Databricks is proud to be a platinum sponsor of NeurIPS 2024. The convention runs from December 10 to fifteen in Vancouver, British Columbia.
Go to our Sales space
Cease by sales space #591 within the Expo Corridor from December 10-12 to fulfill members of the staff and study our newest work.
Demo
Be part of us as we show how MLflow Tracing and the Mosaic AI Agent Framework present observability and automatic analysis as we iteratively enhance the factuality and accuracy of a GenAI utility with DSPy. MLflow’s Tracing function captures detailed details about LLM and agent inputs and outputs, permitting builders to simply determine the supply of bugs and surprising behaviors. Moreover, the Mosaic AI Agent Framework, a part of the Databricks Information Intelligence Platform, gives capabilities for enhancing the standard of GenAI purposes by means of human suggestions and automatic analysis.
Displays and accepted publications
Talks
The Desk Illustration Studying (TRL) workshop is the premier venue for analysis into tabular information as a modality for illustration studying and generative fashions. At this 12 months’s workshop, Matei Zaharia is the featured speaker for the session targeted on pure language interfaces to tables.
Workshop Accepted Papers
On this work, we examine the effectiveness of sparse upcycling towards continued pretraining (CPT) throughout totally different mannequin sizes, compute budgets, and pretraining durations. Our experiments present that sparse upcycling can obtain higher high quality, with enhancements of over 20% relative to CPT in sure eventualities. Nonetheless, this comes with a big inference value, resulting in 40% slowdowns in high-demand inference settings for bigger fashions. Our findings spotlight the trade-off between mannequin high quality and inference effectivity, providing insights for practitioners looking for to steadiness mannequin high quality and deployment constraints.
This paper presents a complete research of the influence of elevated context size on RAG efficiency throughout 20 well-liked open supply and business LLMs. We run RAG workflows whereas various the entire context size from 2,000 to 128,000 tokens (and a couple of million tokens when potential) on three domain-specific datasets, and report key insights on the advantages and limitations of lengthy context in RAG purposes. Our findings reveal that whereas retrieving extra paperwork can enhance efficiency, solely a handful of the latest state-of-the-art LLMs can keep constant accuracy at lengthy context above 64k tokens. We additionally determine distinct failure modes in lengthy context eventualities, suggesting areas for future analysis.
On this work, we discover using MixAttention, a mannequin structure modification that mixes sliding window consideration, the place solely a small subset of latest tokens is saved within the KV cache, with KV cache sharing throughout layers. Our experiments show that MixAttention considerably reduces reminiscence utilization and improves inference velocity with out sacrificing mannequin efficiency in each quick and long-context duties. We additionally discover varied configurations of this structure, figuring out those who keep high quality throughout analysis metrics whereas optimizing useful resource effectivity.
We introduce Critique-out-Loud (CLoud) RLHF reward fashions that motive explicitly in regards to the high quality of a response from an LLM assistant. CLoud reward fashions function by first producing a pure language critique of the assistant’s response that’s then used to foretell a scalar reward for the standard of the response. We show the success of CLoud reward fashions for each Llama-3-8B and 70B base fashions: in comparison with basic reward fashions, CLoud reward fashions enhance pairwise desire classification accuracy on RewardBench by 4.65 and 5.84 proportion factors for the 8B and 70B base fashions respectively. Moreover, CLoud reward fashions result in a Pareto enchancment for win price on ArenaHard when used because the scoring mannequin for Greatest-of-N. Lastly, we discover how you can exploit the dynamic inference compute capabilities of CLoud reward fashions by performing self-consistency decoding for reward prediction.
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