Scott B. Huffman
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CodeGemma: Open Code Models Based on Gemma
Heri Zhao
Joshua Howland
Nam Nguyen
Siqi Zuo
Andrea Hu
Christopher A. Choquette-Choo
Jingyue Shen
Joe Kelley
Mateo Wirth
Paul Michel
Peter Choy
Pratik Joshi
Sarmad Hashmi
Shubham Agrawal
Zhitao Gong
Jane Fine
Ale Hartman
Bin Ni
Kathy Korevec
Kelly Schaefer
(2024)
Preview abstract
This paper introduces CodeGemma, a family of specialized open code models built on top of Gemma, capable of a variety of code and natural language generation tasks. We release three model checkpoints. CodeGemma 7B pretrained (PT) and instruction-tuned (IT) variants have remarkably resilient natural language understanding, excel in mathematical reasoning, while matching code capabilities of other open models. CodeGemma 2B is a state-of-the-art code completion model designed for fast code infilling and open-ended generation in latency sensitive settings.
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Good Abandonment in Mobile and PC Internet Search
Jane Li
Akihito Tokuda
32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM (Association for Computing Machinery), 2 Penn Plaza, Suite 701, New York 10121-0701 (2009), pp. 43-50
Preview abstract
Query abandonment by search engine users is generally considered to be a negative signal. In this paper, we explore the concept of good abandonment. We define a good abandonment as an abandoned query for which the user's information need was successfully addressed by the search results page, with no need to click on a result or refine the query. We present an analysis of abandoned internet search queries across two modalities (PC and mobile) in three locales. The goal is to approximate the prevalence of good abandonment, and to identify types of information needs that may lead to good abandonment, across different locales and modalities. Our study has three key findings: First, queries potentially indicating good abandonment make up a significant portion of all abandoned queries. Second, the good abandonment rate from mobile search is significantly higher than that from PC search, across all locales tested. Third, classified by type of information need, the major classes of good abandonment vary dramatically by both locale and modality. Our findings imply that it is a mistake to uniformly consider query abandonment as a negative signal. Further, there is a potential opportunity for search engines to drive
additional good abandonment, especially for mobile search
users, by improving search features and result snippets.
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How evaluator domain expertise affects search result relevance
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Kenneth A. Kinney
Juting Zhai
Conference on Information and Knowledge Management (2008), pp. 591-598
Multiple-Signal Duplicate Detection for Search Evaluation
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Alexei Stolboushkin
Howard Wong-Toi
Fan Yang
Hein Roehrig
Proceedings of 30th Annual International ACM SIGIR Conference, ACM (2007), pp. 223-230
How well does result relevance predict session satisfaction?
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Michael Hochster
Proceedings of the 30th annual international ACM SIGIR, ACM, Amsterdam (2007), pp. 567-574
