14 citations · 31 across the 30 of their papers we have counts for
9 papers · 1 filter
LibEvolutionEval: A Benchmark and Study for Version-Specific Code Generation
Sachit Kuhar, Wasi Uddin Ahmad, Zijian Wang +6
Recent advancements in code completion models have primarily focused on local file contexts. However, these studies do not fully capture the complexity of real-world software devel…
Approximately Aligned Decoding
Daniel Melcer, Sujan Gonugondla, Pramuditha Perera +7
It is common to reject undesired outputs of Large Language Models (LLMs); however, current methods to do so require an excessive amount of computation to re-sample after a rejectio…
Automated Evaluation of Retrieval-Augmented Language Models with Task-Specific Exam Generation
Gauthier Guinet, Behrooz Omidvar-Tehrani, Anoop Deoras +1
We propose a new method to measure the task-specific accuracy of Retrieval-Augmented Large Language Models (RAG). Evaluation is performed by scoring the RAG on an automatically-gen…
Collage: Light-Weight Low-Precision Strategy for LLM Training
Tao Yu, Gaurav Gupta, Karthick Gopalswamy +7
Large models training is plagued by the intense compute cost and limited hardware memory. A practical solution is low-precision representation but is troubled by loss in numerical…
Fewer Truncations Improve Language Modeling
Hantian Ding, Zijian Wang, Giovanni Paolini +4
In large language model training, input documents are typically concatenated together and then split into sequences of equal length to avoid padding tokens. Despite its efficiency,…
LeDex: Training LLMs to Better Self-Debug and Explain Code
Nan Jiang, Xiaopeng Li, Shiqi Wang +6
In the domain of code generation, self-debugging is crucial. It allows LLMs to refine their generated code based on execution feedback. This is particularly important because gener…
CodeFort: Robust Training for Code Generation Models
Yuhao Zhang, Shiqi Wang, Haifeng Qian +8
Code generation models are not robust to small perturbations, which often lead to incorrect generations and significantly degrade the performance of these models. Although improvin…
BASS: Batched Attention-optimized Speculative Sampling
Haifeng Qian, Sujan Kumar Gonugondla, Sungsoo Ha +6
Speculative decoding has emerged as a powerful method to improve latency and throughput in hosting large language models. However, most existing implementations focus on generating…
Logic-Scaffolding: Personalized Aspect-Instructed Recommendation Explanation Generation using LLMs
Behnam Rahdari, Hao Ding, Ziwei Fan +4
The unique capabilities of Large Language Models (LLMs), such as the natural language text generation ability, position them as strong candidates for providing explanation for reco…