33 citations · 166 across the 51 of their papers we have counts for
6 papers · 1 filter
Nice to Meet You: Synthesizing Practical MLIR Abstract Transformers
Xuanyu Peng, Dominic Kennedy, Yuyou Fan +3
Static analyses play a fundamental role during compilation: they discover facts that are true in all executions of the code being compiled, and then these facts are used to justify…
Bootstrapping Fuzzers for Compilers of Low-Resource Language Dialects Using Language Models
Sairam Vaidya, Marcel Böhme, Loris D'Antoni
Modern extensible compiler frameworks-such as MLIR-enable rapid creation of domain-specific language dialects. This flexibility, however, makes correctness harder to ensure as the…
Constrained Adaptive Rejection Sampling
Paweł Parys, Sairam Vaidya, Taylor Berg-Kirkpatrick +1
Language Models (LMs) are increasingly used in applications where generated outputs must satisfy strict semantic or syntactic constraints. Existing approaches to constrained genera…
ChopChop: a Programmable Framework for Semantically Constraining the Output of Language Models
Shaan Nagy, Timothy Zhou, Nadia Polikarpova +1
Language models (LMs) can generate code but cannot guarantee its correctness$\unicode{x2014}$often producing outputs that violate type safety, program invariants, or other semantic…
Constrained Sampling for Language Models Should Be Easy: An MCMC Perspective
Emmanuel Anaya Gonzalez, Sairam Vaidya, Kanghee Park +3
Constrained decoding enables Language Models (LMs) to produce samples that provably satisfy hard constraints. However, existing constrained-decoding approaches often distort the un…
Flexible and Efficient Grammar-Constrained Decoding
Kanghee Park, Timothy Zhou, Loris D'Antoni
Large Language Models (LLMs) are often asked to generate structured outputs that obey precise syntactic rules, such as code snippets or formatted data. Grammar-constrained decoding…