
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5 comments Do a task using the list of dictionaries below.
Dictionary [1] {122: 765, 4548: 1475, 4818: 4782} Dictionary [2] {526: 290, 9205: 9318, 9278: 1565} ...
Dictionary [32] {2931: 8364, 196: 1464, 812: 5363} ...
Dictionary [85] {344: 1579, 116: 617, 330: 411}
Above is a list of dictionaries such that each key and value is an integer. Report the
value of key 2931 and the dictionary it is in.
Desired answer: The value of key 2931 is 8364 and it is in Dictionary [32].
Imo the best way to handle this is RAG + multi-shot prompting (+ symbolic mapping to an actual data structure). For example, a pre-processing step where you partition the context by "records," another step where you insert (and potentially split up the records) in a RAG database, and another step where you make fuzzy queries. So, if you ask for record 1234 you get an exact match on that line (or set of lines, or record, or whatever) of the original context. And if you ask for "elephant" but there's no "elephant" in the context, you might get the "hippo" record because of the RAG reranking.
This is a lot of work, and is essentially a data pipeline, but the results are much better-curated than just fine-tuning and hoping that generalized needle-in-a-haystack search will work reliably as part of a language model.
But that’s not the point, the point is a task that’s trivial to generate and exercises 10s-100s of thousands of tokens of context in a falsifiable way.
Even gpt4 gets tripped up when there's too many exact instructions needed to be executed on an input. That's why it's common that breaking a task into multiple steps and multiple improves performance.
It's wonderful to see improvements possible on smaller models.
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