Zobrazeno 1 - 10
of 159
pro vyhledávání: '"Wang, Sida"'
Self-assembly of matter in solution generally relies on attractive interactions that overcome entropy and drive the formation of higher-order molecular and particulate structures. Such interactions play key roles in a variety of contexts, e.g., cryst
Externí odkaz:
http://arxiv.org/abs/2405.12099
Autor:
Jain, Naman, Han, King, Gu, Alex, Li, Wen-Ding, Yan, Fanjia, Zhang, Tianjun, Wang, Sida, Solar-Lezama, Armando, Sen, Koushik, Stoica, Ion
Large Language Models (LLMs) applied to code-related applications have emerged as a prominent field, attracting significant interest from both academia and industry. However, as new and improved LLMs are developed, existing evaluation benchmarks (e.g
Externí odkaz:
http://arxiv.org/abs/2403.07974
We introduce Syntax-Aware Fill-In-the-Middle (SAFIM), a new benchmark for evaluating Large Language Models (LLMs) on the code Fill-in-the-Middle (FIM) task. This benchmark focuses on syntax-aware completions of program structures such as code blocks
Externí odkaz:
http://arxiv.org/abs/2403.04814
Autor:
Gu, Alex, Rozière, Baptiste, Leather, Hugh, Solar-Lezama, Armando, Synnaeve, Gabriel, Wang, Sida I.
We present CRUXEval (Code Reasoning, Understanding, and eXecution Evaluation), a benchmark consisting of 800 Python functions (3-13 lines). Each function comes with an input-output pair, leading to two natural tasks: input prediction and output predi
Externí odkaz:
http://arxiv.org/abs/2401.03065
Autor:
Wang, Sida I.
The striking ability of unsupervised word translation has been demonstrated with the help of word vectors / pretraining; however, they require large amounts of data and usually fails if the data come from different domains. We propose coocmap, a meth
Externí odkaz:
http://arxiv.org/abs/2305.14200
Interactive semantic parsing based on natural language (NL) feedback, where users provide feedback to correct the parser mistakes, has emerged as a more practical scenario than the traditional one-shot semantic parsing. However, prior work has heavil
Externí odkaz:
http://arxiv.org/abs/2305.08195
Autor:
Ni, Ansong, Iyer, Srini, Radev, Dragomir, Stoyanov, Ves, Yih, Wen-tau, Wang, Sida I., Lin, Xi Victoria
The advent of large language models trained on code (code LLMs) has led to significant progress in language-to-code generation. State-of-the-art approaches in this area combine LLM decoding with sample pruning and reranking using test cases or heuris
Externí odkaz:
http://arxiv.org/abs/2302.08468
The interaction between charged objects in solution is generally expected to recapitulate two central principles of electromagnetics: (i) like-charged objects repel, and (ii) they do so regardless of the sign of their electrical charge. Here we demon
Externí odkaz:
http://arxiv.org/abs/2212.12894
Autor:
Zhang, Tianyi, Yu, Tao, Hashimoto, Tatsunori B., Lewis, Mike, Yih, Wen-tau, Fried, Daniel, Wang, Sida I.
Sampling diverse programs from a code language model and reranking with model likelihood is a popular method for code generation but it is prone to preferring degenerate solutions. Inspired by collaborative programming, we propose Coder-Reviewer rera
Externí odkaz:
http://arxiv.org/abs/2211.16490
Autor:
Lai, Yuhang, Li, Chengxi, Wang, Yiming, Zhang, Tianyi, Zhong, Ruiqi, Zettlemoyer, Luke, Yih, Scott Wen-tau, Fried, Daniel, Wang, Sida, Yu, Tao
We introduce DS-1000, a code generation benchmark with a thousand data science problems spanning seven Python libraries, such as NumPy and Pandas. Compared to prior works, DS-1000 incorporates three core features. First, our problems reflect diverse,
Externí odkaz:
http://arxiv.org/abs/2211.11501