208 citations · 407 across the 28 of their papers we have counts for
6 papers · 1 filter
Learning Code Preference via Synthetic Evolution
Jiawei Liu, Thanh Nguyen, Mingyue Shang +5
Large Language Models (LLMs) have recently demonstrated remarkable coding capabilities. However, assessing code generation based on well-formed properties and aligning it with deve…
Planning-Aware Code Infilling via Horizon-Length Prediction
Yifeng Ding, Hantian Ding, Shiqi Wang +3
Fill-in-the-Middle (FIM), or infilling, has become integral to code language models, enabling generation of missing code given both left and right contexts. However, the current FI…
On Mitigating Code LLM Hallucinations with API Documentation
Nihal Jain, Robert Kwiatkowski, Baishakhi Ray +2
In this study, we address the issue of API hallucinations in various software engineering contexts. We introduce CloudAPIBench, a new benchmark designed to measure API hallucinatio…
Reasoning in Token Economies: Budget-Aware Evaluation of LLM Reasoning Strategies
Junlin Wang, Siddhartha Jain, Dejiao Zhang +3
A diverse array of reasoning strategies has been proposed to elicit the capabilities of large language models. However, in this paper, we point out that traditional evaluations whi…
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…