May the source be with you.

By S. Mathieson. 2003

Subfields: Information Retrieval & Search

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In Information Retrieval & Search

Citations in the corpus

Choose Your Weapon: Survival Strategies for Depressed AI Academics (2023, background): “The seeds of collaborative open-source projects are already planted; think of StarCoder, the recent large model built by an open-science community involving both universities and industrial partners [11].”
FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision (2024, unknown): “…to longer context will unlock new capabilities (modeling and reasoning over multiple long documents [24, 43, 49] and ﬁles in large codebases [30, 47]), new modalities (high-resolution images [10], audio [23], video [25]), and new applications (user interaction with long history [52], agent…”
LiveBench: A Challenging, Contamination-Free LLM Benchmark (2024, background): “The coding ability of LLMs is one of the most widely studied and sought-after skills for LLMs [28, 34, 41].”
CodeMind: A Framework to Challenge Large Language Models for Code Reasoning (2024, unknown): “…Llama 2 (13B) (Touvron et al., 2023), Mistral (Jiang et al., 2023), CodeLlama (13B, instruction-tuned)(Roziere et al., 2023), StarCoder (15.5B)(Li et al., 2023), Wizard-Coder (15B, instruction-tuned)(Xu et al., 2023), Magic-Coder (7B)(Wei et al., 2023) (instruction-tuned), DeepSeek-Coder…”
NoFunEval: Funny How Code LMs Falter on Requirements Beyond Functional Correctness (2024, background): “…of newer and more capable language models (LMs) of code, e.g., (Chen et al., 2021; Wang & Komatsuzaki, 2021; Austin et al., 2021; Black et al., 2022; Tunstall et al., 2022; Fried et al., 2022; Nijkamp et al., 2023; Li et al., 2023b; Wang et al., 2023; OpenAI, 2023; Luo et al., 2023;…”
PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models (2024, unknown): “Fine-tuning large language models (LLMs) is a highly effective technique for boosting their capabilities in various tasks [1, 2, 3, 4], ensuring models to follow instructions [5, 6, 7], and instilling models with desirable behaviors while eliminating undesirable ones [8, 9].”
Rethinking Benchmark and Contamination for Language Models with Rephrased Samples (2023, methodology): “A widely used code pretraining set, StarCoder Data (Li et al., 2023), shows that hundreds of test cases in the Stack (Kocetkov et al., 2022) are contaminated with benchmarks.”
DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence (2024, unknown): “These models are available in four sizes: 7B, 13B, 34B, and 70B. • StarCoder (Lozhkov et al., 2024) is a publicly accessible model with 15 billion parameters.”
WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct (2023, unknown): “Notably, Meta’s series of Llama (Touvron et al., 2023a;b; Dubey et al., 2024) have sparked an open-source revolution and quickly narrowed the gap with those closed-source LLMs.”
VideoPoet: A Large Language Model for Zero-Shot Video Generation (2023, background): “While Large Language Models (LLMs) are commonly used as foundational models across various modalities including language [3, 8], code [38, 43], audio [51], speech [1], and robotics [20, 86], the diffusion model remains the predominant approach for video generation.”
BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions (2024, methodology): “More recently, pre-trained base code models [41, 34, 36, 52] have been built to perform accurate code completion.”
Cognitive Architectures for Language Agents (2023, methodology): “…these models on internet-scale text also made them useful for many tasks beyond generating text, such as writing code (Li et al., 2022b; Rozière et al., 2023; Li et al., 2023b), modeling proteins (Meier et al., 2021), and acting in interactive environments (Yao et al., 2022b; Nakano et al., 2021).”