Zobrazeno 1 - 10
of 86
pro vyhledávání: '"PARNIN, CHRIS"'
Autor:
Nahar, Nadia, Kästner, Christian, Butler, Jenna, Parnin, Chris, Zimmermann, Thomas, Bird, Christian
Large Language Models (LLMs) are increasingly embedded into software products across diverse industries, enhancing user experiences, but at the same time introducing numerous challenges for developers. Unique characteristics of LLMs force developers,
Externí odkaz:
http://arxiv.org/abs/2410.12071
Autor:
Murugadoss, Bhuvanashree, Poelitz, Christian, Drosos, Ian, Le, Vu, McKenna, Nick, Negreanu, Carina Suzana, Parnin, Chris, Sarkar, Advait
LLMs-as-a-judge is a recently popularized method which replaces human judgements in task evaluation (Zheng et al. 2024) with automatic evaluation using LLMs. Due to widespread use of RLHF (Reinforcement Learning from Human Feedback), state-of-the-art
Externí odkaz:
http://arxiv.org/abs/2408.08781
Autor:
Singha, Ananya, Chopra, Bhavya, Khatry, Anirudh, Gulwani, Sumit, Henley, Austin Z., Le, Vu, Parnin, Chris, Singh, Mukul, Verbruggen, Gust
Automated insight generation is a common tactic for helping knowledge workers, such as data scientists, to quickly understand the potential value of new and unfamiliar data. Unfortunately, automated insights produced by large-language models can gene
Externí odkaz:
http://arxiv.org/abs/2405.01556
Exploring Interaction Patterns for Debugging: Enhancing Conversational Capabilities of AI-assistants
Autor:
Chopra, Bhavya, Bajpai, Yasharth, Biyani, Param, Soares, Gustavo, Radhakrishna, Arjun, Parnin, Chris, Gulwani, Sumit
The widespread availability of Large Language Models (LLMs) within Integrated Development Environments (IDEs) has led to their speedy adoption. Conversational interactions with LLMs enable programmers to obtain natural language explanations for vario
Externí odkaz:
http://arxiv.org/abs/2402.06229
Autor:
Parnin, Chris, Soares, Gustavo, Pandita, Rahul, Gulwani, Sumit, Rich, Jessica, Henley, Austin Z.
A race is underway to embed advanced AI capabilities into products. These product copilots enable users to ask questions in natural language and receive relevant responses that are specific to the user's context. In fact, virtually every large techno
Externí odkaz:
http://arxiv.org/abs/2312.14231
Autor:
Chopra, Bhavya, Singha, Ananya, Fariha, Anna, Gulwani, Sumit, Parnin, Chris, Tiwari, Ashish, Henley, Austin Z.
Large Language Models (LLMs) are being increasingly employed in data science for tasks like data preprocessing and analytics. However, data scientists encounter substantial obstacles when conversing with LLM-powered chatbots and acting on their sugge
Externí odkaz:
http://arxiv.org/abs/2310.16164
Large language models (LLMs) are increasingly applied for tabular tasks using in-context learning. The prompt representation for a table may play a role in the LLMs ability to process the table. Inspired by prior work, we generate a collection of sel
Externí odkaz:
http://arxiv.org/abs/2310.10358
Autor:
Peitek, Norman, Bergum, Annabelle, Rekrut, Maurice, Mucke, Jonas, Nadig, Matthias, Parnin, Chris, Siegmund, Janet, Apel, Sven
Background: Despite similar education and background, programmers can exhibit vast differences in efficacy. While research has identified some potential factors, such as programming experience and domain knowledge, the effect of these factors on prog
Externí odkaz:
http://arxiv.org/abs/2303.07071
Autor:
Rahman, Akond, Parnin, Chris
Despite being beneficial for managing computing infrastructure automatically, Puppet manifests are susceptible to security weaknesses, e.g., hard-coded secrets and use of weak cryptography algorithms. Adequate mitigation of security weaknesses in Pup
Externí odkaz:
http://arxiv.org/abs/2208.01242
Autor:
Horton, Eric, Parnin, Chris
Software developers frequently use the system shell to perform configuration management tasks. Unfortunately, the shell does not scale well to large systems, and configuration management tools like Ansible are more difficult to learn. We address this
Externí odkaz:
http://arxiv.org/abs/2203.12065